Brain network neuroimaging studies the brain as a system of connected regions. While most methods look at undirected connections, newer techniques can capture directional activity. However, these signals include both direct and indirect interactions. This work introduces a regression model that separates these effects to identify direct communication between regions and map effective brain connectivity.
We introduce surrogate framework based on stationary process and give guiding examples and an application in which we compare performance of our framework with existing undirected graph surrogates and show that graph phase randomized surrogates are more suitable to account for directionality. This framework is valuable in the study of network communication and with application to fields such as neuroimaging where biological priors dictate the directionality of graph edges
We demonstrate the feasibility of the scheme to detect irregular node covariance and benchmark our method against conventional schemes using the symmetrized graph. We also investigate how the level of asymmetry affects the detection performance, thus assessing the advantages of the presented approach. Finally, we show results for a real-world graph extracted from the Freeman EIES social network dataset.
We propose a generalization of the Hilbert transform interpreted over the newfound cycle cover, which re-establishes intuitions from traditional Hilbert transform, equivalent to the generalized Hilbert transform on a single cycle. This generalization leads to a number of simple and elegant recipes to effectively exploit the phase information of graph signals provided by the graph Fourier transform. The feasibility of the approach is demonstrated on several examples.
Here, we leverage recent work on community detection for directed graphs and propose a community-driven signal processing approach.
Here, we leverage recent work on community detection for directed graphs and propose a community-driven signal processing approach.
We propose an end-to-end deep neural encoder-decoder model to encode and decode brain activity in response to naturalistic stimuli using functional magnetic resonance imaging (fMRI) data.
In this work we study the relation between individual differences, in particular, the state anxiety and the openness scores, and brain activity during the processing of various emotional scenes in films, through functional gradients.
Our objective was to build an better understanding of design paradigms’ role in introspection. Through formative itera- tions, informed by Self-Determination Theory (SDT), we designed and developed diferent tool formulations (Analogue, Digital, and Hybrid) for comparison.
We present a convolutional neural network (CNN) named YeaZ, the underlying training set of high-quality segmented yeast images (>10 000 cells) including mutants, stressed cells, and time courses, as well as a graphical user interface and a web application (www.quantsysbio.com/data-and-software) to efficiently employ, test, and expand the system