Empirical comparison of deep learning models for fNIRS pain decoding

IntroductionPain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement. However, assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions, individual differences in pain expression, and potential confounding factors. Therefore, the need for an objective pain assessment method that can assist medical practitioners. Functional near-infrared spectroscopy (fNIRS) has shown promising results to assess the neural function in response of nociception and pain. Previous studies have explored the use of machine learning with hand-crafted features in the assessment of pain.MethodsIn this study, we aim to expand previous studies by exploring the use of deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and (CNN-LSTM) to automatically extract features from fNIRS data and by comparing these with classical machine learning models using hand-crafted features.ResultsThe results showed that the deep learning models exhibited favourable results in the identification of different types of pain in our experiment using only fNIRS input data. The combination of CNN and LSTM in a hybrid model (CNN-LSTM) exhibited the highest performance (accuracy = 91.2%) in our problem setting. Statistical analysis using one-way ANOVA with Tukey's (post-hoc) test performed on accuracies showed that the deep learning models significantly improved accuracy performance as compared to the baseline models.DiscussionOverall, deep learning models showed their potential to learn features automatically without relying on manually-extracted features and the CNN-LSTM model could be used as a possible method of assessment of pain in non-verbal patients. Future research is needed to evaluate the generalisation of this method of pain assessment on independent populations and in real-life scenarios

Magnetic Interaction between Surface Engineered Rare-earth Atomic Spins

We report the ab initio study of rare-earth adatoms (Gd) on an insulating surface. This surface is of interest because of previous studies by scanning tunneling microscopy showing spin excitations of transition metal adatoms. The present work is the first study of rare-earth spin-coupled adatoms, as well as the geometry effect of spin coupling, and the underlying mechanism of ferromagnetic coupling. The exchange coupling between Gd atoms on the surface is calculated to be antiferromagnetic in a linear geometry and ferromagnetic in a diagonal geometry, by considering their collinear spins and using the PBE+U exchange correlation. We also find the Gd dimers in these two geometries are similar to the nearest-neighbor (NN) and the next-NN Gd atoms in GdN bulk. We analyze how much direct exchange, superexchange, and RKKY interactions contribute to the exchange coupling for both geometries by additional first-principles calculations of related model systems

Empirical comparison of deep learning models for fNIRS pain decoding

Introduction: Pain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement. However, assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions, individual differences in pain expression, and potential confounding factors. Therefore, the need for an objective pain assessment method that can assist medical practitioners. Functional near-infrared spectroscopy (fNIRS) has shown promising results to assess the neural function in response of nociception and pain. Previous studies have explored the use of machine learning with hand-crafted features in the assessment of pain.Methods: In this study, we aim to expand previous studies by exploring the use of deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and (CNN-LSTM) to automatically extract features from fNIRS data and by comparing these with classical machine learning models using hand-crafted features.Results: The results showed that the deep learning models exhibited favourable results in the identification of different types of pain in our experiment using only fNIRS input data. The combination of CNN and LSTM in a hybrid model (CNN-LSTM) exhibited the highest performance (accuracy = 91.2%) in our problem setting. Statistical analysis using one-way ANOVA with Tukey's (post-hoc) test performed on accuracies showed that the deep learning models significantly improved accuracy performance as compared to the baseline models.Discussion: Overall, deep learning models showed their potential to learn features automatically without relying on manually-extracted features and the CNN-LSTM model could be used as a possible method of assessment of pain in non-verbal patients. Future research is needed to evaluate the generalisation of this method of pain assessment on independent populations and in real-life scenarios

Cortical Network Response to Acupuncture and the Effect of the Hegu Point:An FNIRS study

Acupuncture is a practice of treatment based on influencing specific points on the body by inserting needles. According to traditional Chinese medicine, the aim of acupuncture treatment for pain management is to use specific acupoints to relieve excess, activate qi (or vital energy), and improve blood circulation. In this context, the Hegu point is one of the most widely-used acupoints for this purpose, and it has been linked to having an analgesic effect. However, there exists considerable debate as to its scientific validity. In this pilot study, we aim to identify the functional connectivity related to the three main types of acupuncture manipulations and also identify an analgesic effect based on the hemodynamic response as measured by functional near-infrared spectroscopy (fNIRS). The cortical response of eleven healthy subjects was obtained using fNIRS during an acupuncture procedure. A multiscale analysis based on wavelet transform coherence was employed to assess the functional connectivity of corresponding channel pairs within the left and right somatosensory region. The wavelet analysis was focused on the very-low frequency oscillations (VLFO, 0.01–0.08 Hz) and the low frequency oscillations (LFO, 0.08–0.15 Hz). A mixed model analysis of variance was used to appraise statistical differences in the wavelet domain for the different acupuncture stimuli. The hemodynamic response after the acupuncture manipulations exhibited strong activations and distinctive cortical networks in each stimulus. The results of the statistical analysis showed significant differences ( p < 0.05 ) between the tasks in both frequency bands. These results suggest the existence of different stimuli-specific cortical networks in both frequency bands and the anaesthetic effect of the Hegu point as measured by fNIRS

