SignificanceAs trainees practice fundamental surgical skills, they typically rely on performance measures such as time and errors, which are limited in their sensitivity.AimThe goal of our study was to evaluate the use of portable neuroimaging measures to map the neural processes associated with learning basic surgical skills.ApproachTwenty-one subjects completed 15 sessions of training on the fundamentals of laparoscopic surgery (FLS) suture with intracorporeal knot-tying task in a box trainer. Functional near infrared spectroscopy data were recorded using an optode montage that covered the prefrontal and sensorimotor brain areas throughout the task. Average oxy-hemoglobin (HbO) changes were determined for repetitions performed during the first week of training compared with the third week of training. Statistical differences between the time periods were evaluated using a general linear model of the HbO changes.ResultsAverage performance scores across task repetitions increased significantly from the first day to the last day of training (p < 0.01). During the first day of training, there was significant lateral prefrontal cortex (PFC) activation. On the final day, significant activation was observed in the PFC, as well as the sensorimotor areas. When comparing the two periods, significant differences in activation (p < 0.05) were found for the right medial PFC and the right inferior parietal gyrus. While gaining proficiency, trainees activated the perception-action cycle to build a perceptual model and then apply the model to improve task execution.ConclusionsLearners engaged the sensorimotor areas more substantially as they developed skill on the FLS suturing task. These findings are consistent with findings for the FLS pattern cutting task and contribute to the development of objective metrics for skill evaluation.
Perception-action cycle-based motor learning theory postulates coupled action and perception for visuomotor learning. We hypothesized that perception-action-related brain connectivity will underpin visuomotor skill levels in a complex motor task based on this theory. We tested our hypothesis using multi-modal brain imaging on healthy human subjects (N=6 experts, N= 8 novice, all right-handed) during the performance of fundamentals of laparoscopic surgery (FLS) "suturing and intracorporeal knot-tying" task. We investigated dynamic directed brain networks using nonoverlapping sliding window-based spectral Granger causality (GC) from simultaneously acquired electroencephalogram (EEG), and functional near-infrared spectroscopy (fNIRS) signals. Our GC analysis on EEG signals showed the flow of information from the supplementary motor area complex (SMA) to the left primary motor cortex (LM1) that was statistically different (p <0.05) between the experts and novices. This result aligned with the perception action cycle theory where SMA is central to the orderly descent from the prefrontal to the motor cortex in Fuster's perception-action processing stages. The GC analysis of the fNIRS oxyhemoglobin signal revealed the connectivity from left to right primary motor cortex (LM1 to RM1) and LM1 to left prefrontal cortex (LPFC) that was significantly different (p <0.05) between the cohorts. Here, our preliminary results supported the involvement of perception-action-related directed brain connectivity in distinguishing the skill levels during a complex laparoscopic task that was measured with portable brain imaging during task performance. Future studies need to investigate the fusion of the EEG and fNIRS networks for the causal brain-behavior analysis of complex motor skill acquisition.
Optical neuroimaging is a promising tool to assess motor skills execution. Especially, functional near-infrared spectroscopy (fNIRS) enables the monitoring of cortical activations in scenarios such as surgical task execution. fNIRS data sets are typically preprocessed to derive a few biomarkers that are used to provide a correlation between cortical activations and behavior. Meanwhile, Deep Learning methodologies have found great utility in the data processing of complex spatiotemporal data for classification or prediction tasks. Here, we report on a Deep Convolutional model that takes spatiotemporal fNIRS data sets as input to classify subjects performing a Fundamentals of Laparoscopic Surgery (FLS) task used in board certification of general surgeons in the United States. This convolutional neural network (CNN) uses dilated kernels paired with multiple stacks of convolution to capture long-range dependencies in the fNIRS time sequence. The model is trained in a supervised manner on 474 FLS trials obtained from seven subjects and assessed independently by stratified-10-fold cross-validation (CV). Results demonstrate that the model can learn discriminatory features between passed and failed trials, attaining 0.99 and 0.95 area under the Receiver Operating Characteristics (ROC) and Precision-Recall curves, respectively. The reported accuracy, sensitivity, and specificity are 97.7%, 81%, and 98.9%, respectively.
Significance: Surgical simulators, both virtual and physical, are increasingly used as training tools for teaching and assessing surgical technical skills. However, the metrics used for assessment in these simulation environments are often subjective and inconsistent.
Aim: We propose functional activation metrics, derived from brain imaging measurements, to objectively assess the correspondence between brain activation with surgical motor skills for subjects with varying degrees of surgical skill.
Approach: Cortical activation based on changes in the oxygenated hemoglobin (HbO) of 36 subjects was measured using functional near-infrared spectroscopy at the prefrontal cortex (PFC), primary motor cortex, and supplementary motor area (SMA) due to their association with motor skill learning. Inter-regional functional connectivity metrics, namely, wavelet coherence (WCO) and wavelet phase coherence were derived from HbO changes to correlate brain activity to surgical motor skill levels objectively.
Results: One-way multivariate analysis of variance found a statistically significant difference in the inter-regional WCO metrics for physical simulator based on Wilk’s Λ for expert versus novice, F ( 10,1 ) = 7495.5, p < 0.01. Partial eta squared effect size for the inter-regional WCO metrics was found to be highest between the central prefrontal cortex (CPFC) and SMA, CPFC-SMA (η2 = 0.257). Two-tailed Mann–Whitney U tests with a 95% confidence interval showed baseline equivalence and a statistically significant (p < 0.001) difference in the CPFC-SMA WPCO metrics for the physical simulator training group (0.960 ± 0.045) versus the untrained control group (0.735 ± 0.177) following training for 10 consecutive days in addition to the pretest and posttest days.
Conclusion: We show that brain functional connectivity WCO metric corresponds to surgical motor skills in the laparoscopic physical simulators. Functional connectivity between the CPFC and the SMA is lower for subjects that exhibit expert surgical motor skills than untrained subjects in laparoscopic physical simulators.
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