The frontal cortex is activated during learning of endoscopic procedures

Ohuchida, Kenoki; Kenmotsu, Hajime; Yamamoto, Atsuyuki; Sawada, Kazuya; Hayami, Takehito; Morooka, Ken’ichi; Takasugi, Shin-ichiro; Konishi, Kozo; Ieiri, Satoshi; Tanoue, Kazuo; Iwamoto, Yukihide; Tanaka, Masao; Hashizume, Makoto

doi:10.1007/s00464-008-0316-z

Cited by 52 publications

(53 citation statements)

References 22 publications

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“…Noninvasive neuroimaging technologies such as electroencephalography (EEG) [14] and functional optical brain imaging have previously been applied to assess technical expertise [15], skills acquisition [15,16], cognitive burden [17] and fatigue [18]. These technologies can be applied to assess brain dynamics underlying cognitive processes even in actively moving participants [19,20].…”

Section: Introductionmentioning

confidence: 99%

Automated Task Load Detection with Electroencephalography: Towards Passive Brain–Computer Interfacing in Robotic Surgery

Zander

Shetty

Lorenz

et al. 2017

J. Med. Robot. Res.

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Automatic detection of the current task load of a surgeon in the theatre in real time could provide helpful information, to be used in supportive systems. For example, such information may enable the system to automatically support the surgeon when critical or stressful periods are detected, or to communicate to others when a surgeon is engaged in a complex maneuver and should not be disturbed. Passive brain-computer interfaces (BCI) infer changes in cognitive and affective state by monitoring and interpreting ongoing brain activity recorded via an electroencephalogram. The resulting information can then be used to automatically adapt a technological system to the human user. So far, passive BCI have mostly been investigated in laboratory settings, even though they are intended to be applied in real-world settings. In this study, a passive BCI was used to assess changes in task load of skilled surgeons performing both simple and complex surgical training tasks. Results indicate that the introduced methodology can reliably and continuously detect changes in task load in this realistic environment.

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Section: Introductionmentioning

confidence: 99%

Automated Task Load Detection with Electroencephalography: Towards Passive Brain–Computer Interfacing in Robotic Surgery

Zander

Shetty

Lorenz

et al. 2017

J. Med. Robot. Res.

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“…Whilst amplitude of the evoked response has been used for classification of operator states previously (Coyle et al, 2007;Naito et al, 2007;Power et al, 2012), there have been no such reports in surgeons. Indeed, whilst differences in amplitude signal change in executive 6 6 control and motor cortical regions related to surgical skill level have been previously observed (Leff et al, 2008b;Ohuchida et al, 2009), it has not been possible to discriminate operators' performance based solely on these signal characteristics. Classification of operator proficiency based on functional connectivity data represents a non-trivial high dimensionality problem for which conventional statistics are ill-posed to solve.…”

Section: Introductionmentioning

confidence: 99%

“…In a study with 18 subjects, decreasing ratios of oxygenated haemoglobin were observed in supplementary motor area (SMA) and preSMA when learning a motor skill (Hatenaka et al,5 5 2008; Halsband and Lange, 2006;Kelly and Garavan, 2005). This notwithstanding, investigations of highly complex motor skill levels such as MIS, have failed to demonstrate differences between naïve subjects and expert operators (Ohuchida et al, 2009). One theory is that for highly complex motor tasks such as MIS that require 2D to 3D perceptual transformation and precise inter-manual co-ordination, skill level-related disparity may manifest as differences in frontal lobe connectivity rather than changes in activation per se.…”

Section: Introductionmentioning

confidence: 99%

“…Advances in functional neuroimaging technology have made it possible to monitor operators in more realistic settings and track evolution in brain behaviour that accompanies motor skill levels learning. To this end, there has been an increasing number of research studies focused on studying evoked cortical response to complex motor behaviour in the context of open and minimally invasive surgery (MIS) (Bahrami et al, 2011;Duty et al, 2012;Leff et al, 2007a;Leff et al, 2008a;Leff et al, 2008b;Ohuchida et al, 2009;Zhu et al, 2011). In this study we used functional near-infrared spectroscopy (fNIRS), which has raised increasing interest in recent years for performing less constricted, hence more naturalistic neuroscience experiments (Rodrigo et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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“…Ohuchida et al used functional near infrared (IR) spectroscopy to study the prefrontal cortex activation while performing a laparoscopic task in a training box, and to highlight activation differences based on years of surgical experience (20). Leff et al used the same neuroimaging technique to explore the intersubject variability in novices learning minimally invasive surgery skills (21).…”

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confidence: 99%

Functional MRI‐compatible laparoscopic surgery training simulator

Bahrami

Schweizer

Tam

et al. 2010

Magnetic Resonance in Med

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During the past few years, laparoscopy has become the gold standard for some surgical procedures and its applications continue to expand. Because of multiple factors such as loss of tactile perception, two-dimensional visualization of the three-dimensional surgical field, and demanding bimanual hand-eye coordination, special training is required to achieve proficiency with laparoscopy. In this study, as the first step toward evidence-based development of strategies to improve the quality of laparoscopy training from brain activity and behavior relationships, a laparoscopy training simulator was developed for use in functional MRI. Experiments confirmed the functional MRI compatibility of the device. Representative behavioral and functional MRI results for two subjects showed the feasibility of using this simulator to investigate the brain activation associated with components of laparoscopic task performance. To our knowledge, this is the first study that directly looks at the functional MRI brain activation during complex surgical training tasks. Magn Reson Med 65:873-881,

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The frontal cortex is activated during learning of endoscopic procedures

Abstract: The present data suggest that NIRS is a feasible tool for assessing brain activation during endoscopic surgical tasks, and may have a large impact on the future development of teaching, training, and assessment methods for endoscopic surgical skills.

Cited by 52 publications

References 22 publications

Automated Task Load Detection with Electroencephalography: Towards Passive Brain–Computer Interfacing in Robotic Surgery

Automated Task Load Detection with Electroencephalography: Towards Passive Brain–Computer Interfacing in Robotic Surgery

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

Functional MRI‐compatible laparoscopic surgery training simulator

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