scholarly journals The frustrated brain: from dynamics on motifs to communities and networks

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130532 ◽  
Author(s):  
Leonardo L. Gollo ◽  
Michael Breakspear
Keyword(s):  
Cognitive Processes ◽  
Large Scale ◽  
Phase Synchronization ◽  
Metastable Phase ◽  
Network Size ◽  
Stable States ◽  
Adaptive Function ◽  
Cortical Networks ◽  
Broad Variety ◽  
Coupling Strengths

Cognitive function depends on an adaptive balance between flexible dynamics and integrative processes in distributed cortical networks. Patterns of zero-lag synchrony likely underpin numerous perceptual and cognitive functions. Synchronization fulfils integration by reducing entropy, while adaptive function mandates that a broad variety of stable states be readily accessible. Here, we elucidate two complementary influences on patterns of zero-lag synchrony that derive from basic properties of brain networks. First, mutually coupled pairs of neuronal subsystems—resonance pairs—promote stable zero-lag synchrony among the small motifs in which they are embedded, and whose effects can propagate along connected chains. Second, frustrated closed-loop motifs disrupt synchronous dynamics, enabling metastable configurations of zero-lag synchrony to coexist. We document these two complementary influences in small motifs and illustrate how these effects underpin stable versus metastable phase-synchronization patterns in prototypical modular networks and in large-scale cortical networks of the macaque (CoCoMac). We find that the variability of synchronization patterns depends on the inter-node time delay, increases with the network size and is maximized for intermediate coupling strengths. We hypothesize that the dialectic influences of resonance versus frustration may form a dynamic substrate for flexible neuronal integration, an essential platform across diverse cognitive processes.

MULTIPLE OSCILLATORY STATES IN MODELS OF COLLECTIVE NEURONAL DYNAMICS

2014 ◽  
Vol 24 (06) ◽  
pp. 1450020 ◽  
Author(s):  
STILIYAN KALITZIN ◽  
MARCUS KOPPERT ◽  
GEORGE PETKOV ◽  
FERNANDO LOPES DA SILVA
Keyword(s):  
Analytical Model ◽  
Large Scale ◽  
Computational Models ◽  
Phase Synchronization ◽  
Initial Conditions ◽  
State Transitions ◽  
Model Parameters ◽  
Reactive Control ◽  
Stable States ◽  
Optimal Method

In our previous studies, we showed that the both realistic and analytical computational models of neural dynamics can display multiple sustained states (attractors) for the same values of model parameters. Some of these states can represent normal activity while other, of oscillatory nature, may represent epileptic types of activity. We also showed that a simplified, analytical model can mimic this type of behavior and can be used instead of the realistic model for large scale simulations. The primary objective of the present work is to further explore the phenomenon of multiple stable states, co-existing in the same operational model, or phase space, in systems consisting of large number of interconnected basic units. As a second goal, we aim to specify the optimal method for state control of the system based on inducing state transitions using appropriate external stimulus. We use here interconnected model units that represent the behavior of neuronal populations as an effective dynamic system. The model unit is an analytical model (S. Kalitzin et al., Epilepsy Behav. 22 (2011) S102–S109) and does not correspond directly to realistic neuronal processes (excitatory–inhibitory synaptic interactions, action potential generation). For certain parameter choices however it displays bistable dynamics imitating the behavior of realistic neural mass models. To analyze the collective behavior of the system we applied phase synchronization analysis (PSA), principal component analysis (PCA) and stability analysis using Lyapunov exponent (LE) estimation. We obtained a large variety of stable states with different dynamic characteristics, oscillatory modes and phase relations between the units. These states can be initiated by appropriate initial conditions; transitions between them can be induced stochastically by fluctuating variables (noise) or by specific inputs. We propose a method for optimal reactive control, allowing forced transitions from one state (attractor) into another.

scholarly journals Cortical traveling waves reflect state-dependent hierarchical sequencing of local regions in the human connectome network

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Naoyuki Sato
Keyword(s):  
Traveling Waves ◽  
Large Scale ◽  
Phase Synchronization ◽  
Hierarchical Organization ◽  
Cortical Surface ◽  
Intrinsic Frequency ◽  
Cortical Networks ◽  
State Dependent ◽  
Cortical Regions ◽  
Empirical Networks

AbstractRecent human studies using electrocorticography have demonstrated that alpha and theta band oscillations form traveling waves on the cortical surface. According to neural synchronization theories, the cortical traveling waves may group local cortical regions and sequence them by phase synchronization; however these contributions have not yet been assessed. This study aimed to evaluate the functional contributions of traveling waves using connectome-based network modeling. In the simulation, we observed stable traveling waves on the entire cortical surface wherein the topographical pattern of these phases was substantially correlated with the empirically obtained resting-state networks, and local radial waves also appeared within the size of the empirical networks (< 50 mm). Importantly, individual regions in the entire network were instantaneously sequenced by their internal frequencies, and regions with higher intrinsic frequency were seen in the earlier phases of the traveling waves. Based on the communication-through-coherence theory, this phase configuration produced a hierarchical organization of each region by unidirectional communication between the arbitrarily paired regions. In conclusion, cortical traveling waves reflect the intrinsic frequency-dependent hierarchical sequencing of local regions, global traveling waves sequence the set of large-scale cortical networks, and local traveling waves sequence local regions within individual cortical networks.

