It is suggested that structurally segregated and functionally specialized brain regions are mediated by synchrony over large-scale networks in order to provide the formation of dynamic links and integration functions. The existence of negative synchrony, or negative functional connectivity, however, has been a subject of debate in terms of its origin, interpretation, relationship with structural connectivity, and possible neurophysiological function. The present study, which incorporated 20 cognitively healthy elderly human subjects, focused on testing the hypothesis that negative functional connectivity significantly correlates with the shortest path length in the human brain network. Our theoretical calculation, simulated data and human study results support this hypothesis. In the human study, we find that (1) the percentage of negative functional connectivity connections among all connections between brain regions significantly correlates with spatial Euclidian distance; (2) the strength of the negative functional connectivity between the right amygdala and the left dorsolateral prefrontal cortex is significantly correlated with the shortest path length across the 20 human subjects; (3) such a significant relationship between the negative functional connectivity and shortest path length exists in all the negative functional connectivity connections in the whole brain; and (4) the correlations between the negative functional connectivity and shortest path length also are frequency bandwidth dependent. These results suggest that an accumulated phased delay gives rise to the negative functional connectivity, along the shortest path in the large-scale brain functional network. It is suggested that our study can be extended to examine a variety of neurological diseases and psychiatric disorders by measuring the changes of shortest path length and functional reorganization in the brain.

The white matter of the brain consists of fiber tracts that connect different regions of the brain. Among these tracts, the intrahemispheric cortico-cortical connections are called association fibers. The U-fibers are short association fibers that connect adjacent gyri. These fibers were thought to work as part of the cortico-cortical networks to execute associative brain functions. However, their anatomy and functions have not been documented in detail for the human brain. In past studies, U-fibers have been characterized in the human brain with diffusion tensor imaging (DTI). However, the validity of such findings remains unclear. In this study, DTI of the macaque brain was performed, and the anatomy of U-fibers was compared with that of the human brain reported in a previous study. The macaque brain was chosen because it is the most commonly used animal model for exploring cognitive functions and the U-fibers of the macaque brain have been already identified by axonal tracing studies, which makes it an ideal system for confirming the DTI findings. Ten U-fibers found in the macaque brain were also identified in the human brain, with a similar organization and topology. The delineation of these species-conserved white matter structures may provide new options for understanding brain anatomy and function.

The image contrast in magnetic resonance imaging (MRI) is highly sensitive to several mechanisms that are modulated by the properties of the tissue environment. The degree and type of contrast weighting may be viewed as image filters that accentuate specific tissue properties. Maps of quantitative measures of these mechanisms, akin to microstructural/environmental-specific tissue stains, may be generated to characterize the MRI and physiological properties of biological tissues. In this paper, three quantitative MRI (qMRI) methods for characterizing white matter microstructural properties are reviewed. All of these measures measure complementary aspects of how water interacts with the tissue environment. Diffusion MRI including diffusion tensor imaging characterizes the diffusion of water in the tissues and is sensitive to the microstructural density, spacing and orientational organization of tissue membranes including myelin. Magnetization transfer imaging characterizes the amount and degree of magnetization exchange between free water and macromolecules like proteins found in the myelin bilayers. Relaxometery measures the MRI relaxation constants T1 and T2, which in white matter has a component associated with the water trapped in the myelin bilayers. The conduction of signals between distant brain regions occurs primarily through myelinated white matter tracts, thus these methods are potential indicators of pathology and structural connectivity in the brain. This paper provides an overview of the qMRI stain mechanisms, acquisition and analysis strategies, and applications for these qMRI stains.

