Abstract:Acquisition of fine motor skills is a time-consuming process as it is based on learning via frequent repetitions. Transcranial electrical stimulation (tES) is a promising means of enhancing simple motor skill development via neuromodulatory mechanisms. Here, we report that non-invasive neurostimulation facilitates the learning of complex fine bimanual motor skills associated with a surgical task. During the training of 12 medical students on the Fundamentals of Laparoscopic Surgery (FLS) pattern cutting task o… Show more
“…Then, in a bimanual pattern cutting study with tDCS of the M1 along with portable neuroimaging [3], Gao et al observed that the M1 tDCS effect on the performance error was significant (p < 0.001; t-test when normally distributed or Mann-Whitney U test when not) after day 7 when compared to the sham group. Here, a delayed effect of M1 tDCS after day 7 was found that is postulated to be related to the emergence of M1 activation that was significant (p < 0.001) only during the latter learning stage (day 7-12) when compared to the initial learning stage (day 2-6) [3]. This is expected from known in vivo effects of tDCS that do not change the firing rates of the cortical neurons [31] but modulate endogenous task-specific brain activity [32], so neuroimaging can provide the "target" cortical activation related to endogenous task-specific brain activity.…”
mentioning
confidence: 71%
“…Surgical skill acquisition may be facilitated with a safe application of transcranial electrical stimulation (tES) [1]. Transcranial direct current stimulation (tDCS), a tES modality, has been shown to facilitate surgical skill learning when applied to cortical targets, including the primary motor cortex [2,3], the supplementary motor area [2], and the prefrontal cortex [4]. Prior work has shown that tDCS facilitated complex motor tasks performed during surgical skill training, including laparoscopic technical skills training [5] and tumor resection in neurosurgery [6].…”
Surgical skill acquisition may be facilitated with a safe application of transcranial direct current stimulation (tDCS). A preliminary meta-analysis of randomized control trials showed that tDCS was associated with significantly better improvement in surgical performance than the sham control; however, meta-analysis does not address the mechanistic understanding. It is known from skill learning studies that the hierarchy of cognitive control shows a rostrocaudal axis in the frontal lobe where a shift from posterior to anterior is postulated to mediate progressively abstract, higher-order control. Therefore, optimizing the transcranial electrical stimulation to target surgical task-related brain activation at different stages of motor learning may provide the causal link to the learning behavior. This comment paper presents the computational approach for neuroimaging guided tDCS based on open-source software pipelines and an open-data of functional near-infrared spectroscopy (fNIRS) for complex motor tasks. We performed an fNIRS-based cortical activation analysis using AtlasViewer software that was used as the target for tDCS of the motor complexity-related brain regions using ROAST software. For future studies on surgical skill training, it is postulated that the higher complexity laparoscopic suturing with intracorporeal knot tying task may result in more robust activation of the motor complexity-related brain areas when compared to the lower complexity laparoscopic tasks.
“…Then, in a bimanual pattern cutting study with tDCS of the M1 along with portable neuroimaging [3], Gao et al observed that the M1 tDCS effect on the performance error was significant (p < 0.001; t-test when normally distributed or Mann-Whitney U test when not) after day 7 when compared to the sham group. Here, a delayed effect of M1 tDCS after day 7 was found that is postulated to be related to the emergence of M1 activation that was significant (p < 0.001) only during the latter learning stage (day 7-12) when compared to the initial learning stage (day 2-6) [3]. This is expected from known in vivo effects of tDCS that do not change the firing rates of the cortical neurons [31] but modulate endogenous task-specific brain activity [32], so neuroimaging can provide the "target" cortical activation related to endogenous task-specific brain activity.…”
mentioning
confidence: 71%
“…Surgical skill acquisition may be facilitated with a safe application of transcranial electrical stimulation (tES) [1]. Transcranial direct current stimulation (tDCS), a tES modality, has been shown to facilitate surgical skill learning when applied to cortical targets, including the primary motor cortex [2,3], the supplementary motor area [2], and the prefrontal cortex [4]. Prior work has shown that tDCS facilitated complex motor tasks performed during surgical skill training, including laparoscopic technical skills training [5] and tumor resection in neurosurgery [6].…”
Surgical skill acquisition may be facilitated with a safe application of transcranial direct current stimulation (tDCS). A preliminary meta-analysis of randomized control trials showed that tDCS was associated with significantly better improvement in surgical performance than the sham control; however, meta-analysis does not address the mechanistic understanding. It is known from skill learning studies that the hierarchy of cognitive control shows a rostrocaudal axis in the frontal lobe where a shift from posterior to anterior is postulated to mediate progressively abstract, higher-order control. Therefore, optimizing the transcranial electrical stimulation to target surgical task-related brain activation at different stages of motor learning may provide the causal link to the learning behavior. This comment paper presents the computational approach for neuroimaging guided tDCS based on open-source software pipelines and an open-data of functional near-infrared spectroscopy (fNIRS) for complex motor tasks. We performed an fNIRS-based cortical activation analysis using AtlasViewer software that was used as the target for tDCS of the motor complexity-related brain regions using ROAST software. For future studies on surgical skill training, it is postulated that the higher complexity laparoscopic suturing with intracorporeal knot tying task may result in more robust activation of the motor complexity-related brain areas when compared to the lower complexity laparoscopic tasks.
