scholarly journals Functional Connectivity Analysis of NIRS Data under Rubber Hand Illusion to Find a Biomarker of Sense of Ownership

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Naoki Arizono ◽  
Yuji Ohmura ◽  
Shiro Yano ◽  
Toshiyuki Kondo

The self-identification, which is called sense of ownership, has been researched through methodology of rubber hand illusion (RHI) because of its simple setup. Although studies with neuroimaging technique, such as fMRI, revealed that several brain areas are associated with the sense of ownership, near-infrared spectroscopy (NIRS) has not yet been utilized. Here we introduced an automated setup to induce RHI, measured the brain activity during the RHI with NIRS, and analyzed the functional connectivity so as to understand dynamical brain relationship regarding the sense of ownership. The connectivity was evaluated by multivariate Granger causality. In this experiment, the peaks of oxy-Hb on right frontal and right motor related areas during the illusion were significantly higher compared with those during the nonillusion. Furthermore, by analyzing the NIRS recordings, we found a reliable connectivity from the frontal to the motor related areas during the illusion. This finding suggests that frontal cortex and motor related areas communicate with each other when the sense of ownership is induced. The result suggests that the sense of ownership is related to neural mechanism underlying human motor control, and it would be determining whether motor learning (i.e., neural plasticity) will occur. Thus RHI with the functional connectivity analysis will become an appropriate biomarker for neurorehabilitation.

2020 ◽  
pp. rapm-2020-102088
Author(s):  
Yue Zhang ◽  
Yiting Huang ◽  
Hui Li ◽  
Zhaoxian Yan ◽  
Ying Zhang ◽  
...  

BackgroundDysfunction of the thalamocortical connectivity network is thought to underlie the pathophysiology of the migraine. This current study aimed to explore the thalamocortical connectivity changes during 4 weeks of continuous transcutaneous vagus nerve stimulation (taVNS) treatment on migraine patients.Methods70 migraine patients were recruited and randomized in an equal ratio to receive real taVNS or sham taVNS treatments for 4 weeks. Resting-state functional MRI was collected before and after treatment. The thalamus was parceled into functional regions of interest (ROIs) on the basis of six priori-defined cortical ROIs covering the entire cortex. Seed-based functional connectivity analysis between each thalamic subregion and the whole brain was further compared across groups after treatment.ResultsOf the 59 patients that finished the study, those in the taVNS group had significantly reduced number of migraine days, pain intensity and migraine attack times after 4 weeks of treatment compared with the sham taVNS. Functional connectivity analysis revealed that taVNS can increase the connectivity between the motor-related thalamus subregion and anterior cingulate cortex/medial prefrontal cortex, and decrease the connectivity between occipital cortex-related thalamus subregion and postcentral gyrus/precuneus.ConclusionOur findings suggest that taVNS can relieve the symptoms of headache as well as modulate the thalamocortical circuits in migraine patients. The results provide insights into the neural mechanism of taVNS and reveal potential therapeutic targets for migraine patients.


2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jingping Xu ◽  
Xiangyu Liu ◽  
Jinrui Zhang ◽  
Zhen Li ◽  
Xindi Wang ◽  
...  

Functional near-infrared spectroscopy (fNIRS), a promising noninvasive imaging technique, has recently become an increasingly popular tool in resting-state brain functional connectivity (FC) studies. However, the corresponding software packages for FC analysis are still lacking. To facilitate fNIRS-based human functional connectome studies, we developed a MATLAB software package called “functional connectivity analysis tool for near-infrared spectroscopy data” (FC-NIRS). This package includes the main functions of fNIRS data preprocessing, quality control, FC calculation, and network analysis. Because this software has a friendly graphical user interface (GUI), FC-NIRS allows researchers to perform data analysis in an easy, flexible, and quick way. Furthermore, FC-NIRS can accomplish batch processing during data processing and analysis, thereby greatly reducing the time cost of addressing a large number of datasets. Extensive experimental results using real human brain imaging confirm the viability of the toolbox. This novel toolbox is expected to substantially facilitate fNIRS-data-based human functional connectome studies.


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