Functional MRI is a noninvasive method in brain imaging. Localization, classification, prediction and connectivity are the most common issues. Functional connectivity is a branch of fMRI that focuses on connectivity between voxels and ROIs. There are several methods for investigating Functional connectivity such as correlation analysis. In any field, it is very important that results of any research have reliability according to the experiment. Any methods and measurement instruments need to be reliable. Without reliability, results are meaningless and our research is not trustworthy. Brain imaging can be used as a valuable tool for pre-surgical planning, so the results should be highly reproducible. Test-retest reliability can be explored using the intra-class correlation coefficient (ICC). I2C2 is an extent of ICC to verify the reliability in high-dimensional data as imaging studies. 13 subjects of testretest resting-state fMRI are used to investigate reliability. I2C2 of four ROIs are also computed (Caudate, Cingulate, Cuneus and Precentral regions). Functional connectivity is found to have moderate reliability ranging 0. 6244 to 0. 6941. 95% confidence interval of I2C2 is calculated by nonparametric bootstrap in which CI of Caudate region I2C2 has the shortest length.

Introduction: This study aimed to assess the effect of controlled mouth breathing during the resting state using Functional magnetic resonance imaging (fMRI). Methods: Eleven subjects participated in this experiment in which the controlled “, Nose”,and “, Mouth”,breathings of 6 s respiratory cycle were performed with a visual cue at 3T MRI. Voxel-wise seed-to-voxel maps and whole-brain region of interest (ROI)-to-ROI connectome maps were analyzed in both “, Nose>Mouth”,and “, Mouth>Nose”,contrasts. Results: As a result, there were more connection pairs in the “, Mouth”,breathing condition, i. e., 14 seeds and 14 connecting pairs in the “, Mouth>Nose”,contrast, compared to 7 seeds and 4 connecting pairs in the “, Nose>Mouth”,contrast (false discovery rate [FDR] of P<0. 05). Conclusion: The present study demonstrated that mouth breathing with controlled respiratory cycles could significantly induce alterations in Functional connectivity in the resting-state network, suggesting that it can differently affect resting brain function,in particular, the brain can hardly rest during mouth breathing, as opposed to conventional nasal breathing.

Aim: Many neuroimaging studies have been done, to illustrate neuropathology of chronic insomnia, as one of the most common psychological disorders. Recent studies, emphasis on the role of orbitofrontal cortex. But the Functional connectivity of OFC hasn’ t been studied yet. To the best of our knowledge, it is the first study to discuss about it. Methods: In a case-control study, Functional connectivity of OFC with whole brain was carried out to compare the Functional connectivity alterations of this region between forty-two chronic insomnia patients, obtained at sleep disorders research center in Kermanshah university of medical sciences, and fifty-two healthy controls without sleep problems. Additionally, Pittsburgh Sleep Quality Index and nocturnal polysomnography was taken from subjects. Results: Patients showed decreased inter-Functional connectivity of right OFC and intra Functional connectivity of right OFC with right frontal pole, right insular cortex, right and left inferior frontal gyrus, left middle temporal gyrus and right frontal operculum cortex. Additionally, the Functional connectivity of right OFC with precentral gyrus was increased in patients compared with controls (p<0. 05 corrected for multiple comparisons). Above mentioned alterations were significantly correlated with subjective sleep quality. Conclusion: Our results show Functional connectivity alterations of OFC in patients in comparison to healthy controls and these changes are correlated with subjective sleep quality. Therefore, this cortex can discuss some signs of chronic insomnia and even same neuropathology with depression.

Purpose: Deception is described as a deliberate endeavor to deceive others. In this research, the main purpose is to survey the brain network between deception and telling the truth. Materials and Methods: Electroencephalography (EEG) data were collected from 17 subjects during a deception task in which the subjects had to classify the target stimuli deceptively while responding truthfully to other stimuli (non-targets). Functional connectivity (FC) analysis was carried out in source space in order to attenuate the volume conduction effect. The coherence criterion was applied for calculating FC. Results: The results revealed that deception is associated with significantly greater connectivity between distant regions, including frontal-occipital and frontal-parietal connectivity. In addition, Anterior Cingulate Cortex (ACC) demonstrated significantly greater connectivity with regions of the frontal and occipital lobes. Besides, deception was accompanied by high number of strong connectivity between the left parietal and frontal lobes. Conclusions: The findings demonstrated that the FC studies in source space can strikingly assist in the investigation of deception.

Introduction: The human brain is a complex system consisting of connected nerve cells that adapt to and learn from the environment by changing its regional activities. The synchrony between these regional activities is called Functional network changes during life and results in the learning of new skills. Time perception and interval discrimination are among the most necessary skills for the human being to perceive motions, coordinate motor functions, speak, and perform many cognitive functions. Despite its importance, the underlying mechanism of changes in brain Functional connectivity patterns during learning time intervals still need to be well understood. Methods: This study aimed to show how electroencephalography (EEG) Functional connectivity changes are associated with learning temporal intervals. In this regard, 12 healthy volunteers were trained with an auditory time-interval discrimination task over six days while their brain activities were recorded via EEG signals during the first and the last sessions. Then, changes in regional phase synchronization were calculated using the weighted/phase lag index (WPLI) approach, the most effective EEG Functional connections at the temporal and prefrontal regions, and in the theta and beta bands frequency. In addition, the WPLI reported more accurate values. Results: The results showed that learning interval discrimination significantly changed Functional connectivity in the prefrontal and temporal regions. Conclusion: These findings could shed light on a better understanding of the brain mechanism involved in time perception.