Directed information flow during laparoscopic surgical skill acquisition dissociated skill level and medical simulation technology

doi:10.21203/rs.3.rs-917736/v1

Download PDF

Article

Directed information flow during laparoscopic surgical skill acquisition dissociated skill level and medical simulation technology

https://doi.org/10.21203/rs.3.rs-917736/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 25 Aug, 2022

Read the published version in npj Science of Learning →

You are reading this latest preprint version

The comparison of the effects of physical and virtual reality (VR) simulators on the brain network during skill acquisition has not been well addressed. In this study, the brain network and skilled behavior relationship were evaluated using functional near-infrared spectroscopy (fNIRS) data from seven experienced right-handed surgeons and six right-handed medical students during the performance of a well-established Fundamentals of Laparoscopic Surgery (FLS) pattern of cutting tasks. Multiple regression path analysis found that the FLS performance score was statistically significantly related to the interregional directed functional connectivity from the right prefrontal cortex to the supplementary motor area with F(2, 114) = 9, p < 0.001, and R² = 0.136. The coefficient of variation (CoV) of the FLS performance score was statistically significantly related to the CoV of the interregionally directed functional connectivity from the right primary motor cortex to the left primary motor cortex and the left primary motor cortex to the left prefrontal cortex with F(2, 22) = 3.912, p = 0.035, and R² = 0.262. Additionally, a two-way multivariate analysis of variance (MANOVA) found a statistically significant effect of the simulator technology on the interregional directed functional connectivity from the right prefrontal cortex to the left primary motor cortex (F(1,15) = 6.002, p = 0.027; partial η2 = 0.286). This involvement of the right prefrontal cortex is potentially related to the uncertainty that underpins FLS task performance based on skill level and medical simulator technology.

Fundamentals of Laparoscopic Surgery

medical simulation technology

functional near-infrared spectroscopy

brain-behavior analysis

skill learning

Virtual reality (VR) technology is increasingly being used for motor skill training in medicine¹; however, the investigation of the brain-behavior relationship in VR compared to physical simulators is lacking. Specifically, there is a need to study directional information flow in the brain during interactions with the environment for motor skill acquisition. During motor skill acquisition, the goal of the interactions with the environment is postulated to build models (called internal models^2,3) that can take a desired trajectory and output the corresponding motor command (i.e., inverse models) or take an efferent copy of motor command and predict the future state of the body (i.e., forward models)⁴. Internal models can then be used for sensory processing, sensorimotor integration and motor control leading to expert performance. For example, certain actions are possible whereas others are not in an operative field in laparoscopic surgery, and the possibilities of goal-directed action in the environment⁵, called “affordances,” must be learned by novices for planning sensory-guided actions toward a goal^6,7. The purpose of the internal models is to inform action so that the central nervous system can internally simulate possibilities of goal-directed motor behavior in planning, control, and learning⁸. Here, motor learning involves making predictions from efferent copies of motor commands, performing the action, and then validating the predictions with corresponding sensory information⁹ from the environment called the “reafferent” given that the sensory information is generated by the action and the interactions with the environment. Such “reafferent” sensory information is crucial for action monitoring⁸ and action-specific perception that an action is the consequence of one’s own intention, which is commonly referred to as agency. Christensen et al.¹⁰ investigated the coupling between action and perception based on the effect of action execution on action perception. These researchers found that the integration of motor and multisensory information for action-specific perception depended on the cerebellum, which is thought to encode internal models¹¹. Specifically, an intact forward model can predict a future state from the efferent copy of the motor command, thereby coupling action execution with action perception. Moreover, patients with cerebellar damage showed no beneficial influence of action execution on action perception compared to healthy controls, thereby showing a lack of action perception coupling. Sensorimotor integration with the inverse model to determine the motor commands for a desired goal directed trajectory, then action execution and action perception using the forward model for fine control of voluntary movements¹¹ will require interaction of the cerebellum with the cerebrum, i.e., the sensorimotor cortico-cerebellar loops¹².

Motor exploration, a trial-and-error process due to a lack of adequate internal models in a novel environment, plays a critical role in motor learning¹³. During goal-directed movement, reaching the goal is the “reward”, and actions that lead to the goal are reinforced. For example, motor behavior for laparoscopic surgery training can be characterized as a coordinated spatiotemporal three-dimensional (3D) movement using two-dimensional (2D) camera feedback with the interaction between the body and the environment within a restricted surgical volume that represents a novel procedure for novices. Here, novices need to learn a complex bimanual motor task requiring high-precision hand-eye coordination, depth perception in the 2D view, and goal-directed tool control for optimal performance of laparoscopic surgery¹⁴. Although there can be more than one trajectory of goal-directed movement¹⁵, there are only a few "efficient" trajectories, and not everyone can achieve proficiency¹⁶. For example, novices may switch early from motor exploration to motor exploitation¹⁷, where they reinforce "inefficient" trajectories despite errors for goal-directed movement. Indeed, motor skill automaticity, i.e., a decrease in the need for effortful control over performance¹⁸, can be achieved despite residual error. Therefore, an adequate switch from motor exploration to motor exploitation¹⁷ during the motor learning process is important¹³, where explore-exploit decisions are increasingly being shown to be dependent on behavioral variability¹⁹. Computational modeling of the interplay between the cerebellum and basal ganglia in motor adaptation²⁰ predicts that the learner is less likely to adapt to perturbations when the expected movement variability is “low,” and an underestimated variability can lead to an overly strong reduction in the learning rate for small perturbations. Here, switching to nonerror-based basal ganglia learning mechanisms can also be due to the implicit cost of error correction for the brain²¹; however, acquiring expert performance will require deliberate practice²² with overestimated variability that tends to drive error-based cerebellar learning mechanisms²⁰ despite the cost.

In this brain-behavior study, we aimed to capture the neural correlates of behavioral variability in terms of neural variability²³. Development of the sensorimotor mapping during skill acquisition is usually under variability "for all sets or series of observations that are nonconstant and … nonstationary"²⁴, including variability in brain activation²⁵. The inferences about the state of the tool and the environment under noisy feedback will be made using a perceptual model that novices need to develop based on the prediction error during the trial-and-error process of motor exploration. Therefore, understanding the variability in the brain and behavior in the context of the perception-action cycle²⁶ can provide insights into individual exploration-exploitation trade-offs in human motor learning²⁷. Moreover, it is important for motor skill training in medicine¹ to monitor brain-behavior relationships to identify novices who can learn more efficiently during the basal ganglia-driven motor exploration stage²⁷. Mobile brain-behavior investigation is feasible due to recent developments in portable brain imaging technologies²⁸. For example, Nemani et al.²⁹ assessed bimanual motor skills using functional near-infrared spectroscopy (fNIRS) during laparoscopic surgery training. In this study, we investigated the brain-behavior relationship in the context of the perception-action cycle²⁶ based on the directional information flow in the novice brain compared to the expert brain.

Laparoscopic surgery training following the Fundamentals of Laparoscopic Surgery (FLS) is a common education and training module designed for medical residents, fellows, and physicians to provide them with a set of basic surgical skills necessary to conduct laparoscopic surgery successfully. The FLS training is a joint education program between the Society of American Gastrointestinal Endoscopic Surgeons and the American College of Surgeons to establish box trainers (physical simulators) in standard surgical training curricula³⁰. FLS certification in general surgery in the USA involves five psychomotor tasks with increasing task complexity: (i) pegboard transfers, (ii) pattern cutting, (iii) placement of a ligating loop, (iv) suturing with extracorporeal knot tying, and (v) suturing with intracorporal knot tying. This certification was introduced to systemize the training and evaluation of cognitive and psychomotor skills required to perform minimally invasive surgery. FLS is being used to measure and document these skills for medical practitioners, where the understanding of the brain-behavior relationship is crucial for informed training and assessment³¹, especially in the context of physical versus VR simulators during laparoscopic surgery training³². For example, surgeons rely on 2D visualization of the 3D surgical field at a reduced depth and tactile perception³³, where 3D vision has been shown to speed up laparoscopic training³⁴ potentially by reducing the perceptual load³⁵. Therefore, understanding the perception action coupling from brain-behavior analysis can be used to improve VR simulators to help novices learn more efficiently with exploration-exploitation trade-offs in human motor learning²⁷. For example, behavioral variability can be artificially modulated in VR simulators²⁰ to modulate the exploration-exploitation trade-off in human motor learning²⁷ that can be individualized based on portable brain imaging of error processing mechanisms³⁶. Additionally, VR-driven sensorimotor stimulation may have harmful aftereffects³⁷, impeding transfer to real-life conditions that can be probed with the brain-behavior relationship in the context of sensation weighting in the perception-action cycle³⁸, e.g., greater reliance on visual feedback due to the lack of realistic kinesthetic and tactile feedback in VR simulators. Therefore, the understanding of the perception-action cycle from brain-behavior analysis can aid in improving the design of VR simulators in medicine by providing adequate sensory information with kinesthetic and tactile feedback^1,39 in addition to realistic visual and auditory feedback.

