Neuroimaging has enhanced our understanding of the neural correlates of pain. Yet, how neural circuits interact and contribute to persistent pain remain largely unknown. Here, we investigate the mesoscale organization of the brain through intrinsic functional communities generated from resting state functional MRI data from two independent datasets, a discovery cohort of 43 Fibromyalgia (FM) patients and 20 healthy controls (HC) as well as a replication sample of 34 FM patients and 21 HC. Using normalized mutual information, we found that the global network architecture in chronic pain patients is less stable (more variable). Subsequent analyses of node community assignment revealed the composition of the communities differed between FM and HC. Furthermore, differences in network organization were associated with the changes in the composition of communities between patients with varying levels of clinical pain. Together, this work demonstrates that intrinsic network communities differ substantially between patients with FM and controls. These differences may represent a novel aspect of the pathophysiology of chronic nociplastic pain.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.neuroimage.2020.117504 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Bioinspired Antiswelling Hydrogel Sensors with High Strength and Rapid Self-Recovery for Underwater Information Transmission.
ACS Appl Mater Interfaces
January 2025
School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
Hydrogel-based sensors typically demonstrate conspicuous swelling behavior in aqueous environments, which can severely compromise the mechanical integrity and distort sensing signals, thereby considerably constraining their widespread applicability. Drawing inspiration from the multilevel heterogeneous structures in biological tissues, an antiswelling hydrogel sensor endowed with high strength, rapid self-recovery, and low swelling ratio was fabricated through a water-induced phase separation and coordination cross-linking strategy. A dense heterogeneous architecture was developed by the integration of "rigid" quadridentate carboxyl-Zr coordination bonds and "soft" hydrophobic unit-rich regions featuring π-π stacking and cation-π interactions into the hydrogels.
View Article and Find Full Text PDFpLM4CPPs: Protein Language Model-Based Predictor for Cell Penetrating Peptides.
J Chem Inf Model
January 2025
Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas 66506, United States.
Cell-penetrating peptides (CPPs) are short peptides capable of penetrating cell membranes, making them valuable for drug delivery and intracellular targeting. Accurate prediction of CPPs can streamline experimental validation in the lab. This study aims to assess pretrained protein language models (pLMs) for their effectiveness in representing CPPs and develop a reliable model for CPP classification.
View Article and Find Full Text PDFFrontopolar Cortex Interacts With Dorsolateral Prefrontal Cortex to Causally Guide Metacognition.
Hum Brain Mapp
February 2025
Department of Psychology, Ludwig Maximilian University Munich, Munich, Germany.
Accurate metacognitive judgments about an individual's performance in a mental task require the brain to have access to representations of the quality and difficulty of first-order cognitive processes. However, little is known about how accurate metacognitive judgments are implemented in the brain. Here, we combine brain stimulation with functional neuroimaging to determine the neural and psychological mechanisms underlying the frontopolar cortex's (FPC) role in metacognition.
View Article and Find Full Text PDFThe design space of E(3)-equivariant atom-centred interatomic potentials.
Nat Mach Intell
January 2025
Engineering Laboratory, University of Cambridge, Cambridge, UK.
Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time.
View Article and Find Full Text PDFAnimals survive in dynamic environments changing at arbitrary timescales, but such data distribution shifts are a challenge to neural networks. To adapt to change, neural systems may change a large number of parameters, which is a slow process involving forgetting past information. In contrast, animals leverage distribution changes to segment their stream of experience into tasks and associate them with internal task abstracts.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!