WeChat Mini Program
Old Version Features

Disruption of Periaqueductal Gray-default Mode Network Functional Connectivity in Patients with Crohn's Disease with Abdominal Pain

Neuroscience(2023)

Xi An Jiao Tong Univ

Cited 5|Views30
Abstract
Abdominal pain in Crohn's disease (CD) has been known to be associated with changes in the central nervous system. The periaqueductal gray (PAG) plays a well-established role in pain processing. However, the role of PAG-related network and the effect of pain on the network in CD remain unclear.Resting-state functional magnetic imaging (fMRI) data were collected from 24 CD patients in remission with abdominal pain, 24 CD patients without abdominal pain and 28 healthy controls (HCs). Using the subregions of PAG (dorsomedial (dmPAG), dorsolateral (dlPAG), lateral (lPAG) and ventrolateral (vlPAG)) as seeds, the seed-based FC maps were calculated and one-way analysis of variance (ANOVA) was performed to investigate the differences among the three groups.Results showed that the group differences were mainly involved in the FC of the vlPAG with the precuneus, medial prefrontal cortex (mPFC) as well as orbitofrontal cortex (OFC), and the FC of the right lateral PAG (lPAG) with the precuneus, inferior parietal lobule (IPL), angular gyrus and premotor cortex. The FC values of all these regions decreased successively in the order of HCs, CD without abdominal pain and CD with abdominal pain. The pain score was negatively correlated with the FC of the l/vlPAG with the precuneus, angular gyrus and mPFC in CD patients with abdominal pain.This study implicated the disrupt communication between the PAG and the default mode network (DMN). These findings complemented neuroimaging evidence for the pathophysiology of visceral pain in CD patients.
More
Translated text
Key words
Crohn's disease,pain,periaqueductal gray,functional connectivity,magnetic resonance imaging
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined