The structural connectome has emerged as a powerful tool to characterize the network architecture of the human brain and shows great potential for generating important new biomarkers for neurologic and psychiatric disorders. The edges of the cerebral graph traverse white matter to interconnect cortical and subcortical nodes, although the anatomic embedding of these edges is generally overlooked in the literature. Mapping the paths of the connectome edges could elucidate the relative importance of individual white matter tracts to the overall network topology of the brain and also lead to a better understanding of the effect of regionally-specific white matter pathology on cognition and behavior. In this work, we introduce edge density imaging (EDI), which maps the number of network edges that pass through every white matter voxel. Test-retest analysis shows good to excellent reliability for edge density (ED) measurements, with consistent results using different cortical and subcortical parcellation schemes and different diffusion MR imaging acquisition parameters. We also demonstrate that ED yields complementary information to both traditional and emerging voxel-wise metrics of white matter microstructure and connectivity, including fractional anisotropy, track density, fiber orientation dispersion and neurite density. Our results demonstrate spatially ordered variations of ED throughout the white matter, notably including greater ED in posterior than anterior cerebral white matter. The EDI framework is employed to map the white matter regions that are enriched with pathways connecting rich club nodes and also those with high densities of intra-modular and inter-modular edges. We show that periventricular white matter has particularly high ED and high densities of rich club edges, which is significant for diseases in which these areas are selectively affected, ranging from white matter injury of prematurity in infants to leukoaraiosis in the elderly. Using edge betweenness centrality, we identify specific white matter regions involved in a large number of shortest paths, some containing highly connected rich club edges while others are relatively isolated within individual modules. Overall, these findings reveal an intricate relationship between white matter anatomy and the structural connectome, motivating further exploration of EDI for biomarkers of cognition and behavior.

Uncovering brain activity from magnetoencephalography (MEG) data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources. Sparse reconstruction algorithms, such as Champagne, show great promise in that they provide focal brain activations robust to these confounds. In this paper, we address the technical considerations of statistically thresholding brain images obtained from sparse reconstruction algorithms. The source power distribution of sparse algorithms makes this class of algorithms ill-suited to "conventional" techniques. We propose two non-parametric resampling methods hypothesized to be compatible with sparse algorithms. The first adapts the maximal statistic procedure to sparse reconstruction results and the second departs from the maximal statistic, putting forth a less stringent procedure that protects against spurious peaks. Simulated MEG data and three real data sets are utilized to demonstrate the efficacy of the proposed methods. Two sparse algorithms, Champagne and generalized minimum-current estimation (G-MCE), are compared to two non-sparse algorithms, a variant of minimum-norm estimation, sLORETA, and an adaptive beamformer. The results, in general, demonstrate that the already sparse images obtained from Champagne and G-MCE are further thresholded by both proposed statistical thresholding procedures. While non-sparse algorithms are thresholded by the maximal statistic procedure, they are not made sparse. The work presented here is one of the first attempts to address the problem of statistically thresholding sparse reconstructions, and aims to improve upon this already advantageous and powerful class of algorithm.

Sensory processing disorders (SPD) affect 5-16% of school-aged children and can cause long-term deficits in intellectual and social development. Current theories of SPD implicate primary sensory cortical areas and higher-order multisensory integration (MSI) cortical regions. We investigate the role of white matter microstructural abnormalities in SPD using diffusion tensor imaging (DTI). DTI was acquired in 16 boys, 8-11 years old, with SPD and 24 age-, gender-, handedness- and IQ-matched neurotypical controls. Behavior was characterized using a parent report sensory behavior measure, the Sensory Profile. Fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD) were calculated. Tract-based spatial statistics were used to detect significant group differences in white matter integrity and to determine if microstructural parameters were significantly correlated with behavioral measures. Significant decreases in FA and increases in MD and RD were found in the SPD cohort compared to controls, primarily involving posterior white matter including the posterior corpus callosum, posterior corona radiata and posterior thalamic radiations. Strong positive correlations were observed between FA of these posterior tracts and auditory, multisensory, and inattention scores (r = 0.51-0.78; p < 0.001) with strong negative correlations between RD and multisensory and inattention scores (r = - 0.61-0.71; p < 0.001). To our knowledge, this is the first study to demonstrate reduced white matter microstructural integrity in children with SPD. We find that the disrupted white matter microstructure predominantly involves posterior cerebral tracts and correlates strongly with atypical unimodal and multisensory integration behavior. These findings suggest abnormal white matter as a biological basis for SPD and may also distinguish SPD from overlapping clinical conditions such as autism and attention deficit hyperactivity disorder.

The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled nonlinear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30 Hz) activity estimated from source localized magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. .

Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity. We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations, but show significant, interpretable deviations in improperly developed brains. More specifically, we investigated the effect of agenesis of the corpus callosum (AgCC), one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself. These findings, including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging, show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome.

