Disparity in Frontal Lobe Connectivity on a Complex Bimanual Motor Task Aids in Classification of Operator Skill Level

Abstract

Objective metrics of technical performance (e.g. dexterity, time and path length) are insufficient to fully characterize operator skill level, which may be encoded deep within neural function. Unlike reports that capture plasticity across days or weeks, this paper studies long-term plasticity in functional connectivity that occurs over years of professional task practice. Optical neuroimaging data are acquired from professional surgeons of varying experience on a complex bimanual co-ordination task with the aim of investigating learningrelated disparity in frontal lobe functional connectivity that arises as a consequence of motor skill level. The results suggest that prefrontal and premotor seed connectivity is more critical during naïve versus expert performance. Given learning-related differences in connectivity, a least-squares support vector machine with a radial basis function kernel is employed to evaluate skill level using connectivity data. The results demonstrate discrimination of operator skill level with accuracy ≥ 0.82 and Multiclass Mathew's Correlation Coefficient ≥ 0.70. Furthermore, these indices are improved when local (i.e. within-region) rather than inter-regional (i.e. between-region) frontal connectivity is considered (p=0.002). The results suggest that it is possible to classify operator skill level with good accuracy from functional connectivity data, upon which objective assessment and neuro-feedback may be used to improve operator performance during technical skill training. This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.

Assessing bimanual motor skills with optical neuroimaging

“…Supplementary Figure 9a-c. These results clearly demonstrate that previously reported fNIRS based metrics with the inclusion of superficial tissue responses can statistically differentiate surgical novices and experts [13][14][15][16]40 , yet fail to classify subjects based on motor skill proficiency and perform as poorly as current surgical skill assessment metrics. In contrast, regressing shallow tissue hemodynamics from the optical measurements significantly reduces the false omission rate, where a surgical novice is mistakenly classified as an expert, to 0% whereas previous approaches still maintain false omission rates of 13-18% (see Supplemental Table 2).…”

“…Hence, fNIRS is a promising neuroimaging modality to study cortical brain activations but to date, only a very limited number of studies have been reported in regards to assessing fine surgical motor skills 13 . These exploratory studies have reported differentiation in functional cortical activations between groups with varying surgical motor skills [13][14][15][16][17] . However, they suffer from recognized limitations 13 such as such as the lack of signal specificity between scalp and cortical hemodynamics 18,19 , the lack of multivariate statistical approaches that leverage changes in functional brain activity across multiple brain regions, and benchmarking against established metrics.…”

Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

“…While we observed marked interactions between many regions, the majority were located in the frontal lobe for all frequency bands. Previous work has also demonstrated changes in frontal-motor [75,76] and fronto-parietal [77] connectivity during motor skill learning. In BCI learning specifically, the strength of white matter connectivity between frontal and occipital regions predicts control of motor imagery based BCIs [78].…”

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“…users may alter the cognitive profile intentionally or unintentionally during the experiment [22]; 3) changes in the environmental conditions; 4) considering the BMI context, users may have different perceptual demands for different tasks, hence the uncertainties as to whether the same cognitive patterns will replicate for the same intentions. The first three are practically inherent to any BMI application, while the fourth one is raised by the goal of obtaining real-life BMI systems, where task-specific training data is difficult to obtain or very limited.…”