Error-related brain state analysis using electroencephalography in conjunction with functional near-infrared spectroscopy during a complex surgical motor task

Abstract

Error-based learning is one of the basic skill acquisition mechanisms that can be modeled as a perception–action system and investigated based on brain–behavior analysis during skill training. Here, the error-related chain of mental processes is postulated to depend on the skill level leading to a difference in the contextual switching of the brain states on error commission. Therefore, the objective of this paper was to compare error-related brain states, measured with multi-modal portable brain imaging, between experts and novices during the Fundamentals of Laparoscopic Surgery (FLS) “suturing and intracorporeal knot-tying” task (FLS complex task)—the most difficult among the five psychomotor FLS tasks. The multi-modal portable brain imaging combined functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for brain–behavior analysis in thirteen right-handed novice medical students and nine expert surgeons. The brain state changes were defined by quasi-stable EEG scalp topography (called microstates) changes using 32-channel EEG data acquired at 250 Hz. Six microstate prototypes were identified from the combined EEG data from experts and novices during the FLS complex task that explained 77.14% of the global variance. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10-s error epoch was significantly affected by the skill level (p < 0.01), the microstate type (p < 0.01), and the interaction between the skill level and the microstate type (p < 0.01). Brain activation based on the slower oxyhemoglobin (HbO) changes corresponding to the EEG band power (1–40 Hz) changes were found using the regularized temporally embedded Canonical Correlation Analysis of the simultaneously acquired fNIRS–EEG signals. The HbO signal from the overlying the left inferior frontal gyrus—opercular part, left superior frontal gyrus—medial orbital, left postcentral gyrus, left superior temporal gyrus, right superior frontal gyrus—medial orbital cortical areas showed significant (p < 0.05) difference between experts and novices in the 10-s error epoch. We conclude that the difference in the error-related chain of mental processes was the activation of cognitive top-down attention-related brain areas, including left dorsolateral prefrontal/frontal eye field and left frontopolar brain regions, along with a ‘focusing’ effect of global suppression of hemodynamic activation in the experts, while the novices had a widespread stimulus(error)-driven hemodynamic activation without the ‘focusing’ effect.

Brain-behavior analysis of transcranial direct current stimulation effects on a complex surgical motor task

“…In our prior work (Walia et al, 2022a), we have shown contextual switching of the brain state during laparoscopic suturing with intracorporeal knot tying task-errors that can be related to error perception and corrective action (Benozzo et al, 2021). In the current study, we performed tDCS of the left VLPFC to facilitate subjective task-error awareness (Wessel, 2012) by activating the ventral attention system (Vossel et al, 2014).…”

“…The ventral stream (Kamat et al, 2022a), which senses the feedback ( external monitoring ) of the environment in the primary sensory cortex, flows to the sensory association cortex, and then to the posterior association cortex (such as the supramarginal gyrus), leading to the perception of conscious errors in the ventrolateral prefrontal cortex (VLPFC). Here, controlled goaldirected attention may be facilitated with tDCS of the dorsolateral PFC (Ashcroft et al, 2020) while (unexpected) error stimulus-driven attention may need tDCS of the VLPFC (Walia et al, 2022a), (Walia et al, 2021a) indeed, they have dissociable brain connectivity (Seeley et al, 2007).…”

“…Error-based learning is one of the basic skill acquisition mechanisms, which includes error detection, error correction, and subsequent performance adjustments (Seidler et al, 2013). Here, individual differences in error perception and attention reorientation for error correction actions are argued to be different between experts and beginners (Walia et al, 2022a). In particular, errors can be preemptively corrected by prediction mechanisms based on the forward model (Wolpert and Miall, 1996), which are suggested to improve with expertise.…”

“…functional near-infrared spectroscopy (fNIRS) (Nemani et al, 2018) and electroencephalography (EEG) (Ciechanski et al, 2019), and relating the brain activation changes to behavior can provide mechanistic insights. In particular, a distinction can be made between based on a predictive forward modeling framework (Wolpert and Miall, 1996) that can be modeled as error perception corrective action coupling (Kamat et al, 2022a;Walia et al, 2022a). Then, tDCS may facilitate mparator system in the cerebellum (Tanaka et al, 2020;Welniarz et al, 2021) event is sensed to drive the attention reorientation for skilled corrective action (Walia et al, 2022a).…”

“…In particular, a distinction can be made between based on a predictive forward modeling framework (Wolpert and Miall, 1996) that can be modeled as error perception corrective action coupling (Kamat et al, 2022a;Walia et al, 2022a). Then, tDCS may facilitate mparator system in the cerebellum (Tanaka et al, 2020;Welniarz et al, 2021) event is sensed to drive the attention reorientation for skilled corrective action (Walia et al, 2022a).…”