Traumatic brain injury damages white matter pathways that connect brain regions, disrupting transmission of electrochemical signals and causing cognitive and emotional dysfunction. Connectome-level mechanisms for how the brain compensates for injury have not been fully characterized. Here, we collected serial MRI-based structural and functional connectome metrics and neuropsychological scores in 26 mild traumatic brain injury subjects (29.4 ± 8.0 years, 20 males) at 1 and 6 months postinjury. We quantified the relationship between functional and structural connectomes using network diffusion (ND) model propagation time, a measure that can be interpreted as how much of the structural connectome is being utilized for the spread of functional activation, as captured via the functional connectome. Overall cognition showed significant improvement from 1 to 6 months (t₂₅ = -2.15, p = .04). None of the structural or functional global connectome metrics was significantly different between 1 and 6 months, or when compared to 34 age- and gender-matched controls (28.6 ± 8.8 years, 25 males). We predicted longitudinal changes in overall cognition from changes in global connectome measures using a partial least squares regression model (cross-validated R² = .27). We observe that increased ND model propagation time, increased structural connectome segregation, and increased functional connectome integration were related to better cognitive recovery. We interpret these findings as suggesting two connectome-based postinjury recovery mechanisms: one of neuroplasticity that increases functional connectome integration and one of remote white matter degeneration that increases structural connectome segregation. We hypothesize that our inherently multimodal measure of ND model propagation time captures the interplay between these two mechanisms.

Structural and functional connectomes are emerging as important instruments in the study of normal brain function and in the development of new biomarkers for a variety of brain disorders. In contrast to single-network studies that presently dominate the (non-connectome) network literature, connectome analyses typically examine groups of empirical networks and then compare these against standard (stochastic) network models. The current practice in connectome studies is to employ stochastic network models derived from social science and engineering contexts as the basis for the comparison. However, these are not necessarily best suited for the analysis of connectomes, which often contain groups of very closely related networks, such as occurs with a set of controls or a set of patients with a specific disorder. This paper studies important extensions of standard stochastic models that make them better adapted for analysis of connectomes, and develops new statistical fitting methodologies that account for inter-subject variations. The extensions explicitly incorporate geometric information about a network based on distances and inter/intra hemispherical asymmetries (to supplement ordinary degree-distribution information), and utilize a stochastic choice of network density levels (for fixed threshold networks) to better capture the variance in average connectivity among subjects. The new statistical tools introduced here allow one to compare groups of networks by matching both their average characteristics and the variations among them. A notable finding is that connectomes have high "smallworldness" beyond that arising from geometric and degree considerations alone.

There is evidence from histopathological studies that glioma tumor cells migrate preferentially along large white matter bundles. If the peritumoral white matter structures can be used to predict the likely trajectory of migrating tumor cells outside of the surgical margin, then this information could be used to inform the delineation of radiation therapy (RT) targets. In theory, an anisotropic expansion that takes large white matter bundle anatomy into account may maximize the chances of treating migrating cancer cells and minimize the amount of brain tissue exposed to high doses of ionizing radiation. Diffusion-weighted MRI (DW-MRI) can be used in combination with fiber tracking algorithms to model the trajectory of large white matter pathways using the direction and magnitude of water movement in tissue. The method presented here is a tool for translating a DW-MRI fiber tracking (tractography) dataset into a white matter path length (WMPL) map that assigns each voxel the shortest distance along a streamline back to a specified region of interest (ROI). We present an open-source WMPL tool, implemented in the package Diffusion Imaging in Python (DIPY), and code to convert the resulting WMPL map to anisotropic contours for RT in a commercial treatment planning system. This proof-of-concept lays the groundwork for future studies to evaluate the clinical value of incorporating tractography modeling into treatment planning.

Objective: Recent evidence suggests that co-occurring deficits in cognitive control and visuomotor control are common to many neurodevelopmental disorders. Specifically, children with sensory processing dysfunction (SPD), a condition characterized by sensory hyper/hypo-sensitivity, show varying degrees of overlapping attention and visuomotor challenges. In this study, we assess associations between cognitive and visuomotor control abilities among children with and without SPD. In this same context, we also examined the common and unique diffusion tensor imaging (DTI) tracts that may support the overlap of cognitive control and visuomotor control. Method: We collected cognitive control and visuomotor control behavioral measures as well as DTI data in 37 children with SPD and 25 typically developing controls (TDCs). We constructed regressions to assess for associations between behavioral performance and mean fractional anisotropy (FA) in selected regions of interest (ROIs). Results: We observed an association between behavioral performance on cognitive control and visuomotor control. Further, our findings indicated that FA in the anterior limb of the internal capsule (ALIC), the anterior thalamic radiation (ATR), and the superior longitudinal fasciculus (SLF) are associated with both cognitive control and visuomotor control, while FA in the superior corona radiata (SCR) uniquely correlate with cognitive control performance and FA in the posterior limb of the internal capsule (PLIC) and the cerebral peduncle (CP) tract uniquely correlate with visuomotor control performance. Conclusions: These findings suggest that children who demonstrate lower cognitive control are also more likely to demonstrate lower visuomotor control, and vice-versa, regardless of clinical cohort assignment. The overlapping neural tracts, which correlate with both cognitive and visuomotor control suggest a possible common neural mechanism supporting both control-based processes.