Resting-State Functional Magnetic Resonance Imaging (RS-FMRI) holds the promise of revealing brain functional connectivity without requiring specific tasks targeting particular brain systems. RS-FMRI is being used to find differences between populations even when a specific candidate target for traditional inferences is lacking. However, the problem with RS-FMRI is a lacking definition of what constitutes noise and signal. RS-FMRI is easy to acquire but not to analyze or draw inferences from. In this commentary we discuss a problem that is still treated lightly despite its significant impact on RS-FMRI inferences: Global Signal Regression (GSReg) – the practice of projecting out signal averaged over the entire brain – can change resting state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness. Although Murphy et al. in 2009 demonstrated GSReg negatively biases correlations, the approach remains in wide use. We revisit this issue to argue the problem with GSReg is more than negative bias or the interpretability of negative correlations. Its usage can fundamentally alter inter-regional correlations within a group, or their differences between groups. We used an illustrative model to clearly convey our objections and derived equations formalizing our conclusions. We hope this creates a clear context in which counterarguments can be made. We conclude that GSReg should not be used when studying RS-FMRI because GSReg biases correlations differently in different regions depending on the underlying true inter-regional correlation structure. GSReg can alter local and long-range correlations, potentially spreading underlying group differences to regions that may never have had any. Conclusions also apply to substitutions of GSReg for denoising with decompositions of signals aggregated over the network’s regions to the extent they cannot separate signals of interest from noise. We touch on the need for careful accounting of nuisance parameters when making group comparisons of correlation maps.

Structural connectivity models hold great promise for expanding what is known about the ways information travels throughout the brain. The physiologic interpretability of structural connectivity models depends heavily on how the connections between regions are quantified. This paper presents an integrated structural connectivity framework designed around such an interpretation. The framework provides three measures to characterize the structural connectivity of a subject: 1) The structural connectivity matrix describing the proportion of connections between pairs of nodes, 2) The nodal connection distribution characterizing the proportion of connections that terminate in each node and 3) the connection density image which presents the density of connections as they traverse through white matter. Individually each possess different information concerning the structural connectivity of the individual and could potentially be useful for a variety of tasks, ranging from characterizing and localizing group differences, to identifying novel parcellations of the cortex. The efficiency of the proposed framework allows the determination of large structural connectivity networks, consisting of many small nodal regions, providing a more detailed description of a subject’s connectivity. The nodal connection distribution provides a grey matter contrast that can potentially aid in investigating local cytoarchitecture and connectivity. Similarly the connectivity density images offer insight into the white matter pathways, potentially identifying focal differences that affect a number of pathways. The reliability of these measures was established through a test/retest paradigm performed on 9 subjects while the utility of the method was evaluated through its applications to 20 diffusion datasets acquired from typically developing adolescents.

Recently, carriers of a common variant in the autism risk gene, CNTNAP2, were found to have altered functional brain connectivity using functional MRI. Here we scanned 328 young adults with high-field (4-Tesla) diffusion imaging, to test the hypothesis that carriers of this gene variant would have altered structural brain connectivity. All participants (209 females, 119 males, age: 23.4 +/−2.17 SD years) were scanned with 105-gradient high angular diffusion imaging (HARDI) at 4 Tesla. After performing a whole-brain fiber tractography using the full angular resolution of the diffusion scans, 70 cortical surface-based regions of interest were created from each individual’s co-registered anatomical data to compute graph metrics for all pairs of cortical regions. In graph theory analyses, subjects homozygous for the risk allele (CC) had lower characteristic path length, greater small-worldness and global efficiency in whole brain analyses, as well as greater eccentricity (maximum path length) in 60 of 70 nodes in regional analyses. These results were not reducible to differences in more commonly studied traits such as fiber density or fractional anisotropy. This is the first study to link graph theory metrics of brain structural connectivity to a common genetic variant linked with autism and will help us understand the neurobiology of circuits implicated in risk for autism.

Re-entrant circuits involving communication between frontal cortex and other brain areas have been hypothesized to be necessary for maintaining the sustained patterns of neural activity that represent information in working memory, but evidence has so far been indirect. If working memory maintenance indeed depends on such temporally precise and robust long-distance communication, then performance on a delayed recognition task should be highly dependent on the microstructural integrity of white matter tracts connecting sensory areas with prefrontal cortex. Here the effect of variations in white matter microstructure on working memory performance was explored in two separate groups of subjects: individuals with multiple sclerosis (MS) and age- and gender-matched healthy adults. We used functional magnetic resonance imaging to reveal cortical regions involved in spatial and object working memory, which were used to define specific frontal to extrastriate white matter tracts of interest via diffusion tensor tractography. After factoring out variance due to age and the microstructure of a control tract, the corticospinal tract, it was found that the number of errors produced in the object working memory task was specifically related to the microstructure of the inferior frontal-occipital fasciculus. This result held for both subject groups, independently, providing a within-study replication with two different types of white matter structural variability: MS-related damage and normal variation. The results demonstrate the importance of interactions between specific regions of the prefrontal cortex and sensory cortices for a nonspatial working memory task that preferentially activates those regions.