The FLS task performance (see Fig. 1a) is graded based on the speed and accuracy related to psychomotor skills⁴⁰, where a speed–accuracy tradeoff during skill training can lead to automaticity with greater focus on speed despite residual error^18,41, i.e., an increased speed of action selection with “inefficient” movement trajectories. Here, the early motor learning phase recruits the cerebral structures involving the striatum (nuclear complex of the basal ganglia) and the cerebellum, and the interaction between these two structures is thought to be critical for learning new movement trajectories⁴². Importantly, the cerebellum seems to provide a substrate for error-based learning through the updating of a forward model, whereas the striatum underpins habit formation (related to automaticity)⁴². Therefore, we postulate that successful motor skill acquisition requires adequate updating of a forward model in the cerebellum⁴³ during basal ganglia-driven motor exploration that will be reflected in the directional information flow in the brain cortico-basal ganglia-cerebellar network⁴⁴. Then, the hierarchy of cognitive control during skill learning shows a rostrocaudal axis in the frontal lobe⁴⁵, where a shift from posterior to anterior is postulated to mediate the progressively abstract, higher-order control expected from experts. Numerous functional magnetic resonance imaging (fMRI) and fNIRS studies have been published on skill learning^{33,46,47,48,49,50,51,52,53}, including training under stress⁵⁴; however, these studies have not systematically investigated the directional cortical information flow^55,56, its variability during FLS skill acquisition in physical versus VR simulators, and its interaction with the skill level based on statistical path analysis^57,58.

fNIRS-based brain imaging has limited spatial and depth sensitivity⁵⁹. Published fNIRS studies on skill learning showed the involvement of the inferior parietal cortex, prefrontal cortex (PFC), occipital cortex, and sensorimotor areas, including the premotor and primary motor cortex (PMC), whereas fMRI studies showed additional activation of deeper brain structures, including the basal ganglia and cerebellum^{33,46,47,48,49,50,51,52,53}, and a large-scale brain network^60,61. In this study, we used fNIRS-based brain imaging given that fMRI is challenging for mobile brain-behavior studies of FLS skill acquisition. The directional information flow^55,56 can be elucidated based on dynamic functional brain connectivity⁶² that measures time-varying changes in cortical activation and their dynamic reconfiguration⁶³. Specifically, directed functional brain connectivity based on time-varying Granger causality analysis⁶⁴ can be used to elucidate directional information flow across brain regions in the context of motor exploration (novices) versus exploitation (experts) underlying perception-action coupling²⁷.

Our prior work⁶⁵ established the face and construct validity of the VR simulator used in this study. Additionally, our prior mobile brain-behavior study found wavelet coherence-based interhemispheric primary motor cortex connectivity and its coefficient of variation (CoV) to be different between physical and VR simulators in novices⁶⁶; however, the analysis of directed information flow⁶⁷ to elucidate the hierarchy of the related to the perception-action cycle^68,69 (Fig. 1b) was not performed. Therefore, we investigated the directed information flow⁶⁷ among the following brain regions: the right PFC (RPFC), left PFC (LPFC), supplementary motor area (SMA), right PMC (RPMC), and left PMC (LPMC) (see Fig. 1b). These regions were identified based on the sensitivity profile of our optode montage (Figure S1 in supplementary materials). Figure 1b shows the postulated perception-action link where our optode montage captured the dorsal stream for action starting from action selection in dorsolateral PFC (drives random exploration in novices²³) to action sequencing in the SMA to action performance in the PMC. Then, the efference copy information from the PMC is transmitted to the SMA and PFC, whereas the corollary discharge from the SMA is transmitted to the PFC. Here, we distinguished between efference copy versus collateral discharge based on whether the motor action was transmitted versus the motor plan for action perception⁷⁰ in the PFC⁷¹. Then, any conflict with the sensory reafference is monitored by the angular gyrus for a subjective sense of agency⁷² in the simulation environment. The ventral stream for the perception of the sensory feedback from the environment at the primary sensory cortex flows to the sensory association cortex and then to the posterior association cortex (e.g., supramarginal gyrus), leading to conscious perception in the ventrolateral PFC. Here, the PFC interacts through reciprocal and reentrant connections with different areas of the posterior association cortex⁷³, including the supramarginal gyrus, to integrate the information from multiple sensory inputs and motor actions⁷⁴ for action perception⁷⁰. Figure 1c shows the automated anatomical labeling (AAL)⁷⁵ of the brain regions with Montreal Neurological Institute (MNI) coordinates (see Table S1 in the supplementary materials) based on the sensitivity profile (Figure S1 in the supplementary materials).

Interregional directed functional brain connectivity

Repeated-measure two-way multivariate analysis of variance (two-way MANOVA) found a statistically significant effect of skill level (expert, novice) on interregional directed functional connectivity from the RPFC to SMA (F(1,15) = 6.045, p = 0.027; partial η2 = 0.287), LPMC to SMA (F(1,15) = 7.892, p = 0.013; partial η2 = 0.345) and SMA to LPFC (F(1,15) = 6.591, p = 0.021; partial η2 = 0.305). Additionally, two-way MANOVA found a statistically significant effect of the simulator technology (physical simulator, VR simulator) on the interregional directed functional connectivity from the RPFC to LPMC (F(1,15) = 6.002, p = 0.027; partial η2 = 0.286). Then, two-way MANOVA found a statistically significant effect of the interaction between the skill level and the simulator technology on the interregional directed functional connectivity from the LPMC to RPFC (F(1,15) = 8.523, p = 0.011; partial η2 = 0.362) and SMA to LPFC (F(1,15) = 6.824, p = 0.020; partial η2 = 0.313). The details of the between-subject effects are presented in Table S2 in the supplementary materials.

Figure 2 shows the mean response for each factor (shown with colored arrows) adjusted for other variables in the model, i.e., the plot of estimated marginal means of the significant interregional directed functional brain connectivity after controlling the false discovery rate of 0.05 with Benjamini–Hochberg adjustment. Figures 2(a), 2(b), and 2(c) show the plot of estimated marginal means of the interregional directed functional brain connectivity affected by the skill level (expert, novice), where efference copy information flows from LPMC to SMA and the attentional control from RPFC to SMA is higher in experts than novices across both simulators. Figure 2(d) shows the plot of estimated marginal means of the interregional directed functional brain connectivity affected by the simulator technology (physical simulator, VR simulator). Here, the higher interregional directed functional brain connectivity from the RPFC to LPMC in the VR simulator than in the physical simulator may be related to increased attentional processes⁷⁶ (or attentional control) for fine motor control by the LPMC of the right-handed subjects since the RPFC optodes were over the right middle frontal gyrus (see Table S1 in supplementary materials). However, the interregional directed functional brain connectivity from the RPFC to SMA trended toward being lower (Figure 2b) in the VR simulator compared with the physical simulator, which may underpin less attentional control of SMA. Additionally, the interregional directed functional connectivity from LPMC to SMA trended toward being higher (Figure 2a) in the VR simulator than in the physical simulator, which may underpin a more substantial efference copy to SMA for modulating motor tasks. Then, the interregional directed functional connectivity from the SMA to LPFC, which is considered the corollary discharge for action perception⁷⁰ in PFC⁷¹, trended toward lower in the VR simulator than the physical simulator for the novice and attained a similar level as that of the expert (see Figure 2c). Here, the VR simulator was novel for both the expert and the novice given that experts were experienced with the physical simulator and human surgery, so a similar interregional directed functional connectivity from the SMA to LPFC in the VR simulator was expected and found, as shown in Figure 2c. Moreover, the interregional directed functional connectivity from the LPMC to RPFC, which is considered the efference copy for action perception⁷⁰ in PFC⁷¹, decreased in the VR simulator compared to the physical simulator for novices and attained a similar level as that of the expert (see Figure 2e). This finding potentially suggests a similar level of internal forward model contributions for experts and novices in the VR simulator.

FLS task performance score and coefficient of variation (CoV) of the FLS task performance score

Repeated-measures two-way analysis of variance (ANOVA) found a statistically significant effect of skill level on the FLS score (F(1,20) = 12.786, p = 0.002; partial η2 = 0.390). Table S4 in the supplementary materials presents the tests of between-subject effects. Two-way ANOVA also found a statistically significant effect of skill level on the CoV of the FLS score (F(1,21) = 4.370, p = 0.049; partial η2 = 0.172). Table S5 in the supplementary materials presents the tests of between-subject effects. Figure 3a shows that the FLS score of the experts decreased in the VR simulator compared with the physical simulator given that experts were experienced only with the physical simulator and human surgery. Figure 3b shows that the CoV of the FLS score of experts increased in the VR simulator compared to the physical simulator.

Coefficient of variation (CoV) of interregional directed functional brain connectivity

Two-way MANOVA found a statistically significant effect of the interaction between the skill level and the simulator technology on the CoV of the interregional directed functional connectivity from LPMC to RPMC (F(1,21) = 8.561, p = 0.008; partial η2 = 0.290). Table S3 in the supplementary materials presents the tests of between-subject effects. Figure 3c shows that the CoV of the interhemispheric directed functional connectivity from LPMC to RPMC increased in the VR simulator compared with the physical simulator for novices but decreased for the experts. Here, the CoV of the FLS score of experts increased in the VR simulator compared with the physical simulator (Figure 3b), so a decreased interhemispheric inhibition from LPMC to RPMC may suggest an increased CoV in performance in experts and vice versa in novices.

