Effect of spinal anesthesia-induced deafferentation on pain processing in healthy male volunteers: A task-related fMRI study

Tramadol Effects on Brain Activity During Cognitive and Emotional Empathy for Pain: A Randomized Controlled Study

Pain is perceived not only by personal experience but also vicariously. Pain empathy is the ability to share and understand other's intentions and emotions in their painful conditions, which can be divided into cognitive and emotional empathy. It remains unclear how centrally acting analgesics would modulate brain activity related to pain empathy and which component of pain empathy would be altered by analgesics. In this study, we examined the effects of the analgesic tramadol on the brain activity for pain empathy in healthy adults. We used 2 tasks to assess brain activity for pain empathy. In experiment 1, we used a well-established picture-based pain empathy task involving passive observation of other's pain. In experiment 2, we developed a novel pain empathy task to assess brain activity during cognitive and emotional empathy for pain separately in a single task. We conducted a double-blind, placebo-controlled within-subject crossover study with functional magnetic resonance imaging for 33 participants in experiment 1 and 31 participants in experiment 2, respectively. In experiment 1, we found that tramadol decreased activation in the supramarginal gyrus during observation of other's pain compared with placebo. Supramarginal gyrus activation correlated negatively with the thermal pain threshold. In experiment 2, we found that tramadol decreased activation in angular gyrus in cognitive empathy for pain compared with placebo but did not change brain activity in emotional empathy for pain. PERSPECTIVE: Centrally acting analgesics such as tramadol may have not only analgesic effects on self-experienced pain but also on the complex neural processing of pain empathy.

Differential Effect of Global Signal Regression Between Awake and Anesthetized Conditions in Mice

Introduction: In resting-state functional magnetic resonance imaging (rs-fMRI) studies, global signal regression (GSR) is a controversial preprocessing strategy. It effectively eliminates global noise driven by motion and respiration but also can introduce artifacts and remove functionally relevant metabolic information. Most preclinical rs-fMRI studies are performed in anesthetized animals, and anesthesia will alter both metabolic and neuronal activity. Methods: In this study, we explored the effect of GSR on rs-fMRI data collected under anesthetized and awake state in mice (n = 12). We measured global signal amplitude, and also functional connectivity (FC), functional connectivity density (FCD) maps, and brain modularity, all commonly used data-driven analysis methods to quantify connectivity patterns. Results: We found that global signal amplitude was similar between the awake and anesthetized states. However, GSR had a different impact on connectivity networks and brain modularity changes between states. We demonstrated that GSR had a more prominent impact on the anesthetized state, with a greater decrease in functional connectivity and increased brain modularity. We classified mice using the change in amplitude of brain modularity coefficient (ΔQ) before and after GSR processing. The results revealed that, when compared with the largest ΔQ group, the smallest ΔQ group had increased FCD in the cortex region in both the awake and anesthetized states. This suggests differences in individual mice may affect how GSR differentially affects awake versus anesthetized functional connectivity. Discussion: This study suggests that, for rs-fMRI studies which compare different physiological states, researchers should use GSR processing with caution.

Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN

Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scale, collaborative studies. In this paper, we describe how CBRAIN's unique features and infrastructure were leveraged to integrate TAPAS PhysIO, an open-source MATLAB toolbox for physiological noise modeling in fMRI data. This case study highlights three key elements of CBRAIN's infrastructure that enable streamlined, multimodal tool integration: a user-friendly GUI, a Brain Imaging Data Structure (BIDS) data-entry schema, and convenient in-browser visualization of results. By incorporating PhysIO into CBRAIN, we achieved significant improvements in the speed, ease of use, and scalability of physiological preprocessing. Researchers now have access to a uniform and intuitive interface for analyzing data, which facilitates remote and collaborative evaluation of results. With these improvements, CBRAIN aims to become an essential open-science tool for integrative neuroimaging research, supporting FAIR principles and enabling efficient workflows for complex analysis pipelines.