title: 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 articles 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 coordination task with the aim of investigating learning-related 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 Matthew'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 neurofeedback may be used to improve operator performance during technical skill training.
authors.affiliation: 1 The Hamlyn Centre Imperial College London , London, United Kingdom .
authors.forename: Javier
authors.initials: J
authors.lastname: Andreu-Perez
authors.affiliation: 1 The Hamlyn Centre Imperial College London , London, United Kingdom .
authors.forename: Daniel Richard
authors.initials: DR
authors.lastname: Leff
authors.affiliation: 1 The Hamlyn Centre Imperial College London , London, United Kingdom .
authors.forename: Kunal
authors.initials: K
authors.lastname: Shetty
authors.affiliation: 1 The Hamlyn Centre Imperial College London , London, United Kingdom .
authors.forename: Ara
authors.initials: A
authors.lastname: Darzi
authors.affiliation: 1 The Hamlyn Centre Imperial College London , London, United Kingdom .
authors.forename: Guang-Zhong
authors.initials: GZ
authors.lastname: Yang
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods / Q000706:statistics & numerical data; D063132:Connectome / Q000379:methods; D005625:Frontal Lobe / Q000502:physiology; D059907:Functional Neuroimaging; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D009048:Motor Skills / Q000145:classification* / Q000502:physiology*; D009434:Neural Pathways; D009473:Neuronal Plasticity / Q000502:physiology; D019265:Spectroscopy, Near-Infrared / Q000379:methods; D060388:Support Vector Machine
keywords: functional connectivity; functional near-infrared spectroscopy (fNIRS); motor learning; operator skill level; optical topography; technical skill levels assessment