Brain-behavior relationships

Although the directed functional brain connectivity from the RPFC to SMA, LPMC to SMA, and SMA to LPFC mediated the difference between experts and novices (see Figure 2), elucidation of the structure of the interaction with the medical simulator technology requires multiple regression and path analysis (SPSS Amos, IBM, USA). For example, the VR simulator was novel for both the expert and the novice given that experts were experienced with the physical simulator and human surgery. Therefore, an interaction between the skill level and the simulator technology was expected for the interregional directed functional connectivity from the SMA to LPFC, as shown in Figure 2c, where the directed functional connectivity was similar in the VR simulator for experts and novices. Then, the difference between physical and VR simulators was captured by the directed functional brain connectivity from RPFC to LPMC (see Figure 2d). Nevertheless, the link between the directed functional brain connectivity and the FLS performance score was missing, so multiple regression (with backward elimination) analysis was performed. The results demonstrate that the FLS score was statistically significantly related to the interregional directed functional connectivity from the RPFC to SMA with F(2, 114) = 9, p < 0.001, and R² = 0.136 (see Figure 4a). Here, a significant partial regression (R² = 0.136) of the dependent variable (FLS score) with the predictor (RPFC to SMA-directed functional connectivity) was found after controlling the false discovery rate of 0.05 with Benjamini–Hochberg adjustment. Table S6 in the supplementary materials presents the ANOVA results. Then, the regression weights from the path analysis (Figure S3 in supplementary materials) from the factors (expert vs. novice, physical vs. VR simulator) to the directed functional brain connectivity (RPFC to LPMC, RPFC to SMA, LPMC to RPFC, LPMC to SMA, SMA to LPFC) to the FLS performance (FLS score) are shown in Table 1 (grayed rows are significant after controlling for a false discovery rate of 0.05 with Benjamini–Hochberg adjustment).

When investigating brain-behavior relationships in terms of CoV, multiple regression (with backward elimination) analysis found that the CoV of the FLS score was statistically significantly related to the CoV of the interregional directed functional connectivity from RPMC to LPMC and LPMC to LPFC with F(2, 22) = 3.912, p = 0.035, and R² = 0.262 after controlling for a false discovery rate of 0.05 with Benjamini–Hochberg adjustment (see Figure 4b). Table S7 in the supplementary materials presents the ANOVA results. Then, the regression weights from the path analysis (Figure S4 in the supplementary materials) from the factors (expert vs. novice, physical vs. VR simulator) to the CoV of the directed functional brain connectivity (RPFC to LPMC, RPFC to SMA, LPMC to RPFC, LPMC to SMA, SMA to LPFC) to the CoV of the FLS performance (FLS CoV) are shown in Table 2 (grayed rows are significant after controlling for a false discovery rate of 0.05 with Benjamini–Hochberg adjustment). Here, the regression weight estimates for the CoV of the RPMC to LPMC was 0.21 and that for the CoV of the LPMC to LPFC was -0.30. Both values were statistically significant after controlling for a false discovery rate of 0.05 with Benjamini–Hochberg adjustment.

Our study established a brain and behavior relationship using path analysis with fNIRS-based directed functional brain connectivity data that showed the feasibility of a portable, low-cost brain-imaging tool to compare task-related cortical information flow in ambulant subjects. Here, validation of medical simulation technology for laparoscopic surgical training based on the brain and behavior relationship is crucial given that psychomotor skill learning or adjusting to changes in the environment, e.g., physical versus VR environment, requires adequate motor exploration, leading to more efficient subsequent learning¹³. In this study, we applied spectral Granger causality⁶⁴ to determine the directional information flow in the brain networks and its CoV in physical and VR simulators. We found that the directed functional brain connectivity (Figure S2 in supplementary materials) from the RPFC to SMA during FLS task performance mediated the difference between experts and novices and predicted the behavior (FLS score), as shown in Fig. 4a, b. Our results revealed the SMA as the key junction⁷⁷ for the information flow that differentiated the skill level (experts versus novices) (see Fig. 2). Specifically, the SMA region has been considered a key structure⁷⁷ for directed information flow from the LPFC, RPFC, LPMC, and RPMC brain regions during a bimanual sequence operations task^78,79,80,81, as shown in Fig. 1. SMA is a crucial region for interlimb coordination as well as eye-hand coordination^82,83,84,85 that is critical for perception-action coupling of the temporal organization and bimanual movement execution^78,79,80,81. Therefore, the top-down executive control of the SMA is expected to differ¹⁸ between experts and novices, where the PFC⁸⁶ is known to have higher relevance in novices in facilitating training-induced task performance²⁹. The directed functional brain connectivity from the RPFC to LPMC differentiated medical simulation technology (physical versus VR simulator) (see Fig. 2), which may be related to different uncertainty computations in physical versus VR simulators leading to the downstream choice reflected in motor cortex activity²³. Additionally, an interaction between medical simulation technology and skill level was captured by the directed functional brain connectivity from the LPMC to the RPFC and the SMA to the LPFC (see Fig. 2), and these findings can be related to the efference copy and collateral discharge, respectively. Here, directed functional brain connectivity from the SMA to the LPFC (see Fig. 2c, e) aligned with our prior work using wavelet coherence-based functional connectivity measures⁸⁷ that found undirected functional connectivity between the PFC and SMA to be lower for experts than novices in the physical simulators. Therefore, a directed functional connectivity approach⁶² to the fNIRS time series could capture the cascading directional processing of goal-directed action⁶⁹, as postulated based on the dorsal stream of action in Fig. 1b.

In this study, sliding-window Granger causality provided a tool for identifying directed functional interactions from the fNIRS time series data that did not assume a static functional brain network across the whole FLS task. Thus, this method could also capture the CoV across repeated trials of the FLS task. An interaction effect between the skill level and the simulator technology for the CoV was found for the directed functional brain connectivity from the LPMC to RPMC, as shown in Fig. 3. This finding aligned with our prior work using a wavelet coherence-based functional brain connectivity measure⁸⁸ that elucidated the brain-behavior relationship based on the CoV between the LPMC-RPMC magnitude-squared wavelet coherence metric and the FLS score; however, the directionality of the information flow was not investigated earlier. Then, path analysis for the brain-behavior relation showed that the CoV of the directed functional brain connectivity from the RPMC to LPMC and LPMC to LPFC were significant predictors for the CoV of the FLS score, as shown in Fig. 4c, d, and e. Here, our current study highlighted the importance of portable brain imaging to evaluate medical simulation technology. Specifically, Granger causality and a multiple regression approach identified the directed information flow related to efference copy and corollary discharge linked to predictive internal signaling⁸⁹ that mediated the interaction between skill level and medical simulation technology.

In this study, we found that portable brain imaging for brain-behavior modeling can evaluate medical simulation technology in terms of its interaction with the skill level within the context of the perception-action cycle²⁶. This difference was captured by the directed functional brain connectivity from the RPFC to LPMC (Fig. 2) during FLS task performance, which was higher in the VR simulator than in the physical simulator for both experts and novices. Here, the PFC is postulated to subserve cognitive control⁹⁰ and attentional processes⁷⁶ that can depend on the uncertainty²³ underlying FLS training with different medical simulation technologies. Then, the distinguishing directed information flow for skill level as a predictor of FLS performance was found to proceed from the RPFC to SMA (see Fig. 4a), which trended toward being lower in the VR simulator than in the physical simulator (see Fig. 2b). Jenkins et al.⁹¹ demonstrated that PFC activation is associated with the learning of new sequence tasks, whereas the lateral premotor cortex is more activated during new learning and the SMA is more activated during the performance of a prelearned sequence. Therefore, the descent of the information flow from the PFC to the premotor/motor cortex^92,93 is expected in a VR simulator that was novel for both the expert and the novice and affected the exploration strategy⁹⁴. Specifically, experts had prior knowledge of the FLS task and thus could have used directed exploration in the VR simulator, whereas novices could have depended on random exploration in the initial stages of the FLS task²³. This investigation of different exploration strategies will require a higher density fNIRS optode montage to segregate the dorsolateral, ventrolateral, and rostrolateral PFC²³ in our future work. The dorsolateral and ventrolateral PFC can be related to attention control, cognitive control, feature extraction, and the formation of first-order relationships^95,96,97,98 that are relevant during the initial stage of motor skill learning in novices. Specifically, the dorsolateral PFC of the dorsal stream is more involved in the visual guidance of action, whereas the ventrolateral PFC of the ventral stream is more involved in recognition and conscious perception⁹⁹. Then, the SMA and the PMC are crucial for the coordination of bimanual movement^100, where SMA is crucial for the complex spatiotemporal sequencing of movements^101,79 necessary in FLS tasks. Then, in the later stage of motor skill learning for proficiency¹⁶, rostrolateral PFC may drive directed exploration based on relative uncertainty²³ to improve the robustness of the internal models.