“… 1 – 4 HbO and HbR time series can reflect changes in neurovascular coupling and hence, neuronal activity. fNIRS has been widely used in cognitive, 5 – 9 motor skill studies, 10 – 15 and brain–computer interface technique. 16 …”
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Significance:
Functional near-infrared spectroscopy (fNIRS), a well-established neuroimaging technique, enables monitoring cortical activation while subjects are unconstrained. However, motion artifact is a common type of noise that can hamper the interpretation of fNIRS data. Current methods that have been proposed to mitigate motion artifacts in fNIRS data are still dependent on expert-based knowledge and the post hoc tuning of parameters.
Aim:
Here, we report a deep learning method that aims at motion artifact removal from fNIRS data while being assumption free. To the best of our knowledge, this is the first investigation to report on the use of a denoising autoencoder (DAE) architecture for motion artifact removal.
Approach:
To facilitate the training of this deep learning architecture, we (i) designed a specific loss function and (ii) generated data to mimic the properties of recorded fNIRS sequences.
Results:
The DAE model outperformed conventional methods in lowering residual motion artifacts, decreasing mean squared error, and increasing computational efficiency.
Conclusion:
Overall, this work demonstrates the potential of deep learning models for accurate and fast motion artifact removal in fNIRS data.
“…Then, the hierarchy of cognitive control during skill learning shows a rostrocaudal axis in the frontal lobe 45 , where a shift from posterior to anterior is postulated to mediate the progressively abstract, higher-order control expected from experts. Numerous functional magnetic resonance imaging (fMRI) and fNIRS studies have been published on skill learning 33,46,47,48,49,50,51,52,53 , including training under stress 54 ; however, these studies have not systematically investigated the directional cortical information ow 55,56 , its variability during FLS skill acquisition in physical versus VR simulators, and its interaction with the skill level based on statistical path analysis 57,58 . fNIRS-based brain imaging has limited spatial and depth sensitivity 59 .…”
The comparison of the effects of physical and virtual reality (VR) simulators on the brain network during skill acquisition has not been well addressed. In this study, the brain network and skilled behavior relationship were evaluated using functional near-infrared spectroscopy (fNIRS) data from seven experienced right-handed surgeons and six right-handed medical students during the performance of a well-established Fundamentals of Laparoscopic Surgery (FLS) pattern of cutting tasks. Multiple regression path analysis found that the FLS performance score was statistically significantly related to the interregional directed functional connectivity from the right prefrontal cortex to the supplementary motor area with F(2, 114) = 9, p < 0.001, and R2 = 0.136. The coefficient of variation (CoV) of the FLS performance score was statistically significantly related to the CoV of the interregionally directed functional connectivity from the right primary motor cortex to the left primary motor cortex and the left primary motor cortex to the left prefrontal cortex with F(2, 22) = 3.912, p = 0.035, and R2 = 0.262. Additionally, a two-way multivariate analysis of variance (MANOVA) found a statistically significant effect of the simulator technology on the interregional directed functional connectivity from the right prefrontal cortex to the left primary motor cortex (F(1,15) = 6.002, p = 0.027; partial η2 = 0.286). This involvement of the right prefrontal cortex is potentially related to the uncertainty that underpins FLS task performance based on skill level and medical simulator technology.
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