Purpose: Schizophrenia (SZ), which affects 0.45% of adults worldwide, is a complex mental illness with unknown causes and mechanisms. Neuroimaging techniques have been used to study changes in the brain of patients with SZ. In this study, we aim to construct brain subnetworks, analyze the association of structure with function, and investigate them with graph measures. We hope to identify important subnetworks and graph measures for SZ diagnosis. Materials and Methods: This study investigates the structural and functional brain connectivity of 27 healthy controls (HC) and 27 patients with SZ. Independent component analysis (ICA) and joint ICA (jICA) are used to construct subnetworks based on functional and structural connectivity. An association between structural and functional connectivity is examined. Joint functional and structural subnetworks are also examined and compared with independent analysis of functional and structural subnetworks. Several graph measures are used in the whole brain and its subnetworks. Results: In this study, we investigated brain connectivity in HC and SZ patients using graph measures. The study analyzed both the whole brain and brain subnetworks to better understand the importance of partitioning the brain into subregions. Our results suggest that analyzing whole brain may not be the most effective method for studying brain peculiarities of SZ patients. In addition, multimodal brain analysis has proven to be effective in understanding SZ. There is no one-to-one relationship between structural and functional connectivity in the brain. Certain measures such as maximum modularity, clustering coefficient, network strength, global efficiency and path length were important in distinguishing patients with SZ from HCs in specific subnetworks. This study recommends further investigation of specific subnetworks that overlap with default mode, visual, and somatomotor resting state networks. Conclusion: This study emphasizes importance of subnetwork and multimodal analysis for understanding SZ disease.

Introduction: Autism is a heterogeneous neurodevelopmental disorder associated with social, cognitive and behavioral impairments. These impairments are often reported along with alteration of the brain structure such as abnormal changes in the grey matter (GM) density. However, it is not yet clear whether these changes could be used to differentiate various subtypes of autism spectrum disorder (ASD). Method: We compared the regional changes of GM density in ASD, Asperger's Syndrome (AS) individuals and a group of healthy controls (HC). In addition to regional changes itself, the amount of GM density changes in one region as compared to other brain regions was also calculated. We hypothesized that this structural covariance network could differentiate the AS individuals from the ASD and HC groups. Therefore, statistical analysis was performed on the MRI data of 70 male subjects including 26 ASD (age=14-50, IQ=92-132), 16 AS (age=7-58, IQ=93-133) and 28 HC (age=9-39, IQ=95-144). Result: The one-way ANOVA on the GM density of 116 anatomically separated regions showed significant differences among the groups. The pattern of structural covariance network indicated that covariation of GM density between the brain regions is altered in ASD. Conclusion: This changed structural covariance could be considered as a reason for less efficient segregation and integration of information in the brain that could lead to cognitive dysfunctions in autism. We hope these findings could improve our understanding about the pathobiology of autism and may pave the way towards a more effective intervention paradigm.

Structural analysis of remotely sensed data provides a method of assessing the structural significance of regional metallogenesis in the Dehaj area as the northwestern part of the Kerman porphyry Cu belt. This belt is consisted of dominant Eocene volcanics and the Dehaj type subvolcanic intrusives. In the study area, geologically, Cu-mineralization is hosted by the Kuh-e-Panj type subvolcanic intrusives. Photogeological analysis of the Landsat imagery reveals a pattern of mainly NW-SE oriented linear structures which were apparently generated in response to crustal thickening and lineament reactivations during the generation of a huge stratovolcanoes. A comparison of lineament map generated from Landsat ETM+ image indicates that the locations of some of the deposits, magmatic and hydrothermal centers are at/or close to the intersections of linear structures. This study deals also with the irrefutable genetic links between some small circular features and copper mineralization that has not been previously examined. It is proposed that the circular features are superficial expressions of intruded stocks or bodies in subvolcanic levels without remarkable volcanic equivalents. Of particular matter in this framework is the possible genetic/age relationship between the linear structures and the circular features. These small circular features are thought, in some cases, as having been formed due to development of the local extensional points near the intersection of the linear structures in a regional, tectonically, compressive environment. Regionally, plotting the residual anomaly and bouguer anomaly maps of the region provides, geophysically, a lucid explanation as to how the small circles came into existence.