An interaction between the medical simulation technology (physical vs. VR simulator) and the skill level (experts vs. novices) was captured by the directed functional brain connectivity from the LPMC to the RPFC and the SMA to the LPFC (see Fig. 2f and 2e), and this interaction can be related to efference copy and corollary discharge information flow, respectively (see Fig. 1b). Here, the SMA contributes to the prediction of the sensory consequences of movement¹⁰², which is expected when an internal forward model is available (e.g., for experts in the physical simulator). Therefore, corollary discharge¹⁰³ from the SMA to the PFC is expected for experts who have experienced physical simulators and human surgery for the cognitive control of bimanual movement^101,79. However, the VR simulator was novel for both the experts and the novices, so the corollary discharge¹⁰³ from the SMA to the PFC was reduced from the physical to VR simulator in the experts and was comparable to the novices in the VR simulator (see Fig. 2e). Furthermore, the efference copy from the LPMC to the RPFC is postulated to be related to the functional coupling of the prefrontal and premotor/motor areas that are expected during cognitive manipulation¹⁰⁴ under uncertain conditions²³. Here, an increased cognitive manipulation¹⁰⁴ under higher uncertainty²³ for both the experts and the novices (both inexperienced in VR) is postulated in the VR simulator compared to the physical simulator⁷⁰, i.e., an increased information flow from the RPFC to the LPMC in the VR simulator (see Fig. 2d). Additionally, the efference copy from the LPMC to the RPFC was reduced from the physical simulator to the VR simulator in the experts due to a lack of an internal model such that the LPMC- to RPFC-directed functional connectivity in the experts was comparable to that in the novices in the VR simulator (see Fig. 2f).

Our prior work⁶⁵ established the face and construct validity of the VR simulator. Our prior results are consistent with the current study results, where only the skill level and not the simulator technology exhibited a significant effect on the FLS score and its CoV (see Fig. 3a and 3b). Specifically, the expert had a higher FLS score (Fig. 3a) and lower CoV (Fig. 3b) than the novice in the physical simulator; however, in the VR simulator, the expert without VR experience trended toward a similar level as the novice. Here, motor variability influencing task performance has been postulated to shape motor learning^105,106, and motor variability typically tends to decrease with practice¹⁰⁷, which tends to drive the trade-off between exploitation and exploration¹⁰⁸. Subjects are expected to learn to avoid the influence of motor variability on goal-directed task performance^105,106, as observed in the experts with reduced CoV in the task performance (FLS score) in the physical simulator than novices. Both experts and novices exhibited similar CoV in the novel VR simulator (Fig. 3b). The variability (CoV) in the task performance (FLS score) was significantly related to the variability (CoV) in the directed functional brain connectivity from the RPMC to LPMC and LPMC to LPFC, as shown in Fig. 4c, d, and e, which presented a neural correlate of performance variability¹⁰⁹. Here, an increase in the CoV of the RPMC to LPMC and a decrease in the CoV of LPMC to LPFC were related to an increase in the CoV of the FLS score. Additionally, an effect of the interaction between the skill level and the simulator technology was found on the CoV of the directed functional brain connectivity from the LPMC to RPMC, as shown in Fig. 3c. Here, the efference copy information from the bilateral motor cortices to the LPFC highlighted left-lateralized cognitive control of behavioral (FLS score) variability. We also found hemispheric lateralization in our right-handed subjects where the coupling between the LPMC and the RPFC (see Fig. 2d and 2f) can be related to the detection (efferent copy LPMC to RPFC) and response (cognitive control RPFC to LPMC) to unexpected environmental stimuli^110,111 in the VR environment. Additionally, uncertainty due to unexpected environmental stimuli¹¹⁰ is subserved by the right PFC, where relative uncertainty (e.g., in experts) is represented in the right rostrolateral PFC, whereas total uncertainty (e.g., in novices) is represented in the right dorsolateral PFC²³. In contrast, the involvement of LPFC (see Fig. 2) as the recipient of the corollary discharge information from SMA may be related to its role in analyzing external information during planning a goal hierarchy¹¹¹. Then, any conflict between the efferent information and the sensory reafferent information can lead to a loss of subjective sense of agency in the angular gyrus⁷² (see Fig. 1b). Future work on improving the design of the VR simulator needs to address the brain-behavior relationship by reducing the conflict between the efferent information and the sensory reafferent information to facilitate the sense of agency that has been associated with learnability¹¹² and eventually motor skill “automaticity”¹⁸. Specifically, brain-behavior monitoring can be used to drive the virtual environment in ‘real-time’ to calibrate according to the degree of adaptation of the user’s prediction models to create a subjective sense of agency in novices as they learn psychomotor tasks with increasing task complexity, i.e., an adaptive VR simulator.

The experimental results of this study are conducive to the exploration of transcranial electrical stimulation¹¹³ to facilitate learnability in medical simulators. For example, mobile brain-behavior analysis with fNIRS can capture the interaction between the angular gyrus (AG) and the middle frontal gyrus (MFG) that is underpinned by the dorsal superior longitudinal fascicle (SLF II)¹¹⁴, and the subjective sense of agency may be facilitated by neuroimaging-guided transcranial electrical stimulation¹¹³ of the AG-MFG interactions¹¹⁵. The dorsal branch of the superior longitudinal fasciculus, which is responsible for visuospatial integration and motor planning, is linked to lateralized hand preference and manual specialization¹¹⁶. Here, the right MFG has been proposed to be a site of convergence of the dorsal and ventral attention networks⁷⁶ for cognitive control that is relevant in the perception-action cycle. The ventral superior longitudinal fascicle (SLF III)¹¹⁴ is postulated to be more relevant in perception (see Fig. 1b) from the supramarginal gyrus (SMG), where the left MFG and left inferior frontal gyrus (IFG) are more involved in more perceptually demanding FLS tasks, e.g., FLS suturing with intracorporal knot tying¹¹⁷. Here, the ventral stream of perception can be facilitated by neuroimaging-guided transcranial electrical stimulation¹¹³ of SMG-IFG interactions¹¹⁵. Then, the coupling between the SMA and LPFC may be related to patterns of prelearned behavior performed in familiar environments¹¹⁰ in the case of experts in the physical simulator. Here, it is postulated that the interaction between the preSMA/SMA and the PFC/IFG is underpinned by the extended frontal aslant tract (exFAT)¹¹⁸ of the short frontal lobe connections¹¹⁹ that have a role in executive function/ability¹²⁰. The exFAT may be left-lateralized¹¹⁸, which aligns well with left-lateralized activation for more complex bimanual FLS tasks, e.g., FLS suturing with intracorporal knot tying¹¹⁷. Although the FLS pattern cutting task is also a bimanual task, we only investigated the first sliding window of 54 sec across five repeated trials of FLS tasks when the cutting was performed with the right hand for all right-handed subjects (the cutting direction and occasionally the hand switched at different timepoints after 54 sec due to the surgical field constraints; see the FLS pattern cutting video in the supplementary materials). Therefore, we aimed to capture the initial stage in FLS pattern cutting skill acquisition to investigate the action-perception link⁷⁰ when the perceptual model⁷ is being developed. Here, transcranial electrical stimulation¹¹³ may facilitate the development of internal models¹²¹ as well as efference copy and corollary discharge, which may facilitate predictive internal signaling⁸⁹.

Limitations of this study include the spatial resolution of fNIRS and the optimality of the parameter of the sliding window method for measuring dynamic functional connectivity ⁶². The smallest window greater than 50 sec was found by running stationarity tests on the fNIRS time series. Here, a tradeoff was made. On the one hand, the width must be long enough to provide good frequency resolution. On the other hand, the width must be short enough to satisfy the condition of stationarity. Therefore, instead of an ad hoc window size¹²², we searched for an optimal¹²³ sliding window pertinent to our data. Additionally, due to limitations of the spatial resolution of our fNIRS device, we investigated only five brain regions, including the LPFC, RPFC, LPMC, RPMC, and SMA. Here, the premotor and motor areas were combined in the PMC (see Table S1), and the fNIRS optode montage could not distinguish the SMA proper from the preSMA brain regions, which may be important to better assess the temporal structure¹²⁴ of the perception-action coupling link⁷⁰. Additionally, we did not investigate all of the subregions of the PFC, e.g., the ventrolateral PFC and inferior frontal gyrus (IFG), that may have essential functional interactions during FLS surgical skill acquisition¹¹³, where the feasibility of fNIRS’ temporal resolution needs to be demonstrated in the future to capture the fast interactions that are expected via shorter frontal lobe connections¹¹⁹.

Subjects and experimental design

The human study was approved by the Institutional Review Board of the Massachusetts General Hospital, University at Buffalo, and the Rensselaer Polytechnic Institute, USA. Convenience sampling recruited seven experienced right-handed surgeons (experts, 5th-year residents, and attending surgeons) and six right-handed medical students (novices, 1st- to 3rd-year residents) to participate in the study. The subject details are provided in Table 3. Only right-handed subjects were selected to avoid dominant hemisphere-related intersubject variability.

Written consent was obtained from each subject before starting the study. All of the subjects were instructed verbally with a standard set of instructions on how to complete the FLS pattern cutting task on the FLS-certified physical and the VR simulator¹²⁵. For the completion of the FLS pattern cutting task, the right-handed subjects were asked to grasp the gauze using the left grasper (for traction) and cut along (and within) the circular stamp with the right laparoscopic scissors (for cutting). The trial time started when the subject touched the gauge and ended when the circular cut piece was removed from the gauge frame, and the participants were asked to cut the marked piece of gauze as quickly and as accurately as possible. Data collection was performed with a block design of a rest and stimulus period (the pattern cutting task). Specifically, after a 1-min rest period (baseline data), the FLS pattern cutting task had to be completed or stopped within 5 min (task data). This was repeated five times (5 trials) for each participant in this repeated-measure study. The performance score for each trial was recorded based on the FLS metrics.

