Autism spectrum disorders (ASDs) are characterized by deficits in social and communication processes. Recent data suggest that altered functional connectivity (FC), i.e. synchronous brain activity, might contribute to these deficits. Of specific interest is the FC integrity of the default mode network (DMN), a network active during passive resting states and cognitive processes related to social deficits seen in ASD, e.g. Theory of Mind. We investigated the role of altered FC of default mode sub-networks (DM-SNs) in 16 patients with high-functioning ASD compared to 16 matched healthy controls of short resting fMRI scans using independent component analysis (ICA). ICA is a multivariate data-driven approach that identifies temporally coherent networks, providing a natural measure of FC. Results show that compared to controls, patients showed decreased FC between the precuneus and medial prefrontal cortex/anterior cingulate cortex, DMN core areas, and other DM-SNs areas. FC magnitude in these regions inversely correlated with the severity of patients' social and communication deficits as measured by the Autism Diagnostic Observational Schedule and the Social Responsiveness Scale. Importantly, supplemental analyses suggest that these results were independent of treatment status. These results support the hypothesis that DM-SNs under-connectivity contributes to the core deficits seen in ASD. Moreover, these data provide further support for the use of data-driven analysis with resting-state data for illuminating neural systems that differ between groups. This approach seems especially well suited for populations where compliance with and performance of active tasks might be a challenge, as it requires minimal cooperation.

Diffusion tensor imaging (DTI) and resting state temporal correlations (RSTC) are two leading techniques for investigating the connectivity of the human brain. They have been widely used to investigate the strength of anatomical and functional connections between distant brain regions in healthy subjects, and in clinical populations. Though they are both based on magnetic resonance imaging (MRI) they have not yet been compared directly. In this work both techniques were employed to create global connectivity matrices covering the whole brain gray matter. This allowed for direct comparisons between functional connectivity measured by RSTC with anatomical connectivity quantified using DTI tractography. We found that connectivity matrices obtained using both techniques showed significant agreement. Connectivity maps created for a priori defined anatomical regions showed significant correlation, and furthermore agreement was especially high in regions showing strong overall connectivity, such as those belonging to the default mode network. Direct comparison between functional RSTC and anatomical DTI connectivity, presented here for the first time, links two powerful approaches for investigating brain connectivity and shows their strong agreement. It provides a crucial multi-modal validation for resting state correlations as representing neuronal connectivity. The combination of both techniques presented here allows for further combining them to provide richer representation of brain connectivity both in the healthy brain and in clinical conditions.

Background Schizophrenia is hypothesized to involve disordered connectivity between brain regions. Currently, there are no direct measures of brain connectivity; functional and structural connectivity used separately provide only limited insight. Simultaneous measure of anatomical and functional connectivity and its interactions allow for better understanding of schizophrenia-related alternations in brain connectivity. Methods 27 schizophrenia patients and 27 healthy controls undergone MRI imaging using resting state fMRI and Diffusion Tensor Imaging. Separate functional and anatomical connectivity maps were calculated and combined for each subject. Global, regional and voxel measures and K-means network analysis were employed to identify group differences and correlation with clinical symptoms. Results A global connectivity analysis indicated that patients had lower anatomical connectivity and lower coherence between the two imaging modalities. In schizophrenia these group differences correlated with clinical symptom severity. While anatomical connectivity nearly uniformly decreased, functional connectivity in schizophrenia was lower for some connections (e.g. middle temporal gyrus) and higher for others (e.g. cingulate and thalamus). Within the Default Mode Network (DMN) two separate subsystems can be identified. Schizophrenia patients showed decoupling between structural and functional connectivity that can be localized to networks originating in Posterior Cingulate Cortex as well as in the Task Positive Network and one of DMN components. Conclusions Combining two measures of brain connectivity provides more comprehensive descriptions of altered brain connectivity underlying schizophrenia. Patients show deficits in white matter anatomy but functional connectivity alterations are more complex. Fusion of both methods allows identification of subsystems showing both increased and decreased functional connectivity.

Cigarette smoke contains nicotine and toxic chemicals and may cause significant neurochemical and anatomical brain changes. Voxel-based morphometry studies have examined the effects of smoking on the brain by comparing gray matter volume (GMV) in nicotine dependent individuals (NDs) to nonsmoking individuals with inconsistent results. Although sex differences in neural and behavioral features of nicotine dependence are reported, sex differences in regional GMV remain unknown. The current study examined sex differences in GMV in a large sample of 80 NDs (41 males) and 80 healthy controls (41 males) using voxel-based morphometry. Within NDs, we explored whether GMV was correlated with measures of cigarette use and nicotine dependence. High-resolution T1 structural scans were obtained from all participants. Segmentation and registration were performed in SPM8 using the optimized DARTEL approach. Covariates included age and an estimate of total global GMV. Differences were considered significant at p≤0.001, with a whole brain FWE-corrected cluster probability of p<0.025. Among NDs compared to Controls less GMV was observed in the thalamus and bilateral cerebellum and greater GMV was observed in the bilateral putamen and right parahippocampus. Lower thalamic GMV was observed in both female and male NDs compared to Controls. Female NDs also had lower GMV in the left cerebellum and in the ventral medial and orbitofrontal cortices with no areas of greater GMV. Male NDs had lower GMV in bilateral cerebellum and greater GMV in bilateral parahippocampus and left putamen. Within male NDs, GMV in the left putamen was correlated with number of pack years. This study, conducted in a large cohort, contributes to our knowledge of brain morphology in nicotine addiction and provides additional evidence of sex-specific effects on GMV in NDs. Identifying brain vulnerabilities with respect to sex provides a methodological framework for personalized therapies to improve relapse rates for both sexes.

Background-Many studies have employed voxel-based morphometry (VBM) of MRI images as an automated method of investigating cortical gray matter differences in schizophrenia. However, results from these studies vary widely, likely due to different methodological or statistical approaches.Objective-To use VBM to investigate gray matter differences in schizophrenia in a sample significantly larger than any published to date, and to increase statistical power sufficiently to reveal differences missed in smaller analyses.Methods-Magnetic resonance whole brain images were acquired from four geographic sites, all using the same model 1.5T scanner and software version, and combined to form a sample of 200 patients with both first episode and chronic schizophrenia and 200 healthy controls, matched for age, gender and scanner location. Gray matter concentration was assessed and compared using optimized VBM.Results-Compared to the healthy controls, schizophrenia patients showed significantly less gray matter concentration in multiple cortical and subcortical regions, some previously unreported. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Overall, we found lower concentrations of gray matter in regions identified in prior studies, most of which reported only subsets of the affected areas. NIH Public AccessConclusions-Gray matter differences in schizophrenia are most comprehensively elucidated using a large, diverse and representative sample.