BACKGROUND During an experimental session, behavioral performance fluctuates, yet most neuroimaging analyses of functional connectivity derive a single connectivity pattern. These conventional connectivity approaches assume that since the underlying behavior of the task remains constant, the connectivity pattern is also constant. NEW METHOD We introduce a novel method, behavior-regressed conn...

International efforts are underway to establish well-connected systems of marine protected areas (MPAs) covering at least 10% of the ocean by 2020. But the nature and dynamics of ocean ecosystem connectivity are poorly understood, with unresolved effects of climate variability. We used 40-year runs of a particle tracking model to examine the sensitivity of an MPA network for habitat-forming col...

Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from...

In this paper, we present a novel and versatile method to study the dynamics of restingstate fMRI brain connectivity with a high temporal sensitivity. Whereas most existing methods often rely on dividing the time-series into larger segments of data (i.e. so called sliding window techniques), the point-based method (PBM) proposed here provides an estimate of brain connectivity at the level of in...

Emerging ideas of brain function emphasize the context-dependency of regional contributions to cognitive operations, where the function of a particular region is constrained by its pattern of functional connectivity. We used functional magnetic resonance imaging to examine how modality of input (auditory or visual) affects prefrontal cortex (PFC) functional connectivity for simple working memor...

People familiar with an urban environment share common spatial knowledge enabling, for instance, successful communication about places and routes. Mental representations of this spatial knowledge, as well as reasoning and communication processes are organized hierarchically. This paper introduces a hierarchical model of urban street networks motivated by shared experience of space. This model i...

This paper proposes a connectivity-based method for circle $tting. The use of pixel connectivity e3ectively avoids false circle detection, improves the robustness against noise and signi$cantly reduces the computational load. The desired circular models are extracted by searching for meaningful circular arcs. The algorithm does not require a good initial guess, and is e3ective for extracting an...

Brain connectivity networks have been widely used for diagnosis of brain-related diseases, e.g., Alzheimer’s disease (AD), mild cognitive impairment (MCI), and attention deficit hyperactivity disorder (ADHD). Although several network descriptors have been designed for representing brain connectivity networks, most of them not only ignore the important weight information of edges, but also canno...

Understanding the neural mechanisms responsible for human social interactions is difficult, since the brain activities of two or more individuals have to be examined simultaneously and correlated with the observed social patterns. We introduce the concept of hyper-brain network, a connectivity pattern representing at once the information flow among the cortical regions of a single brain as well...