A 32-channel continuous-wave near-infrared spectrometer (CW6 system, TechEn Inc., USA) was used for optical brain imaging using infrared light at 690 nm and 830 nm. The optode montage consisted of eight long-distance and eight short-distance sources coupled to 16 detectors. Twenty-five long-distance (30-40 mm) channels and eight short-distance (~8 mm) channels measured brain activation and systemic physiological signals, respectively (brain regions listed in Table S1 in supplementary materials), that were assessed using the photon migration simulation in AtlasViewer software¹²⁶. Here, the photon migration forward matrix represents the sensitivity profile. We selected the average fNIRS signal of the left and right middle frontal gyrus for prefrontal cortex activation, i.e., LPFC and RPFC; the left and right precentral gyrus for premotor/motor cortex activation, i.e., LPMC and RPMC; and the bilateral supplementary motor area complex for supplementary motor area activation, i.e., SMA. Table S1 in the supplementary materials provides the Montreal Neurological Institute and Hospital (MNI) coordinates. The optical fibers were duly arranged in a cap so that they did not obstruct the free movement of the participant during the FLS task performance.

fNIRS data processing for the oxyhemoglobin time series

Motion artifact detection and correction were performed using Savitzky–Golay filtering¹²⁷ and bandpass filtering (0.01-0.1 Hz) in HOMER3 software (https://github.com/BUNPC/Homer3). Then, the modified Beer–Lambert law was used to convert the optical signals of the detectors into changes in the oxyhemoglobin (HbO₂) concentrations for partial path-length factors of 6.4 (690 nm) and 5.8 (830 nm). The short separation channels (interoptode distance of 8 mm) captured the systemic physiological signals originating from noncortical superficial regions. The averaged signal from the long separation channels (interoptode distance of 30-40 mm) measured the HbO₂ changes at each of the following brain regions: LPFC, RPFC, LPMC, RPMC, and SMA. Figure S5 in the supplementary materials shows an illustrative plot of the HbO₂ time series.

Granger causality analysis

Granger causality using custom code measured the directed functional connectivity that provided the strength and direction of cortical information flow⁶⁷ between a pair of brain regions from the LPFC, RPFC, LPMC, RPMC, and SMA¹²⁸. Granger causality is grounded upon the postulate that one “causally” connected region would leave a component of its signal on another region with some latency, i.e., an autoregressive model (Granger Causality Description provided in the supplementary materials). Here, short-time Fourier transformation (STFT) was employed for nonparametric spectral Granger causality to estimate sliding-window pairwise measures of Granger causality, thereby eliminating the need for explicit autoregressive modeling⁶⁷. The lowest frequency of 0.02 Hz was found from the fNIRS power spectral density (after 0.01-0.1 Hz bandpass filtering), so a nonoverlapping fixed window size of 54 sec (greater than 50 sec) was selected heuristically¹²⁹.

Directed functional brain network

Granger causality in the neurovascular frequency range of 0.01 Hz to 0.07 Hz⁸⁷ was used to obtain directed connectivity for each pair of regions (a total of 20 connections). Figure S1 in the supplementary materials shows an example of 20 interregional directed connections for an illustrative time window. The directed connectivity between each pair of brain regions was used to form the directed functional brain network at each time window. Here, a neurovascular frequency range of 0.01 Hz to 0.07 Hz⁸⁷ acted as a filter for systemic and physiological noise, such as heartbeat and respiration¹³⁰.

Statistical analysis

Individual data collected from seven experienced right-handed surgeons and six right-handed medical students during the performance of FLS pattern cutting tasks in a physical simulator and a VR stimulator were used to determine the Granger causal directed functional connectivity metric for each subject (seven experts, six novices) with each simulator technology (physical, VR). The Shapiro–Wilk test was used to test normality for each of the dependent variables (i.e., interregional directed functional connectivity metric). Table S8 in the supplementary materials shows the results from Shapiro–Wilk’s test of normality for the Granger causality measure of each pair of brain regions in experts and novices while performing FLS tasks in the physical and virtual simulators across five trials. Then, the directed functional connectivity (Granger causality) between each pair of brain regions for the first window (54 sec) of each trial was used to conduct a repeated-measure two-way MANOVA in SPSS version 27 (IBM, USA) to determine whether there was a significant difference in the interregional directed functional connectivity based on the skill level (expert, novice), simulator technology (physical simulator, VR simulator) and their interaction. We conducted two-way MANOVA in SPSS version 27 (IBM, USA) to determine whether a significant difference in the CoV of interregional directed functional connectivity was noted across trials based on skill level, simulator technology, and their interaction. We conducted a repeated-measure two-way ANOVA in SPSS version 27 (IBM, USA) to determine whether there was a significant difference in the FLS score based on the skill level, simulator technology, and their interaction. We also conducted a repeated-measure two-way ANOVA in SPSS version 27 (IBM, USA) to determine whether there was a significant difference in the CoV of the FLS score based on skill level, simulator technology, and their interaction. The Levene test was used to test the homogeneity of variance. All significance levels were set at alpha = 0.05. To determine how the dependent variables (i.e., interregional directed functional connectivity) differed for the independent variables, the skill level, simulator technology, and their interaction, i.e., the tests of between-subjects effects, alpha with multiple comparison correction (false discovery rate), and partial eta squared effect size were used. Here, partial eta squared effect size measures the proportion of the total variance in a dependent variable defined by an independent variable in which the effects of other independent variables and interactions are parsed out¹³¹. Then, correction for a false discovery rate of 0.05 with Benjamini–Hochberg adjustment was performed using the MATLAB code in the Supplementary Materials. Then, we conducted brain-behavior analysis via multiple regression (backward elimination with a probability of F for removal ≥ 0.1) in SPSS version 27 (IBM, USA) to assess the relationship of the interregional directed functional brain connectivity with the FLS score. Then, in SPSS Amos (IBM, USA), the path analysis was performed from the skill level (expert, novice) and simulator technology (physical simulator, VR simulator) to the dependent variables (interregional directed functional brain connectivity and FLS score). Multiplicity control for the path analysis was also based on a false discovery rate of 0.05 with Benjamini–Hochberg adjustment using the MATLAB code in the Supplementary Materials.

Data Availability

All processed data analyzed during this study are included in this published article (and its supplementary information files).

Code Availability

The code generated during the current study is publicly available at https://github.com/anilkamat/GC_FLS_PC.git.

Acknowledgments

The authors gratefully acknowledge the support of this work through the Medical Technology Enterprise Consortium (MTEC) award #W81XWH2090019 (2020-628) and the U.S. Army Futures Command, Combat Capabilities Development Command Soldier Center STTC cooperative research agreement #W912CG-21-2-0001.

Competing Interests

The authors have no relevant financial or competing interests or other potential conflicts of interest.

Author Contributions

Anil Kamat: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft

Basiel Makled: Funding acquisition, Project administration, Resources

Jack Norfleet: Funding acquisition, Project administration, Resources

Steven D. Schwaitzberg: Funding acquisition, Project administration, Resources

Xavier Intes: Funding acquisition, Resources, Supervision, Writing – review & editing

Suvranu De: Funding acquisition, Supervision, Project administration, Resources, Writing – review & editing

