Scale-free amplitude modulation of low-frequency fluctuations in episodic migraine

Patients' Use of Virtual Reality Technology for Pain Reduction during Outpatient Hysteroscopy: A Meta-analysis of Randomized Controlled Trials

OBJECTIVE

To summarize evidence from randomized controlled trials (RCTs) on the effectiveness of virtual reality technology (VRT), as used by patients, for reducing pain during outpatient hysteroscopy.

DATA SOURCES

Electronic databases and clinical registers were searched until June 21, 2023. The review protocol was registered in PROSPERO before the data extraction (CRD42023434340).

METHODS OF STUDY SELECTION

We included RCTs of patients receiving VRT compared with controls receiving routine care during outpatient hysteroscopy.

TABULATION, INTEGRATION, AND RESULTS

The primary outcome was average pain during hysteroscopy. Pooled results were expressed as mean differences (MDs) with 95% confidence interval (CI). Sources of heterogeneity were investigated through sensitivity and subgroups analysis. Five RCTs were included (435 participants). The comparison between the intervention and control groups showed a borderline difference in perceived pain during hysteroscopy (MD -0.88, 95% CI -1.77 to 0.01). Subgroup analysis based on the type of VRT (active or passive) indicated that active VRT potentially reduced the perception of pain (MD -1.42, 95% CI -2.21 to -0.62), whereas passive VRT had no effect (MD -0.06, 95% CI -1.15 to 1.03).

CONCLUSION

Patients' use of active VRT may be associated with a reduction in pain during outpatient hysteroscopy (evidence Grading of Recommendations Assessment, Development, and Evaluation 2/4). Future research should focus on conducting methodologically robust studies with larger sample sizes and more homogeneous populations.

Anti-Disturbance of Scale-Free Spiking Neural Network against Impulse Noise

The bio-brain presents robustness function to external stimulus through its self-adaptive regulation and neural information processing. Drawing from the advantages of the bio-brain to investigate the robustness function of a spiking neural network (SNN) is conducive to the advance of brain-like intelligence. However, the current brain-like model is insufficient in biological rationality. In addition, its evaluation method for anti-disturbance performance is inadequate. To explore the self-adaptive regulation performance of a brain-like model with more biological rationality under external noise, a scale-free spiking neural network(SFSNN) is constructed in this study. Then, the anti-disturbance ability of the SFSNN against impulse noise is investigated, and the anti-disturbance mechanism is further discussed. Our simulation results indicate that: (i) our SFSNN has anti-disturbance ability against impulse noise, and the high-clustering SFSNN outperforms the low-clustering SFSNN in terms of anti-disturbance performance. (ii) The neural information processing in the SFSNN under external noise is clarified, which is a dynamic chain effect of the neuron firing, the synaptic weight, and the topological characteristic. (iii) Our discussion hints that an intrinsic factor of the anti-disturbance ability is the synaptic plasticity, and the network topology is a factor that affects the anti-disturbance ability at the level of performance.

Complex spiking neural networks with synaptic time-delay based on anti-interference function

The research on a brain-like model with bio-interpretability is conductive to promoting its information processing ability in the field of artificial intelligence. Biological results show that the synaptic time-delay can improve the information processing abilities of the nervous system, which are an important factor related to the formation of brain cognitive functions. However, the synaptic plasticity with time-delay of a brain-like model still lacks bio-interpretability. In this study, combining excitatory and inhibitory synapses, we construct the complex spiking neural networks (CSNNs) with synaptic time-delay that more conforms biological characteristics, in which the topology has scale-free property and small-world property, and the nodes are represented by an Izhikevich neuron model. Then, the information processing abilities of CSNNs with different types of synaptic time-delay are comparatively evaluated based on the anti-interference function, and the mechanism of this function is discussed. Using two indicators of the anti-interference function and three kinds of noise, our simulation results consistently verify that: (i) From the perspective of anti-interference function, an CSNN with synaptic random time-delay outperforms an CSNN with synaptic fixed time-delay, which in turn outperforms an CSNN with synaptic none time-delay. The results imply that brain-like networks with more bio-interpretable synaptic time-delay have stronger information processing abilities. (ii) The synaptic plasticity is the intrinsic factor of the anti-interference function of CSNNs with different types of synaptic time-delay. (iii) The synaptic random time-delay makes an CSNN present better topological characteristics, which can improve the information processing ability of a brain-like network. It implies that synaptic time-delay is a factor that affects the anti-interference function at the level of performance.

Left to themselves: Time to target chronic pain in childhood rare diseases

BACKGROUND

Chronic pain is prevalent among patients with rare diseases (RDs). However, little is understood about how biopsychosocial mechanisms may be integrated in the unique set of clinical features and therapeutic challenges inherent in their pain conditions.

METHODS

This review presents examples of major categories of RDs with particular pain conditions. In addition, we provide translational evidence on clinical and scientific rationale for psychosocially- and neurodevelopmentally-informed treatment of pain in RD patients.

RESULTS

Neurobiological and functional overlap between various RD syndromes and pain states suggests amalgamation and mutual modulation of the respective conditions. Emotional sequelae could be construed as an emotional homologue of physical pain mediated via overlapping brain circuitry. Given their clearly defined genetic and molecular etiologies, RDs may serve as heuristic models for unraveling pathophysiological processes inherent in chronic pain.

CONCLUSIONS

Systematic evaluation of chronic pain in patients with RD contributes to sophisticated insight into both pain and their psychosocial correlates, which could transform treatment.

NIRS measures in pain and analgesia: Fundamentals, features, and function

Current pain assessment techniques based only on clinical evaluation and self-reports are not objective and may lead to inadequate treatment. Having a functional biomarker will add to the clinical fidelity, diagnosis, and perhaps improve treatment efficacy in patients. While many approaches have been deployed in pain biomarker discovery, functional near-infrared spectroscopy (fNIRS) is a technology that allows for non-invasive measurement of cortical hemodynamics. The utility of fNIRS is especially attractive given its ability to detect specific changes in the somatosensory and high-order cortices as well as its ability to measure (1) brain function similar to functional magnetic resonance imaging, (2) graded responses to noxious and innocuous stimuli, (3) analgesia, and (4) nociception under anesthesia. In this review, we evaluate the utility of fNIRS in nociception/pain with particular focus on its sensitivity and specificity, methodological advantages and limitations, and the current and potential applications in various pain conditions. Everything considered, fNIRS technology could enhance our ability to evaluate evoked and persistent pain across different age groups and clinical populations.