Continuous Monitoring of Mental Load During Virtual Simulator Training for Laparoscopic Surgery Reflects Laparoscopic Dexterity: A Comparative Study Using a Novel Wireless Device.

INTRODUCTION: Cognitive Load Theory (CLT) relates to the efficiency with which individuals manipulate the limited capacity of working memory load. Repeated training generally results in individual performance increase and cognitive load decrease, as measured by both behavioral and neuroimaging methods. One of the known biomarkers for cognitive load is frontal theta band, measured by an EEG. Simulation-based training is an effective tool for acquiring practical skills, specifically to train new surgeons in a controlled and hazard-free environment. Measuring the cognitive load of young surgeons undergoing such training can help to determine whether they are ready to take part in a real surgery. In this study, we measured the performance of medical students and interns in a surgery simulator, while their brain activity was monitored by a single-channel EEG. METHODS: A total of 38 medical students and interns were divided into three groups and underwent three experiments examining their behavioral performances. The participants were performing a task while being monitored by the Simbionix LAP MENTOR™. Their brain activity was simultaneously measured using a single-channel EEG with novel signal processing (Aurora by Neurosteer®). Each experiment included three trials of a simulator task performed with laparoscopic hands. The time retention between the tasks was different in each experiment, in order to examine changes in performance and cognitive load biomarkers that occurred during the task or as a result of nighttime sleep consolidation. RESULTS: The participants' behavioral performance improved with trial repetition in all three experiments. In Experiments 1 and 2, delta band and the novel VC9 biomarker (previously shown to correlate with cognitive load) exhibited a significant decrease in activity with trial repetition. Additionally, delta, VC9, and, to some extent, theta activity decreased with better individual performance. DISCUSSION: In correspondence with previous research, EEG markers delta, VC9, and theta (partially) decreased with lower cognitive load and higher performance; the novel biomarker, VC9, showed higher sensitivity to lower cognitive load levels. Together, these measurements may be used for the neuroimaging assessment of cognitive load while performing simulator laparoscopic tasks. This can potentially be expanded to evaluate the efficacy of different medical simulations to provide more efficient training to medical staff and measure cognitive and mental loads in real laparoscopic surgeries.

Volitional limbic neuromodulation exerts a beneficial clinical effect on Fibromyalgia.

Volitional neural modulation using neurofeedback has been indicated as a potential treatment for chronic conditions that involve peripheral and central neural dysregulation. Here we utilized neurofeedback in patients suffering from Fibromyalgia - a chronic pain syndrome that involves sleep disturbance and emotion dysregulation. These ancillary symptoms, which have an amplificating effect on pain, are known to be mediated by heightened limbic activity. In order to reliably probe limbic activity in a scalable manner fit for EEG-neurofeedback training, we utilized an Electrical Finger Print (EFP) model of amygdala-BOLD signal (termed Amyg-EFP), that has been successfully validated in our lab in the context of volitional neuromodulation. We anticipated that Amyg-EFP-neurofeedback training aimed at limbic down modulation would improve chronic pain in patients suffering from Fibromyalgia, by reducing sleep disorder improving emotion regulation. We further expected that improved clinical status would correspond with successful training as indicated by improved down modulation of the Amygdala-EFP signal. Thirty-Four Fibromyalgia patients (31F; age 35.6 ±â€¯11.82) participated in a randomized placebo-controlled trial with biweekly Amyg-EFP-neurofeedback sessions or sham neurofeedback (n = 9) for a total duration of five consecutive weeks. Following training, participants in the real-neurofeedback group were divided into good (n = 13) or poor (n = 12) modulators according to their success in the neurofeedback training. Before and after treatment, self-reports on pain, depression, anxiety, fatigue and sleep quality were obtained, as well as objective sleep indices. Long-term clinical follow-up was made available, within up to three years of the neurofeedback training completion. REM latency and objective sleep quality index were robustly improved following the treatment course only in the real-neurofeedback group (time × group p < 0.05) and to a greater extent among good modulators (time × sub-group p < 0.05). In contrast, self-report measures did not reveal a treatment-specific response at the end of the neurofeedback training. However, the follow-up assessment revealed a delayed improvement in chronic pain and subjective sleep experience, evident only in the real-neurofeedback group (time × group p < 0.05). Moderation analysis showed that the enduring clinical effects on pain evident in the follow-up assessment were predicted by the immediate improvements following training in objective sleep and subjective affect measures. Our findings suggest that Amyg-EFP-neurofeedback that specifically targets limbic activity down modulation offers a successful principled approach for volitional EEG based neuromodulation treatment in Fibromyalgia patients. Importantly, it seems that via its immediate sleep improving effect, the neurofeedback training induced a delayed reduction in the target subjective symptom of chronic pain, far and beyond the immediate placebo effect. This indirect approach to chronic pain management reflects the substantial link between somatic and affective dysregulation that can be successfully targeted using neurofeedback.