Anirban Dutta: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Riener, R. & Harders, M. Virtual Reality in Medicine. (Springer-Verlag, 2012). doi:10.1007/978-1-4471-4011-5.
Wolpert, D. M. & Miall, R. C. Forward Models for Physiological Motor Control. Neural Netw 9, 1265–1279 (1996).
Kawato, M. Internal models for motor control and trajectory planning. Curr Opin Neurobiol 9, 718–727 (1999).
Orban de Xivry, J.-J. & Ethier, V. Neural Correlates of Internal Models. J Neurosci 28, 7931–7932 (2008).
Gibson, J. J. The Ecological Approach to Visual Perception: Classic Edition. (Psychology Press, 2014).
Renner, R. S., Velichkovsky, B. M. & Helmert, J. R. The perception of egocentric distances in virtual environments - A review. ACM Comput. Surv. 46, 23:1-23:40 (2013).
Cioffi, D. Beyond attentional strategies: cognitive-perceptual model of somatic interpretation. Psychol Bull 109, 25–41 (1991).
Sommer, M. A. & Wurtz, R. H. Brain circuits for the internal monitoring of movements. Annu Rev Neurosci 31, 317–338 (2008).
Popa, L. S. & Ebner, T. J. Cerebellum, Predictions and Errors. Frontiers in Cellular Neuroscience 12, (2019).
Christensen, A. et al. An intact action-perception coupling depends on the integrity of the cerebellum. J Neurosci 34, 6707–6716 (2014).
Ito, M. Control of mental activities by internal models in the cerebellum. Nat Rev Neurosci 9, 304–313 (2008).
Proville, R. D. et al. Cerebellum involvement in cortical sensorimotor circuits for the control of voluntary movements. Nat Neurosci 17, 1233–1239 (2014).
Uehara, S., Mawase, F., Therrien, A. S., Cherry-Allen, K. M. & Celnik, P. Interactions between motor exploration and reinforcement learning. Journal of Neurophysiology 122, 797–808 (2019).
Hannah, T. C. et al. Neuromonitoring Correlates of Expertise Level in Surgical Performers: A Systematic Review. Front Hum Neurosci 16, 705238 (2022).
Vetter, P., Flash, T. & Wolpert, D. M. Planning Movements in a Simple Redundant Task. Current Biology 12, 488–491 (2002).
Grantcharov, T. P. & Funch-Jensen, P. Can everyone achieve proficiency with the laparoscopic technique? Learning curve patterns in technical skills acquisition. Am J Surg 197, 447–449 (2009).
Phillips, H. N., Howai, N. A., Stan, G.-B. V. & Faisal, A. A. The implied exploration-exploitation trade-off in human motor learning. BMC Neurosci 12, P98 (2011).
Poldrack, R. A. et al. The Neural Correlates of Motor Skill Automaticity. J Neurosci 25, 5356–5364 (2005).
Feng, S. F., Wang, S., Zarnescu, S. & Wilson, R. C. The dynamics of explore–exploit decisions reveal a signal-to-noise mechanism for random exploration. Sci Rep 11, 3077 (2021).
Todorov, D. I. et al. The interplay between cerebellum and basal ganglia in motor adaptation: A modeling study. PLOS ONE 14, e0214926 (2019).
Sedaghat-Nejad, E. & Shadmehr, R. The cost of correcting for error during sensorimotor adaptation. Proceedings of the National Academy of Sciences 118, e2101717118 (2021).
Ericsson, K. A. The Influence of Experience and Deliberate Practice on the Development of Superior Expert Performance. in The Cambridge Handbook of Expertise and Expert Performance (eds. Ericsson, K. A., Charness, N., Feltovich, P. J. & Hoffman, R. R.) 683–704 (Cambridge University Press, 2006). doi:10.1017/CBO9780511816796.038.
Tomov, M. S., Truong, V. Q., Hundia, R. A. & Gershman, S. J. Dissociable neural correlates of uncertainty underlie different exploration strategies. Nat Commun 11, 2371 (2020).
Levac, D. E., Huber, M. E. & Sternad, D. Learning and transfer of complex motor skills in virtual reality: a perspective review. Journal of NeuroEngineering and Rehabilitation 16, 121 (2019).
Haar, S., Donchin, O. & Dinstein, I. Individual Movement Variability Magnitudes Are Explained by Cortical Neural Variability. J. Neurosci. 37, 9076–9085 (2017).
Fuster, J. M. Chapter 8 - Prefrontal Cortex in Decision-Making: The Perception–Action Cycle. in Decision Neuroscience (eds. Dreher, J.-C. & Tremblay, L.) 95–105 (Academic Press, 2017). doi:10.1016/B978-0-12-805308-9.00008-7.
Little, D. Y. & Sommer, F. T. Learning and exploration in action-perception loops. Front Neural Circuits 7, 37 (2013).
Yücel, M. A., Selb, J. J., Huppert, T. J., Franceschini, M. A. & Boas, D. A. Functional Near Infrared Spectroscopy: Enabling Routine Functional Brain Imaging. Curr Opin Biomed Eng 4, 78–86 (2017).
Nemani, A. et al. Assessing bimanual motor skills with optical neuroimaging. Science Advances 4, eaat3807 (2018).
Birkmeyer, J. D. et al. Surgical Skill and Complication Rates after Bariatric Surgery. New England Journal of Medicine 369, 1434–1442 (2013).
Dehabadi, M., Fernando, B. & Berlingieri, P. The use of simulation in the acquisition of laparoscopic suturing skills. International Journal of Surgery 12, 258–268 (2014).
Bahrami, P. et al. Functional MRI-compatible laparoscopic surgery training simulator. Magnetic Resonance in Medicine 65, 873–881 (2011).
Roberts, K. E., Bell, R. L. & Duffy, A. J. Evolution of surgical skills training. World Journal of Gastroenterology 12, 3219–3224 (2006).
Kunert, W. et al. Learning curves, potential and speed in training of laparoscopic skills: a randomised comparative study in a box trainer. Surg Endosc 35, 3303–3312 (2021).
Marucci, M. et al. The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence. Sci Rep 11, 4831 (2021).
Seidler, R. D., Kwak, Y., Fling, B. W. & Bernard, J. A. Neurocognitive Mechanisms of Error-Based Motor Learning. Adv Exp Med Biol 782, 10.1007/978-1-4614-5465–6_3 (2013).
Wright, W. G. Using virtual reality to augment perception, enhance sensorimotor adaptation, and change our minds. Front. Syst. Neurosci. 8, (2014).
Voorhorst, F., Meijer, D., Overbeeke, C. & Smets, G. Depth perception in laparoscopy through perception-action coupling. Minimally Invasive Therapy & Allied Technologies 7, 325–334 (1998).
Sigrist, R., Rauter, G., Riener, R. & Wolf, P. Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review. Psychon Bull Rev 20, 21–53 (2013).
Ritter, E. M. & Scott, D. J. Design of a proficiency-based skills training curriculum for the fundamentals of laparoscopic surgery. Surg Innov 14, 107–112 (2007).
Toner, J., Montero, B. G. & Moran, A. The Perils of Automaticity. Review of General Psychology 19, 431–442 (2015).
Welniarz, Q., Worbe, Y. & Gallea, C. The Forward Model: A Unifying Theory for the Role of the Cerebellum in Motor Control and Sense of Agency. Frontiers in Systems Neuroscience 15, (2021).
Wolpert, D. M., Miall, R. C. & Kawato, M. Internal models in the cerebellum. Trends Cogn. Sci. (Regul. Ed.) 2, 338–347 (1998).
Cataldi, S., Stanley, A. T., Miniaci, M. C. & Sulzer, D. Interpreting the role of the striatum during multiple phases of motor learning. The FEBS Journal 289, 2263–2281 (2022).
Badre, D. & D’Esposito, M. Is the rostro-caudal axis of the frontal lobe hierarchical? Nature Reviews Neuroscience vol. 10 659–669 (2009).
Ohuchida, K. et al. The frontal cortex is activated during learning of endoscopic procedures. Surgical Endoscopy (2009) doi:10.1007/s00464-008-0316-z.
Leff, D. R., Orihuela-Espina, F., Leong, J., Darzi, A. & Yang, G.-Z. Modelling dynamic fronto-parietal behaviour during minimally invasive surgery--a Markovian trip distribution approach. Med Image Comput Comput Assist Interv 11, 595–602 (2008).
Wanzel, K. R. et al. Visual-spatial ability and fMRI cortical activation in surgery residents. Am J Surg 193, 507–510 (2007).
Leff, D. R., Orihuela-Espina, F., Atallah, L., Darzi, A. & Yang, G.-Z. Functional near infrared spectroscopy in novice and expert surgeons--a manifold embedding approach. Med Image Comput Comput Assist Interv 10, 270–277 (2007).
Gao, Y. et al. Decreasing the Surgical Errors by Neurostimulation of Primary Motor Cortex and the Associated Brain Activation via Neuroimaging. Front Neurosci 15, 651192 (2021).
Leff, D. R. et al. Functional prefrontal reorganization accompanies learning-associated refinements in surgery: a manifold embedding approach. Comput Aided Surg 13, 325–339 (2008).
Khoe, H. C. H. et al. Use of prefrontal cortex activity as a measure of learning curve in surgical novices: results of a single blind randomised controlled trial. Surg Endosc 34, 5604–5615 (2020).
Gao, Y. et al. Functional Brain Imaging Reliably Predicts Bimanual Motor Skill Performance in a Standardized Surgical Task. IEEE Trans Biomed Eng 68, 2058–2066 (2021).
Shi, Y., Zhu, Y., Mehta, R. K. & Du, J. A neurophysiological approach to assess training outcome under stress: A virtual reality experiment of industrial shutdown maintenance using Functional Near-Infrared Spectroscopy (fNIRS). Advanced Engineering Informatics 46, 101153 (2020).
Kaminski, M. J. & Blinowska, K. J. A new method of the description of the information flow in the brain structures. Biol. Cybern. 65, 203–210 (1991).
Kamiński, M., Ding, M., Truccolo, W. A. & Bressler, S. L. Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance. Biol Cybern 85, 145–157 (2001).
Tak, S. & Ye, J. C. Statistical analysis of fNIRS data: a comprehensive review. Neuroimage 85 Pt 1, 72–91 (2014).
Streiner, D. L. Finding Our Way: An Introduction to Path Analysis. Can J Psychiatry 50, 115–122 (2005).
Strangman, G. E., Li, Z. & Zhang, Q. Depth Sensitivity and Source-Detector Separations for Near Infrared Spectroscopy Based on the Colin27 Brain Template. PLoS One 8, e66319 (2013).
Heitger, M. H. et al. Motor learning-induced changes in functional brain connectivity as revealed by means of graph-theoretical network analysis. NeuroImage 61, 633–650 (2012).
Gerraty, R. T., Davidow, J. Y., Wimmer, G. E., Kahn, I. & Shohamy, D. Transfer of learning relates to intrinsic connectivity between hippocampus, ventromedial prefrontal cortex, and large-scale networks. J Neurosci 34, 11297–11303 (2014).
Hutchison, R. M. et al. Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage 80, 10.1016/j.neuroimage.2013.05.079 (2013).
Kao, C.-H. et al. Functional brain network reconfiguration during learning in a dynamic environment. Nat Commun 11, 1682 (2020).
Seth, A. K., Barrett, A. B. & Barnett, L. Granger causality analysis in neuroscience and neuroimaging. Journal of Neuroscience 35, 3293–3297 (2015).
Prasad, R., Muniyandi, M., Manoharan, G. & Chandramohan, Servarayan. M. Face and Construct Validity of a Novel Virtual Reality–Based Bimanual Laparoscopic Force-Skills Trainer With Haptics Feedback. Surg Innov 25, 499–514 (2018).
Dutta, A. et al. Interhemispheric functional connectivity in the primary motor cortex distinguishes between training on a physical and a virtual surgical simulator. bioRxiv 2021.07.10.451831 (2021) doi:10.1101/2021.07.10.451831.
Dhamala, M., Rangarajan, G. & Ding, M. Analyzing information flow in brain networks with nonparametric Granger causality. NeuroImage 41, 354–362 (2008).