Whole brain functional connectivity MRI (fcMRI) requires acquisition of a time course of gradient-recalled (GR) volumetric images. A method is developed to accelerate this acquisition using GR echo-planar imaging (GR-EPI) and RF slice phase tagging. For N-fold acceleration, a tailored RF pulse excites N slices using a uniform-field transmit coil. This pulse is the Fourier transform of the profile for the N slices with a predetermined RF phase tag on each slice. A multichannel RF receive coil is used for detection. For n slices, there are n/N groups of slices. Signal-averaged reference images are created for each slice within each slice group for each member of the coil array and used to separate overlapping images that are simultaneously received. The time-overhead for collection of reference images is small relative to the acquisition time of a complete volumetric time course. A least-square singular value decomposition method allows image separation on a pixel-by-pixel basis. Two-fold slice acceleration is demonstrated using an eight-channel RF receive coil, with application to resting-state functional MRI in the human brain. Data from six subjects at 3 T are reported. The method has been extended to half k-space acquisition, which not only provides additional acceleration, but also facilitates slice separation because of increased signal intensity of the central lines of k-space coupled with reduced susceptibility effects.

Functional imaging studies have shown reduced activity within the default mode network during attention-demanding tasks. The network circuitry underlying this suppression remains unclear. Proposed hypotheses include an attentional “switch” in the right anterior insula and reciprocal inhibition between the default mode and attention control networks. We analyzed resting state BOLD data from 1278 subjects from 26 sites and constructed whole brain maps of functional connectivity between 7266 ROIs covering the gray matter at approximately 5 mm resolution. ROIs belonging to the default mode network and attention control network were identified based on correlation to six published seed locations. Spatial heterogeneity of correlation between the default mode and attention control networks was observed, with smoothly varying gradients in every hub of both networks that ranged smoothly from weakly but significantly anticorrelated to positively correlated. Such gradients were reproduced in 3 separate groups of subjects. Anticorrelated subregions were identified in major hubs of both networks. Between-network connectivity gradients strengthen with age during late adolescence and early adulthood, with associated sharpening of the boundaries of the default mode network, integration of the insula and cingulate with frontoparietal attentional regions, and decreasing correlation between the default mode and attention control networks with age.

A multivariate source-based morphometry (SBM) method for processing fractional anisotropy (FA) data is presented. SBM utilizes independent component analysis (ICA) and decomposes an FA image into spatial maps and loading coefficients. The loading coefficients represent the relative degree each component contributes to a given subject’s FA map. We hypothesized that SBM analysis on a large dataset of age- and gender-matched patients with schizophrenia (n = 65, ages 18 to 60 years) and healthy controls (n =102, ages 18 to 60 years) would show a similar, specific pattern of frontal and temporal group differences as a recent VBM meta-analysis. Two approaches using a) the loading coefficients obtained from the ICA analysis, and alternatively b) the weighted mean FA values obtained from the ICA defined clusters were compared for group analysis.

Six of the ten selected components had significant group differences with the loading coefficients. Each component was composed of several white matter tracts distributed throughout the brain. Nine of the ten non-artifactual components had significant group differences with the weighted mean FA values. The weighted mean FA values for each ICA spatial map generally had larger effects sizes relative to the loading coefficients. These networks were consistent with regions identified in previous voxel-based studies of schizophrenia. SBM identified several components that covered disjoint brain regions and multiple white matter tracts that would not have been possible with previous voxel-based univariate techniques. Overall, these results suggest the importance of utilizing multivariate approaches in morphometric studies in schizophrenia.

In the decade and a half since Biswal’s fortuitous discovery of spontaneous correlations in functional imaging data, the field of functional connectivity (FC) has seen exponential growth resulting in the identification of widely-replicated intrinsic networks and the innovation of novel analytic methods with the promise of diagnostic application. As such a young field undergoing rapid change, we have yet to converge upon a desired and needed set of standards. In this issue, Habeck and Moeller begin a dialogue for developing best practices by providing four criticisms with respect to FC estimation methods, interpretation of FC networks, assessment of FC network features in classifying sub-populations, and network visualization. Here, we respond to Habeck and Moeller and provide our own perspective on the concerns raised in the hope that the neuroimaging field will benefit from this discussion.