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Fibromyalgia (FM) is a chronic widespread pain condition characterized by augmented multi-modal sensory sensitivity. Although the mechanisms underlying this sensitivity are thought to involve an imbalance in excitatory and inhibitory activity throughout the brain, the underlying neural network properties associated with hypersensitivity to pain stimuli are largely unknown. In network science, explosive synchronization (ES) was introduced as a mechanism of hypersensitivity in diverse biological and physical systems that display explosive and global propagations with small perturbations. We hypothesized that ES may also be a mechanism of the hypersensitivity in FM brains. To test this hypothesis, we analyzed resting state electroencephalogram (EEG) of 10 FM patients. First, we examined theoretically well-known ES conditions within functional brain networks reconstructed from EEG, then tested whether a brain network model with ES conditions identified in the EEG data is sensitive to an external perturbation. We demonstrate for the first time that the FM brain displays characteristics of ES conditions, and that these factors significantly correlate with chronic pain intensity. The simulation data support the conclusion that networks with ES conditions are more sensitive to perturbation compared to non-ES network. The model and empirical data analysis provide convergent evidence that ES may be a network mechanism of FM hypersensitivity.

Introduction: Recent evidence suggests that the conscious brain is characterized by a diverse repertoire of functional connectivity patterns while the anesthetized brain shows stereotyped activity. However, classical time-averaged methods of connectivity dismiss dynamic and temporal characteristics of functional configurations. Here we demonstrate a new approach which characterizes time-varying patterns of functional connectivity at the subsecond time scale., Methods: We introduce phase-lag entropy (PLE), a measure of the diversity of temporal patterns in the phase relationships between two signals. The proposed measure was applied to multichannel electroencephalogram (EEG), which were recorded from two distinct experimental settings: (1) propofol was administrated at a constant infusion rate for 60 min (n = 96); (2) administration of propofol by a target effect-site concentration-controlled infusion with simultaneous assessment of the level of consciousness (n = 10)., Results: From the first dataset, two substantial changes of the phase relationship during anesthesia was found: (1) the dynamics of the phase relationship between frontal channels became progressively less diverse and more stereotyped during unconsciousness, quantified as a reduction in PLE; and (2) the reduction in PLE was consistent across subjects. Furthermore, PLE provided better performance in the classification of states of consciousness than did phase-lag index, a classical time-averaged connectivity method. From the second dataset, PLE showed the highest agreement with the level of consciousness, compared to existing anesthetic depth indicators., Conclusions: This study suggests that a scarcity of functional configurations is closely associated with anesthetically induced unconsciousness, and shows promise as a basis for a new consciousness monitoring system during general anesthesia. Hum Brain Mapp 38:4980-4995, 2017. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

Slow coherent spontaneous fluctuations (<0.1 Hz) in functional magnetic resonance imaging blood-oxygen-level-dependent signals have been observed for a resting state of the human brain. In this paper, considering feed-forward inhibition in addition to excitation between brain areas, which we assume to be in up (active) or down (quiescent) states, we propose a model for the generation and organization of the slow fluctuations. Connectivity with feed-forward excitation and inhibition between the areas makes the system have multiple stable states and organized slow fluctuations manifest as noise-induced slow transitions between the states. With various connectivities, we observe slow fluctuations and various organizations, including anticorrelated clusters, through numerical simulations.

General anesthesia significantly alters brain network connectivity. Graph-theoretical analysis has been used extensively to study static brain networks but may be limited in the study of rapidly changing brain connectivity during induction of or recovery from general anesthesia. Here we introduce a novel method to study the temporal evolution of network modules in the brain. We recorded multichannel electroencephalograms (EEG) from 18 surgical patients who underwent general anesthesia with either propofol (n = 9) or sevoflurane (n = 9). Time series data were used to reconstruct networks; each electroencephalographic channel was defined as a node and correlated activity between the channels was defined as a link. We analyzed the frequency of subgraphs in the network with a defined number of links; subgraphs with a high probability of occurrence were deemed network "backbones." We analyzed the behavior of network backbones across consciousness, anesthetic induction, anesthetic maintenance, and two points of recovery. Constitutive, variable and state-specific backbones were identified across anesthetic state transitions. Brain networks derived from neurophysiologic data can be deconstructed into network backbones that change rapidly across states of consciousness. This technique enabled a granular description of network evolution over time. The concept of network backbones may facilitate graph-theoretical analysis of dynamically changing networks.