Analysis of Pain Hemodynamic Response Using Near-Infrared Spectroscopy (NIRS)

Despite recent advances in brain research, understanding the various signals for pain and pain intensities in the brain cortex is still a complex task due to temporal and spatial variations of brain hemodynamics. In this paper we have investigated pain based on cerebral hemodynamics via near-infrared spectroscopy (NIRS). This study presents a pain stimulation experiment that uses three acupuncture manipulation techniques to safely induce pain in healthy subjects. Acupuncture pain response was presented and hemodynamic pain signal analysis showed the presence of dominant channels and their relationship among surrounding channels, which contribute the further pain research area.Comment: 11 pages, 11 figure

Fractographic analysis of fractured dental implant components

AbstractBackground/purposeThis study investigated in seven patients the main causes of accidental fractures of various implant components.Materials and methodsWe used a scanning electron microscope and transmission electron microscope to observe the fracture interfaces of four fixtures, six abutment screws, and nine gold screws retrieved from patients with prosthetic problems.ResultsIn all fixtures and some abutment screws, parafunctional force and a cantilever design ultimately resulted in movement of low-angle grain boundaries (LAGBs) at most fracture surfaces. Fractographic observations showed that overloading deformed the grain sizes, and the no precipitates were present on the high-angle grain boundaries (HAGBs) or matrices of some abutment screws and most gold screws.ConclusionTo avoid implant fracture, certain underlying mechanical risk factors should be noted such as patients with a habit of bruxism, bridgework with a cantilever design, or two implants installed in a line in the posterior mandible

An Immunomodulatory Protein (Ling Zhi-8) from a Ganoderma lucidum

The purpose of this study was to investigate the effect of an immunomodulatory protein (Ling Zhi-8, LZ-8) on wound healing in rat liver tissues after monopolar electrosurgery. Animals were sacrificed for evaluations at 0, 3, 7, and 28 days postoperatively. It was found that the wound with the LZ-8 treatment significantly increases wound healing. Western blot analysis clearly indicated that the expression of NF-κB was decreased at 3, 7, and 28 days when liver tissues were treated with LZ-8. Moreover, caspase-3 activity of the liver tissue also significantly decreases at 7 and 28 days, respectively. DAPI staining and TUNEL assays revealed that only a minimal dispersion of NF-κB was found on the liver tissue treated with LZ-8 at day 7 as compared with day 3 and tissues without LZ-8 treatment. Similarly, apoptosis was decreased on liver tissues treated with LZ-8 at 7 days when compared to the control (monopolar electrosurgery) tissues. Therefore, the analytical results demonstrated that LZ-8 induced acceleration of wound healing in rat liver tissues after monopolar electrosurgery

Nanoparticles prepared from the water extract of Gusuibu (Drynaria fortunei J. Sm.) protects osteoblasts against insults and promotes cell maturation

Our previous study showed that Gusuibu (Drynaria fortunei J. Sm.) can stimulate osteoblast maturation. This study was further designed to evaluate the effects of nanoparticles prepared from the water extract of Gusuibu (WEG) on osteoblast survival and maturation. Primary osteoblasts were exposed to 1, 10, 100, and 1000 μg/mL nanoparticles of WEG (nWEG) for 24, 48, and 72 hours did not affect morphologies, viability, or apoptosis of osteoblasts. In comparison, treatment of osteoblasts with 1000 μg/mL WEG for 72 hours decreased cell viability and induced DNA fragmentation and cell apoptosis. nWEG had better antioxidant bioactivity in protecting osteoblasts from oxidative and nitrosative stress-induced apoptosis than WEG. In addition, nWEG stimulated greater osteoblast maturation than did WEG. Therefore, this study shows that WEG nanoparticles are safer to primary osteoblasts than are normal-sized products, and may promote better bone healing by protecting osteoblasts from apoptotic insults, and by promoting osteogenic maturation