Ongoing debate exists within the resting-state functional MRI (fMRI) literature over how intrinsic connectivity is altered in the autistic brain, with reports of general over-connectivity, under-connectivity, and/or a combination of both. Classifying autism using brain connectivity is complicated by the heterogeneous nature of the condition, allowing for the possibility of widely variable connectivity patterns among individuals with the disorder. Further differences in reported results may be attributable to the age and sex of participants included, designs of the resting-state scan, and to the analysis technique used to evaluate the data. This review systematically examines the resting-state fMRI autism literature to date and compares studies in an attempt to draw overall conclusions that are presently challenging. We also propose future direction for rs-fMRI use to categorize individuals with autism spectrum disorder, serve as a possible diagnostic tool, and best utilize data-sharing initiatives.

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The origin, structure, and function of the claustrum, as well as its role in neural computation, have remained a mystery since its discovery in the 17th century. Assessing the in vivo connectivity of the claustrum may bring forth useful insights with relevance to models the overall functionality of the claustrum itself. Using structural and diffusion tensor neuroimaging in N=100 healthy subjects, we found that the claustrum has the highest connectivity in the brain by regional volume. Network theoretical analyses revealed that a) the claustrum is a primary contributor to global brain network architecture, and that b) significant connectivity dependencies exist between the claustrum, frontal lobe, and cingulate regions. These results illustrate that the claustrum is ideally located within the human CNS connectome to serve as the putative “gate keeper” of neural information for consciousness awareness. Our findings support and underscore prior theoretical contributions about the involvement of the claustrum in higher cognitive function and its relevance in devastating neurological disease.

BACKGROUND & PURPOSE Precision Medicine is an approach to disease diagnosis, treatment and prevention which relies on quantitative biomarkers that minimize the variability of individual patient measurements. The aim of this study is to assess the inter-site variability after harmonization of a high angular resolution 3T diffusion tensor imaging protocol across 13 scanners at the 11 academic medical centers participating in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) multisite study. MATERIALS AND METHODS Diffusion MRI was acquired from a novel isotropic diffusion phantom developed at the National Institute of Standards and Technology (NIST) and from the brain of a traveling volunteer on thirteen 3T MR scanners representing three major vendors (General Electric, Philips and Siemens). Means of the DTI parameters and their coefficients of variation (CoVs) across scanners were calculated for each DTI metric and white matter tract. RESULTS For the NIST diffusion phantom, the CoV of apparent diffusion coefficient (ADC) across the 13 scanners was < 3.8% for a range of diffusivities from 0.4 to1.1×10−6 mm2/s. For the volunteer, the CoVs across scanners of the 4 primary DTI metrics, each averaged over the entire white matter skeleton, were all < 5%. In individual white matter tracts, large central pathways showed good reproducibility with the CoV consistently below 5%. However, smaller tracts showed more variability with the CoV of some DTI metrics reaching 10%. CONCLUSION The results suggest the feasibility of standardizing DTI across 3T scanners from different MR vendors in a large-scale neuroimaging research study.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.