Monitoring cardiac stress using features extracted from S1 heart sounds.

It is known that acoustic heart sounds carry significant information about the mechanical activity of the heart. In this paper, we present a novel type of cardiac monitoring based on heart sound analysis. Specifically, we study two morphological features and their associations with physiological changes from the baseline state. The framework is demonstrated on recordings during laparoscopic surgeries of 15 patients. Insufflation, which is performed during laparoscopic surgery, provides a controlled, externally induced cardiac stress, enabling an analysis of each patient with respect to their own baseline. We demonstrate that the proposed features change during cardiac stress, and the change is more significant for patients with cardiac problems. Furthermore, we show that other well-known ECG morphology features are less sensitive in this specific cardiac stress experiment.

The use of pneumoperitoneum during laparoscopic surgery as a model to study pathophysiologic phenomena: the correlation of cardiac functionality with computerized acoustic indices--preliminary data.

BACKGROUND: Induction of pneumoperitoneum during laparoscopic surgery leads to diverse cardiovascular changes that can be used as a model to study pathophysiologic phenomena. Application of novel signal processing and figure extraction enabled searching for correlation between various signals and pathophysiologic setting. Our aim was to quantitatively correlate cardiac functionality (as expressed by cardiac output) with the spectral energy of the first heart sound (S1) obtained from the phonocardiogram, during laparoscopic surgery. PATIENTS AND METHODS: Patients who were scheduled for elective laparoscopic operations were enrolled in the study. Cardiac output was maximally changed during anesthesia and abdominal insufflation and was obtained from the arterial pressure wave (FloTrac™ sensor and Vigileo™ monitor [Edwards Lifesciences Ltd.]). Heart signals were recorded during surgery from each subject by a computerized digital data acquisition system. The automatic analysis of the heart sounds included segmentation that was based on the energy envelope of the heart sounds together with analysis of the electrocardiogram signal. We analyzed the morphology of the sounds using hierarchial cluster analysis to remove those sounds that were not reliably recorded. The magnitude of the amplitude of heart sounds was obtained by using the Hilbert transform for each heartbeat. Statistical analysis was based on linear regression. RESULTS: Following exclusion of 3 patients (mainly because of technical reasons), we were left with 7 patients who demonstrated statistically significant positive correlation between cardiac index and the amplitude of S1 (regression coefficient between 0.4 and 0.9, P<.05). Linear regression analysis was done on the normalized values of all 7 patients and was found to be highly significant. CONCLUSIONS: In this study we have demonstrated significant linear correlation between the acoustic amplitude (spectral energy) of S1 and cardiac functionality, through sophisticated computerized analysis, using the pneumoperitoneum model for changing the cardiac output.

Free water elimination and mapping from diffusion MRI.

Relating brain tissue properties to diffusion tensor imaging (DTI) is limited when an image voxel contains partial volume of brain tissue with free water, such as cerebrospinal fluid or edema, rendering the DTI indices no longer useful for describing the underlying tissue properties. We propose here a method for separating diffusion properties of brain tissue from surrounding free water while mapping the free water volume. This is achieved by fitting a bi-tensor model for which a mathematical framework is introduced to stabilize the fitting. Applying the method on datasets from a healthy subject and a patient with edema yielded corrected DTI indices and a more complete tract reconstruction that passed next to the ventricles and through the edema. We were able to segment the edema into areas according to the condition of the underlying tissue. In addition, the volume of free water is suggested as a new quantitative contrast of diffusion MRI. The findings suggest that free water is not limited to the borders of the brain parenchyma; it therefore contributes to the architecture surrounding neuronal bundles and may indicate specific anatomical processes. The analysis requires a conventional DTI acquisition and can be easily merged with existing DTI pipelines.