scholarly journals Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abhishek Uday Patil ◽  
Sejal Ghate ◽  
Deepa Madathil ◽  
Ovid J. L. Tzeng ◽  
Hsu-Wen Huang ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Large Scale ◽  
Brain Networks ◽  
Blood Oxygen Level Dependent ◽  
Detection Algorithm ◽  
Brain Regions ◽  
Creative Cognition ◽  
Network Flexibility ◽  
Community Detection Algorithm ◽  
Window Approach

AbstractCreative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.

scholarly journals Studying thermodynamics of metastable states

2005 ◽  
Vol 3 (2) ◽  
pp. 115-128
Author(s):  
Yuri Kornyushin
Keyword(s):  
Heat Capacity ◽  
Relaxation Time ◽  
Metastable Phase ◽  
Nonequilibrium State ◽  
Metastable States ◽  
Metastable Equilibrium ◽  
General Description ◽  
Stable States ◽  
Heated Sample ◽  
Temperature Oscillations

Simple classical thermodynamic approach to the general description of metastable states is presented. It makes it possible to calculate the explicit dependence of the Gibbs free energy on temperature, to calculate the heat capacity, the thermodynamic barrier, dividing metastable and more stable states, and the thermal expansion coefficient. Thermodynamic stability under mechanical loading is considered. The influence of the heating (cooling) rate on the measured dynamic heat capacity is investigated. A phase shift of the temperature oscillations of an ac heated sample is shown to be determined by the relaxation time of the relaxation of the metastable nonequilibrium state back to the metastable equilibrium one. This dependence allows one to calculate the relaxation time. A general description of the metastable phase equilibrium is proposed. Metastable states in AB3 alloys are considered. Reasons for the change from the diffusional mechanism of the supercritical nucleus growth to the martensitic one as the heating rate increases are discussed. The Ostwald stage rule is derived.

scholarly journals A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

2019 ◽  
Author(s):  
Daniel A Llano ◽  
Chihua Ma ◽  
Umberto Di Fabrizio ◽  
Aynaz Taheri ◽  
Kevin A. Stebbings ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Large Scale ◽  
Brain Slices ◽  
Dynamic Network ◽  
Analytical Tool ◽  
Aging Brain ◽  
Autofluorescence Imaging ◽  
Imaging Data ◽  
Cortical Networks ◽  
Network Metrics

AbstractNetwork analysis of large-scale neuroimaging data has proven to be a particularly challenging computational problem. In this study, we adapt a novel analytical tool, known as the community dynamic inference method (CommDy), which was inspired by social network theory, for the study of brain imaging data from an aging mouse model. CommDy has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network parameters in the auditory and motor cortices using flavoprotein autofluorescence imaging in brain slices and in vivo. Analysis of spontaneous activations in the auditory cortex of slices taken from young and aged animals demonstrated that cortical networks in aged brains were highly fragmented compared to networks observed in young animals. Specifically, the degree of connectivity of each activated node in the aged brains was significantly lower than those seen in the young brain, and multivariate analyses of all derived network metrics showed distinct clusters of these metrics in young vs. aged brains. CommDy network metrics were then used to build a random-forests classifier based on NMDA-receptor blockade data, which successfully recapitulated the aging findings, suggesting that the excitatory synaptic substructure of the auditory cortex may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity obtained from the awake motor cortex, suggesting that the findings in the auditory cortex are reflections of general mechanisms that occur during aging. Therefore, CommDy therefore provides a new dynamic network analytical tool to study the brain and provides links between network-level and synaptic-level dysfunction in the aging brain.

Large-Scale Discrete-Time Interconnected Dynamical Systems

2011 ◽  
Author(s):  
Wassim M. Haddad ◽  
Sergey G. Nersesov
Keyword(s):  
Dynamical Systems ◽  
Discrete Time ◽  
Large Scale ◽  
Discrete Energy ◽  
Flow Balance ◽  
Subsystem Level ◽  
Coupling Strengths ◽  
Definition Of ◽  
Energy Dissipated ◽  
Discrete Time Dynamical Systems

This chapter develops vector dissipativity notions for large-scale nonlinear discrete-time dynamical systems. In particular, it introduces a generalized definition of dissipativity for large-scale nonlinear discrete-time dynamical systems in terms of a vector dissipation inequality involving a vector supply rate, a vector storage function, and a nonnegative, semistable dissipation matrix. On the subsystem level, the proposed approach provides a discrete energy flow balance in terms of the stored subsystem energy, the supplied subsystem energy, the subsystem energy gained from all other subsystems independent of the subsystem coupling strengths, and the subsystem energy dissipated. The chapter also develops extended Kalman–Yakubovich–Popov conditions, in terms of the local subsystem dynamics and the interconnection constraints, for characterizing vector dissipativeness via vector storage functions for large-scale discrete-time dynamical systems.

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