Background: Loss of consciousness is an essential feature of general anesthesia. Although alterations of neural networks during anesthesia have been identified in the spatial domain, there has been relatively little study of temporal organization., Methods: Ten healthy male volunteers were anesthetized with an induction dose of propofol on two separate occasions. The duration of network connections in the brain was analyzed by multichannel electroencephalography and the minimum spanning tree method. Entropy of the connections was calculated based on Shannon entropy. The global temporal configuration of networks was investigated by constructing the cumulative distribution function of connection times in different frequency bands and different states of consciousness., Results: General anesthesia was associated with a significant reduction in the number of network connections, as well as significant alterations of their duration. These changes were most prominent in the δ bandwidth and were also associated with a significant reduction in entropy of the connection matrix. Despite these and other changes, a global "scale-free" organization was consistently preserved across multiple subjects, anesthetic exposures, states of consciousness, and electroencephalogram frequencies., Conclusions: Our data suggest a fundamental principle of temporal organization of network connectivity that is maintained during consciousness and anesthesia, despite local changes. These findings are consistent with a process of adaptive reconfiguration during general anesthesia.

In consciousness or anesthesia studies, pinpointing the precise moment of consciousness or anesthetic transition has been challenging because of the variable lag time between a treatment and its induced response. Here, we describe a novel behavioral method, a forced walking test, which pinpoints the moment of the anesthetic-induced loss of motion (LOM) without handling the animals manually. The mouse is forced to walk on a treadmill, and an anesthetic drug is administered into the peritoneum via a previously secured injection route. The physical activity and the angle of head posture are tracked using a motion sensor preinstalled on the animal's head. The moments of LOM and recovery of motion (ROM) are identified from the physical activity parameters obtained by the sensor. Comparison of our method with the conventional loss-of-righting-reflex assay showed that the time point of LOM was not significantly different between the two methods when examined with two different types of anesthetic agents, propofol and ketamine/xylazine cocktail. In addition, the electrophysiological signals simultaneously acquired in the cortex and the thalamus of the mouse during the forced walking test showed that the brain rhythms induced by ketamine/xylazine anesthesia were generated and terminated in a time-locked manner with respect to LOM and ROM, respectively. In conclusion, the forced walking test allows an objective and precise detection of anesthetic-induced LOM, as well as ROM during awakening from anesthesia, in test animals., (Copyright 2010 Elsevier B.V. All rights reserved.)

Recent studies on complex systems have shown that the synchronization of oscillators, including neuronal ones, is faster, stronger, and more efficient in small-world networks than in regular or random networks. We show that the functional structures in the brain can be self-organized to both small-world and scale-free networks by synaptic reorganization via spike timing dependent synaptic plasticity instead of conventional Hebbian learning rules. We show that the balance between the excitatory and the inhibitory synaptic inputs is critical in the formation of the functional structure, which is found to lie in a self-organized critical state.

Epilepsy is a dynamic disease in which self-organization and emergent structures occur dynamically at multiple levels of neuronal integration. Therefore, the transient relationship within multichannel electroencephalograms (EEGs) is crucial for understanding epileptic processes. In this paper, we show that the global relationship within multichannel EEGs provides us with more useful information in classifying two different epilepsy types than pairwise relationships such as cross correlation. To demonstrate this, we determine the global network structure within channels of the scalp EEG based on the minimum spanning tree method. The topological dissimilarity of the network structures from different types of temporal lobe epilepsy is described in the form of the divergence rate and is computed for 11 patients with left (LTLE) and right temporal lobe epilepsy (RTLE). We find that patients with LTLE and RTLE exhibit different large scale network structures, which emerge at the epoch immediately before the seizure onset, not in the preceding epochs. Our results suggest that patients with the two different epilepsy types display distinct large scale dynamical networks with characteristic epileptic network structures.

Tinnitus is defined as an unwanted auditory perception of internal origin, usually localized, and rarely heard by others. Persisting appearances of tinnitus are most commonly combined with diseases or damage in the inner ear or neuro-auditory pathway. Diffusion tensor magnetic resonance imaging (DTI) is a new imaging method with the capability of providing non-invasive information on tissue microstructure not available in routine clinical MRI images. Since white matter regions of the brain are an ordered structure due to the myelination and directionality of axons and have a high degree of anisotropy, the ability to detect changes in anisotropy can be extremely useful in the study of diseases such as tinnitus and multiple sclerosis, which are assumed to involve the demyelination of axons. While several studies investigated tinnitus using MRI, few studies tried to analyze neurological disorders quantitatively using DTI. In this study, the cerebral volume of white matter on DTI images of patients with tinnitus was measured using the semi-automated and intuitive menu based image processing tool (Human Analyzer, ETRI, Korea). Total number of ten patients with tinnitus including three women was examined.

