Joint density distributions of dynamic spatial brain networks show systematic variations at rest.

Human resting-state functional magnetic resonance imaging data have been broadly studied previously to identify coherent spatio-temporal patterns of activity in functional brain networks and their dysfunction in brain disorders. While most studies focused on spatially static networks, here we developed an approach to estimate 4D spatially dynamic brain networks, evaluated systematic voxel-wise changes in such networks and the joint density distributions between pairs of networks using two-dimensional (2D) histograms. Clusters of 2D histograms computed using the k-means algorithm across subjects and sliding windows for each network pair showed significant group differences in subject-wise cluster occupancy and dwell time between healthy controls (CN) and patients with schizophrenia (SZ), implying altered network dynamics and interactions.

Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity.

Introduction: The Integration of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has allowed for a novel exploration of the brain's spatial-temporal resolution. While functional brain networks show variations in both spatial and temporal dimensions, most studies focus on fixed spatial networks that change together over time.

Methods: In this study, for the first time, we link spatially dynamic brain networks with EEG spectral properties recorded simultaneously, which allows us to concurrently capture high spatial and temporal resolutions offered by these complementary imaging modalities.

4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia.

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.

4D DYNAMIC SPATIAL BRAIN NETWORKS AT REST LINKED TO COGNITION SHOW ATYPICAL VARIABILITY AND COUPLING IN SCHIZOPHRENIA.

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain net-works in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.

Ordered intricacy of Shilnikov saddle-focus homoclinics in symmetric systems.

Using the technique of Poincaré return maps, we disclose an intricate order of subsequent homoclinic bifurcations near the primary figure-8 connection of the Shilnikov saddle-focus in systems with reflection symmetry. We also reveal admissible shapes of the corresponding bifurcation curves in a parameter space. Their scalability ratio and organization are proven to be universal for such homoclinic bifurcations of higher orders.

Homoclinic chaos in the Rössler model.

We study the origin of homoclinic chaos in the classical 3D model proposed by Rössler in 1976. Of our particular interest are the convoluted bifurcations of the Shilnikov saddle-foci and how their synergy determines the global bifurcation unfolding of the model, along with transformations of its chaotic attractors. We apply two computational methods proposed, one based on interval maps and a symbolic approach specifically tailored to this model, to scrutinize homoclinic bifurcations, as well as to detect the regions of structurally stable and chaotic dynamics in the parameter space of the Rössler model.

Dynamics and bifurcations in multistable 3-cell neural networks.

We disclose the generality of the intrinsic mechanisms underlying multistability in reciprocally inhibitory 3-cell circuits composed of simplified, low-dimensional models of oscillatory neurons, as opposed to those of a detailed Hodgkin-Huxley type [Wojcik et al., PLoS One 9, e92918 (2014)]. The computational reduction to return maps for the phase-lags between neurons reveals a rich multiplicity of rhythmic patterns in such circuits.