In brain disorders, both the brain structural and Functional connectivity are altered and cause different behavioral symptoms. Recognizing these variations can help us to diagnose, treat, and control its progression. Schizophrenia is one of these mental disorders that widely affects the brain structure and function. Investigation of brain variations in this disease has commonly been based on voxel-wise analysis or region-based studies. The aim of this study is to evaluate brain structure and function alterations in schizophrenia patients comparing to healthy control from the brain connectivity perspective. For this purpose, using the statistical test method, a comparison was made between all the structural and Functional connections in the brain of 92 healthy individuals and 37 schizophrenia patients obtained from diffusion tensor imaging (DTI) and Functional Magnetic Resonance Imaging (fMRI) respectively. The findings of this study indicate that the number of altered edges in the brain Functional network of patients is about 4 times more than the number of varied structural connections, which indicates the high impact of this disorder on brain function. Also, examination of the number of altered edges connected to each node, the affected areas in this disease were identified and it was shown that the schizophrenia patients’ brain has changed in parts of the brain subnetworks related to the default mode network (DMN), attention, somatomotor and vision networks. It was also shown that the altered brain structural connections of patients are involved in the areas of the superior frontal gyrus, temporal gyrus and part of the occipital cortex which are mostly shown relative increasing of the structural connectivity weights. The results of this study indicate the widespread effect of this disorder on the brain and suggest that the occurrence of some abnormal behaviors in schizophrenia patients may be due to some increased structural connectivity weights.

Brain as the most complex organ in the human body has been investigated from various aspects. The greatest origin of this complexity is due to the fact that, despite the fixed architecture of brain structure (physical connections), the Functional connectivity is in a constantly changing state, resulting to different behaviors. In many mental diseases, both brain structural and Functional connectivities and their relationship are changed and cause different symptoms. Investigation of brain connectivity variations in the disease may help to better understanding of the relationship between brain structure and function. One of the most severe and debilitating brain disorders is Schizophrenia in which both brain structure and function are involved. Among all available methods, multimodal analysis of data has been recently gained great interest to provide the capability of extracting association between separate neuroimaging data. However, due to their voxel based viewpoint, relationship between brain connectivities cannot be inferred. In this study, the joint independent component analysis (jICA) has been proposed to investigate the relationship between brain Functional and structural connectivity. We applied the suggested approach to combine Functional and structural connectivity, in order to assess abnormalities underlying schizophrenic patients relative to healthy people. The findings suggest that the correspondence between brain function and structure is not necessarily one-to-one. The results also indicated that variations in several structural fibers, such as superior longitudinal fasciculus and inferior longitudinal fasciculus, are associated with Functional changes in the temporal and frontal lobes. Besides, analyzing the nodal strength and shortest path length in the obtained subnetworks demonstrates that the Functional subnetworks efficiency in parallel information transfer in schizophrenic patients is reduced. Overall, the outcomes point out the capability of the proposed method to better understanding of brain Functional and structural connectivity association and its variations in brain disorders.

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.