Willingham, D. B. A Neuropsychological Theory of Motor Skill Learning. Psychological Review 105, 558–584 (1998).
Fuster, J. M. Upper processing stages of the perception–action cycle. Trends in Cognitive Sciences 8, 143–145 (2004).
Latash, M. L. Efference copy in kinesthetic perception: a copy of what is it? Journal of Neurophysiology 125, 1079–1094 (2021).
Raos, V. & Savaki, H. E. The Role of the Prefrontal Cortex in Action Perception. Cerebral Cortex 27, 4677–4690 (2017).
Haggard, P. Sense of agency in the human brain. Nat Rev Neurosci 18, 196–207 (2017).
Fuster, J. M. The Prefrontal Cortex—An Update: Time Is of the Essence. Neuron 30, 319–333 (2001).
Lebedev, M. A., Douglass, D. K., Moody, S. L. & Wise, S. P. Prefrontal cortex neurons reflecting reports of a visual illusion. J Neurophysiol 85, 1395–1411 (2001).
Rolls, E. T., Huang, C.-C., Lin, C.-P., Feng, J. & Joliot, M. Automated anatomical labelling atlas 3. NeuroImage 206, 116189 (2020).
Japee, S., Holiday, K., Satyshur, M. D., Mukai, I. & Ungerleider, L. G. A role of right middle frontal gyrus in reorienting of attention: a case study. Frontiers in Systems Neuroscience 9, 23 (2015).
Cona, G. & Semenza, C. Supplementary motor area as key structure for domain-general sequence processing: A unified account. Neurosci Biobehav Rev 72, 28–42 (2017).
Sadato, N., Yonekura, Y., Waki, A., Yamada, H. & Ishii, Y. Role of the Supplementary Motor Area and the Right Premotor Cortex in the Coordination of Bimanual Finger Movements. J Neurosci 17, 9667–9674 (1997).
Swinnen, S. P. & Wenderoth, N. Two hands, one brain: cognitive neuroscience of bimanual skill. Trends Cogn Sci 8, 18–25 (2004).
Toyokura, M., Muro, I., Komiya, T. & Obara, M. Relation of bimanual coordination to activation in the sensorimotor cortex and supplementary motor area: analysis using functional magnetic resonance imaging. Brain Res Bull 48, 211–217 (1999).
Ullén, F., Forssberg, H. & Ehrsson, H. H. Neural networks for the coordination of the hands in time. J Neurophysiol 89, 1126–1135 (2003).
Gerloff, C., Corwell, B., Chen, R., Hallett, M. & Cohen, L. G. Stimulation over the human supplementary motor area interferes with the organization of future elements in complex motor sequences. Brain 120 ( Pt 9), 1587–1602 (1997).
Mushiake, H., Fujii, N. & Tanji, J. Visually guided saccade versus eye-hand reach: contrasting neuronal activity in the cortical supplementary and frontal eye fields. J Neurophysiol 75, 2187–2191 (1996).
Pierrot-Deseilligny, C., Israël, I., Berthoz, A., Rivaud, S. & Gaymard, B. Role of the different frontal lobe areas in the control of the horizontal component of memory-guided saccades in man. Exp Brain Res 95, 166–171 (1993).
Steyvers, M. et al. High-frequency transcranial magnetic stimulation of the supplementary motor area reduces bimanual coupling during anti-phase but not in-phase movements. Exp Brain Res 151, 309–317 (2003).
Euston, D. R., Gruber, A. J. & McNaughton, B. L. The Role of Medial Prefrontal Cortex in Memory and Decision Making. Neuron 76, 1057–1070 (2012).
Nemani, A. et al. Functional brain connectivity related to surgical skill dexterity in physical and virtual simulation environments. Neurophotonics 8, (2021).
Dutta, A. et al. Interhemispheric Functional Connectivity in the Primary Motor Cortex Distinguishes Between Training on a Physical and a Virtual Surgical Simulator. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (eds. de Bruijne, M. et al.) 636–644 (Springer International Publishing, 2021). doi:10.1007/978-3-030-87202-1_61.
Straka, H., Simmers, J. & Chagnaud, B. P. A New Perspective on Predictive Motor Signaling. Curr Biol 28, R232–R243 (2018).
Koechlin, E., Ody, C. & Kouneiher, F. The architecture of cognitive control in the human prefrontal cortex. Science 302, 1181–1185 (2003).
Jenkins, I. H., Brooks, D. J., Nixon, P. D., Frackowiak, R. S. J. & Passingham, R. E. Motor sequence learning: A study with positron emission tomography. Journal of Neuroscience 14, 3775–3790 (1994).
Bates, J. F. & Goldman-Rakic, P. S. Prefrontal connections of medial motor areas in the rhesus monkey. J Comp Neurol 336, 211–228 (1993).
Morecraft, R. J. & Van Hoesen, G. W. Frontal granular cortex input to the cingulate (M3), supplementary (M2) and primary (M1) motor cortices in the rhesus monkey. J Comp Neurol 337, 669–689 (1993).
Wilson, R. C., Bonawitz, E., Costa, V. D. & Ebitz, R. B. Balancing exploration and exploitation with information and randomization. Current Opinion in Behavioral Sciences 38, 49–56 (2021).
Badre, D. & D’Esposito, M. Is the rostro-caudal axis of the frontal lobe hierarchical? Nature Reviews Neuroscience 10, 659–669 (2009).
Badre, D. Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes. Trends in Cognitive Sciences 12, 193–200 (2008).
Koechlin, E. & Summerfield, C. An information theoretical approach to prefrontal executive function. Trends Cogn Sci 11, 229–235 (2007).
Christoff, K. & Gabrieli, J. D. E. The frontopolar cortex and human cognition: Evidence for a rostrocaudal hierarchical organization within the human prefrontal cortex. Psychobiology 28, 168–186 (2000).
Milner, A. D. How do the two visual streams interact with each other? Exp Brain Res 235, 1297–1308 (2017).
Tanji, J., Okano, K. & Sato, K. C. Neuronal activity in cortical motor areas related to ipsilateral, contralateral, and bilateral digit movements of the monkey. J Neurophysiol 60, 325–343 (1988).
Debaere, F., Wenderoth, N., Sunaert, S., Van Hecke, P. & Swinnen, S. P. Cerebellar and premotor function in bimanual coordination: parametric neural responses to spatiotemporal complexity and cycling frequency. Neuroimage 21, 1416–1427 (2004).
Makoshi, Z., Kroliczak, G. & van Donkelaar, P. Human supplementary motor area contribution to predictive motor planning. J Mot Behav 43, 303–309 (2011).
McCloskey, D. I. Corollary Discharges: Motor Commands and Perception. in Comprehensive Physiology 1415–1447 (American Cancer Society, 2011). doi:10.1002/cphy.cp010232.
Abe, M. et al. Functional Coupling of Human Prefrontal and Premotor Areas during Cognitive Manipulation. J. Neurosci. 27, 3429–3438 (2007).
Thorp, E. B., Kording, K. P. & Mussa-Ivaldi, F. A. Using noise to shape motor learning. Journal of Neurophysiology 117, 728–737 (2017).
Wu, H. G., Miyamoto, Y. R., Castro, L. N. G., Ölveczky, B. P. & Smith, M. A. Temporal structure of motor variability is dynamically regulated and predicts motor learning ability. Nat Neurosci 17, 312–321 (2014).
Ranganathan, R. & Newell, K. M. Emergent flexibility in motor learning. Exp Brain Res 202, 755–764 (2010).
Cohen, J. D., McClure, S. M. & Yu, A. J. Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philos Trans R Soc Lond B Biol Sci 362, 933–942 (2007).
Albouy, G. et al. Neural correlates of performance variability during motor sequence acquisition. Neuroimage 60, 324–331 (2012).
MacNeilage, P. F., Rogers, L. J. & Vallortigara, G. ORIGINS OF THE Left & Right Brain. Scientific American 301, 60–67 (2009).
Kaller, C. P., Rahm, B., Spreer, J., Weiller, C. & Unterrainer, J. M. Dissociable Contributions of Left and Right Dorsolateral Prefrontal Cortex in Planning. Cerebral Cortex 21, 307–317 (2011).
Wen, W. et al. Perception and control: individual difference in the sense of agency is associated with learnability in sensorimotor adaptation. Sci Rep 11, 20542 (2021).
Walia, P., Kumar, K. N. & Dutta, A. Neuroimaging Guided Transcranial Electrical Stimulation in Enhancing Surgical Skill Acquisition. Comment on Hung et al. The Efficacy of Transcranial Direct Current Stimulation in Enhancing Surgical Skill Acquisition: A Preliminary Meta-Analysis of Randomized Controlled Trials. Brain Sci. 2021, 11, 707. Brain Sciences 11, 1078 (2021).
Nakajima, R., Kinoshita, M., Shinohara, H. & Nakada, M. The superior longitudinal fascicle: reconsidering the fronto-parietal neural network based on anatomy and function. Brain Imaging and Behavior 14, 2817–2830 (2020).
Walia, P., Kamat, A., De, S. & Dutta, A. Dynamic causal modeling for EEG during complex laparoscopic skill acquisition. (2021).
Howells, H. et al. Frontoparietal Tracts Linked to Lateralized Hand Preference and Manual Specialization. Cerebral Cortex (New York, NY) 28, 2482 (2018).
Walia, P. et al. Neuroimaging guided tES to facilitate complex laparoscopic surgical tasks – insights from functional near-infrared spectroscopy. (2021) doi:10.21203/rs.3.rs-730076/v1.
Pascual-Diaz, S., Varriano, F., Pineda, J. & Prats-Galino, A. Structural characterization of the Extended Frontal Aslant Tract trajectory: A ML-validated laterality study in 3T and 7T. NeuroImage 222, 117260 (2020).
Catani, M. et al. Short frontal lobe connections of the human brain. Cortex 48, 273–291 (2012).
La Corte, E. et al. The Frontal Aslant Tract: A Systematic Review for Neurosurgical Applications. Frontiers in Neurology 12, 51 (2021).
Spampinato, D. A., Celnik, P. A. & Rothwell, J. C. Cerebellar–Motor Cortex Connectivity: One or Two Different Networks? J. Neurosci. 40, 4230–4239 (2020).
Li, Z. et al. Dynamic functional connectivity revealed by resting-state functional near-infrared spectroscopy. Biomedical Optics Express 6, 2337 (2015).
Wang, Z. et al. Best window width determination and glioma analysis application of dynamic brain network measure on resting-state functional magnetic resonance imaging. Journal of Medical Imaging and Health Informatics 6, 1735–1740 (2016).
Schwartze, M., Rothermich, K. & Kotz, S. A. Functional dissociation of pre-SMA and SMA-proper in temporal processing. NeuroImage 60, 290–298 (2012).
Linsk, A. M. et al. Validation of the VBLaST pattern cutting task: a learning curve study. Surg Endosc 32, 1990–2002 (2018).
Aasted, C. M. et al. Anatomical guidance for functional near-infrared spectroscopy: AtlasViewer tutorial. Neurophotonics 2, (2015).
Jahani, S., Setarehdan, S. K., Boas, D. A. & Yücel, M. A. Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky–Golay filtering. Neurophotonics 5, (2018).
Seth, A. K., Barrett, A. B. & Barnett, L. Granger Causality Analysis in Neuroscience and Neuroimaging. Journal of Neuroscience 35, 3293–3297 (2015).
Leonardi, N. & Van De Ville, D. On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage 104, 430–436 (2015).
Auer, D. P. Spontaneous low-frequency blood oxygenation level-dependent fluctuations and functional connectivity analysis of the ‘resting’ brain. Magnetic Resonance Imaging 26, 1055–1064 (2008).
Richardson, J. T. E. Eta squared and partial eta squared as measures of effect size in educational research. Educational Research Review 6, 135–147 (2011).
Dhamala, M., Rangarajan, G. & Ding, M. Estimating granger causality from fourier and wavelet transforms of time series data. Physical Review Letters 100, 018701 (2008).
Geweke, J. Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 77, 304–313 (1982).