To investigate postoperative changes in the cerebral glucose metabolism of patients with mesial temporal lobe epilepsy (MTLE), statistical parametric mapping (SPM) analysis was performed on pre- and postoperative (18)F-fluorodeoxyglucose PET (FDG-PET) images. We included 28 patients with MTLE who had undergone surgery and had been seizure-free postoperatively (16 had left MTLE and 12 right MTLE). All patients showed hippocampal sclerosis by pathology or brain MRI. FDG-PET images of the 12 right temporal lobe epilepsy patients were reversed to lateralize the epileptogenic zone to the left side in all patients. Application of the paired t-test in SPM to pre- and postoperative FDG-PETs showed that postoperative glucose metabolism decreased in the caudate nucleus, the pulvinar of the thalamus, fusiform gyrus, lingual gyrus and the posterior region of the insular cortex in the hemisphere ipsilateral to resection, whereas postoperative glucose metabolism increased in the anterior region of the insular cortex, temporal stem white matter, midbrain, inferior precentral gyrus, anterior cingulate gyrus and supramarginal gyrus in the hemisphere ipsilateral to resection. No significant postsurgical changes in cerebral glucose metabolism occurred in the contralateral hemisphere. Subtraction between pre- and postoperative FDG-PET images in individual patients produced similar findings to the SPM results, and additionally showed that postoperative glucose metabolism increased in the anterior thalamus in 12/28 patients (42.8%). SISCOM (subtraction ictal-interictal SPECT co-registered to MRI) performed in 17 patients showed ictal hyperperfusion in the ipsilateral temporal lobe, including the temporal stem white matter, midbrain, insular cortex and cingulate gyrus, bilateral basal ganglia and thalami, and multiple small regions in the frontoparietal lobes during seizures. This study suggests that brain regions showing a postoperative increase in glucose metabolism appear to represent the propagation pathways of ictal and interictal epileptic discharges in MTLE, whereas the postoperative decrease in glucose metabolism may be related to a permanent loss of afferents from resected anterior-mesial temporal structures.

Purpose: Ictal-interictal single-photon emission computed tomography (SPECT) subtraction was performed to find brain structures related to ictal dystonic posturing (DP) in patients with temporal lobe epilepsy (TLE)., Methods: Thirty-two patients with mesial TLE who had ictal and interictal SPECTs were included. They were divided into two groups; DP group with ictal dystonia during ictal SPECT (n = 15) and Non-DP group without ictal DP (n = 17). Ictal-interictal SPECT subtraction was performed, and then subtracted SPECT was coregistered with brain spoiled gradient recalled (SPGR) magnetic resonance imaging (MRI). The ictal hyperperfusion on subtracted SPECT was analyzed in basal ganglia, frontal cortex, thalamus, temporal lobe, and insular cortex., Results: The incidences of ictal hyperperfusion on brain regions in DP versus Non-DP groups were 80.0% (12 of 15 patients) versus none (0 of 17), p = 0.001, chi2, in caudate nucleus; 93.3% (14 of 15) versus 47.0% (eight of 17), p = 0.005, in putamen; and 80.0% (12 of 15) versus 41.2% (seven of 17), p = 0.026, in thalamus. No significant difference of ictal hyperperfusion was found in globus pallidus, temporal lobes, insular and frontal cortices between DP and Non-DP groups. DP patients showed an earlier age at seizure onset [8.6 years (DP) vs. 15.7 years (Non-DP) (p = 0.015)] and a longer duration of seizure history [19.0 years (DP) vs. 11.9 years (Non-DP) (p = 0.015)]., Conclusions: Caudate nucleus, putamen, and thalamus were significantly related to the ictal DP during TLE seizures. Our study showed first an active involvement of the caudate nucleus in the generation of ictal DP.

The pattern formation in orientation and ocular dominance columns is one of the most investigated problems in the brain. From a known cortical structure, we build spinlike Hamiltonian models with long-range interactions of the Mexican hat type. These Hamiltonian models allow a coherent interpretation of the diverse phenomena in the visual map formation with the help of relaxation dynamics of spin systems. In particular, we explain various phenomena of self-organization in orientation and ocular dominance map formation including the pinwheel annihilation and its dependency on the columnar wave vector and boundary conditions.