Anatomy of the human brain constrains the formation of large-scale functional networks. Here, given measured brain activity in gray matter, we interpolate these functional signals into the white matter on a structurallyinformed high-resolution voxel-level brain grid. The interpolated volumes reflect the underlying anatomical information, revealing white matter structures that mediate functional signal flow between temporally coherent gray matter regions. Functional connectivity analyses of the interpolated volumes reveal an enriched picture of the default mode network (DMN) and its subcomponents, including how white matter bundles support their formation, thus transcending currently known spatial patterns that are limited within the gray matter only. These subcomponents have distinct structurefunction patterns, each of which are differentially recruited during tasks, demonstrating plausible structural mechanisms for functional switching between task-positive and -negative components. This work opens new avenues for integration of brain structure and function, and demonstrates how global patterns of activity arise from a collective interplay of signal propagation along different white matter pathways.Coordination of distant neuronal populations gives rise to a vast repertoire of functional networks that underpin human brain function. Using functional magnetic resonance imaging (fMRI), temporally coherent activity can be investigated using measures of functional connectivity (FC). On the other hand, the mediation of inter-regional communication by the anatomical scaffold can be conveniently summarized by structural connectivity (SC) extracted from diffusion-weighted MRI (DW-MRI). Over the past decade, several methods have been proposed to bridge the gap between SC and FC. Seminal works have found evidence for strong statistical interdependence between separately defined SC and FC [1][2][3][4]. Limited by the bivariate and summarizing nature of the analysis, the effect is capturing only a general trend of correlation. Following after were studies that specify regions of interests (ROI) as a FC prior for extracting white matter pathways [5,6], most of which were specifically concentrated to the analysis of the default mode network (DMN), a set of brain regions that are known be more engaged during rest [7]. In contrast to extracting SC from FC priors, a number of studies have attempted to reproduce brain activity from predefined structural connectomes through numerical simulations [8][9][10][11][12][13].Studies that extract SC from FC or vice versa are mostly hypothesisdriven and entail many explicit assumptions. In order to understand how distributed patterns of functional activity arise from a fixed underlying anatomy, a need for research methodologies that are observer-independent and datadriven are of utmost importance. A common approach for data-driven approaches are based on blind-decomposition techniques. By combining diffusion anisotropy (e.g., fractional anisotropy, axial and radial diffusivities) and classi...

Functional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Here, we explored the dynamical properties of large-scale functional brain networks derived from transient brain activity using functional magnetic resonance imaging. Spatial brain maps generally display significant modifications in terms of their tendency to occur across wakefulness and NREM sleep. Unexpectedly, almost all networks predominated in activity during NREM stage 2 before an abrupt loss of activity is observed in NREM stage 3. Yet, functional connectivity and mutual dependencies between these networks progressively broke down with increasing sleep depth. Thus, the efficiency of information transfer during NREM stage 2 is low despite the high attempt to communicate. Critically, our approach provides relevant data for evaluating functional brain network integrity and our findings robustly support a significant advance in our neural models of human sleep and consciousness.