Table 1: Regression weight estimates for path analysis of the FLS score (grayed rows: significant after controlling for a false discovery rate of 0.05 with Benjamini–Hochberg adjustment). Standard errors (S.E.) and critical ratios (C.R.) are also presented in addition to the p value (P) with two significant figures.

Dependent var.		Independent variable	Weight Estimate	S.E.	C.R.	P
RPFC to LPMC	<---	Expert vs. Novice	0.02	0.12	0.13	0.89
RPFC to SMA	<---	Expert vs. Novice	-0.23	0.08	-2.94	0.00
LPMC to RPFC	<---	Expert vs. Novice	0.15	0.11	1.47	0.14
LPMC to SMA	<---	Expert vs. Novice	-0.21	0.09	-2.45	0.01
SMA to LPFC	<---	Expert vs. Novice	0.22	0.09	2.32	0.02
RPFC to LPMC	<---	Physical vs. VR Simulator	0.23	0.12	1.93	0.05
RPFC to SMA	<---	Physical vs. VR Simulator	-0.13	0.08	-1.63	0.10
LPMC to RPFC	<---	Physical vs. VR Simulator	-0.09	0.11	-0.84	0.40
LPMC to SMA	<---	Physical vs. VR Simulator	0.06	0.09	0.64	0.52
SMA to LPFC	<---	Physical vs. VR Simulator	-0.12	0.09	-1.26	0.21
FLS score	<---	RPFC to LPMC	-7.11	5.83	-1.22	0.22
FLS score	<---	RPFC to SMA	21.01	8.43	2.49	0.01
FLS score	<---	LPMC to RPFC	3.66	6.50	0.56	0.57
FLS score	<---	LPMC to SMA	3.13	7.89	0.40	0.69
FLS score	<---	SMA to LPFC	-1.16	7.15	-0.16	0.87

Table 2: Regression weight estimates for path analysis of CoV of the FLS score (grayed rows: significant after controlling for a false discovery rate of 0.05 with Benjamini–Hochberg adjustment). Standard errors (S.E.) and critical ratios (C.R.) are also shown in addition to the p value (P) with two significant figures.

Dependent var.		Independent variable	Weight Estimate	S.E.	C.R.	P
RPMC to LPMC	<---	Expert vs. Novice	0.05	0.11	0.47	0.64
LPMC to LPFC	<---	Expert vs. Novice	-0.03	0.10	-0.34	0.74
RPMC to LPMC	<---	Physical vs. VR Simulator	0.05	0.11	0.43	0.67
LPMC to LPFC	<---	Physical vs. VR Simulator	-0.02	0.10	-0.19	0.85
LPMC to RPMC	<---	Expert vs. Novice	-0.02	0.13	-0.17	0.87
LPMC to RPMC	<---	Physical vs. VR Simulator	0.08	0.13	0.60	0.55
FLS CoV	<---	RPMC to LPMC	0.21	0.08	2.82	0.01
FLS CoV	<---	LPMC to LPFC	-0.30	0.08	-3.74	<0.001
FLS CoV	<---	LPMC to RPMC	-0.05	0.06	-0.78	0.44

Table 3: Subject demographics

Physical simulator
Novice
Subject	Age	Gender	Specialization
N1	32	M	General Surgery
N2	31	F	General Surgery
N3	30	M	General Surgery
N4	33	M	General Surgery
N5	32	F	General Surgery
N6	30	M	Orthopedic Surgery

Expert
E1	38	F	General Surgery
E2	49	F	General Surgery
E3	30	M	General Surgery
E4	30	F	General Surgery
E5	37	M	General Surgery
E6	32	F	General Surgery
E7	31	M	General Surgery

VR simulator
Novice
Subject	Age	Gender	Specialization
N1	31	F	General Surgery
N2	30	M	General Surgery
N3	33	M	General Surgery
N4	32	F	General Surgery
N5	30	M	Orthopedic Surgery
N6	26	M	General Surgery

Expert
E1	38	F	General Surgery
E2	49	F	General Surgery
E3	30	M	General Surgery
E4	30	F	General Surgery
E5	37	M	General Surgery
E6	34	M	General Surgery

(Not answered)

FLStask.mp4
FLS pattern cutting task video
SupplementartMaterials07182022.pdf

Download PDF

Journal Publication

published 25 Aug, 2022

Read the published version in npj Science of Learning →

Editorial decision: accept
04 Aug, 2022
Submission checks completed at journal
04 Aug, 2022
Unknown event
20 Jul, 2022
Editor assigned by journal
18 Jul, 2022
First submitted to journal
18 Jul, 2022

You are reading this latest preprint version

Directed information flow during laparoscopic surgical skill acquisition dissociated skill level and medical simulation technology

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Results

Discussion

Methods

Subjects and experimental design

fNIRS data processing for the oxyhemoglobin time series

Granger causality analysis

Directed functional brain network

Statistical analysis

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1