Abnormal dynamic functional networks during pain-free periods: resting-state co-activation pattern analysis in primary dysmenorrhea: Abnormal dynamic functional networks in primary dysmenorrhea.

Chronic pain alters the configuration of brain functional networks. Primary dysmenorrhea (PDM) is a form of chronic visceral pain, which has been identified spatial alterations in brain functional networks using static functional connectivity analysis methods. However, the dynamics alterations of brain functional networks during pain-free periovulation phase remain unclear.

The alterations of repetitive transcranial magnetic stimulation on the energy landscape of resting-state networks differ across the human cortex.

Repetitive transcranial magnetic stimulation (rTMS) is a promising intervention tool for the noninvasive modulation of brain activity and behavior in neuroscience research and clinical settings. However, the resting-state dynamic evolution of large-scale functional brain networks following rTMS has rarely been investigated. Here, using resting-state fMRI images collected from 23 healthy individuals before (baseline) and after 1 Hz rTMS of the left frontal (FRO) and occipital (OCC) lobes, we examined the different effects of rTMS on brain dynamics across the human cortex.

Brain Network Dynamics in Women With Primary Dysmenorrhea During the Pain-Free Periovulation Phase.

The human brain is a dynamic system that shows frequency-specific features. Neuroimaging studies have shown that both healthy individuals and those with chronic pain disorders experience pain influenced by various processes that fluctuate over time. Primary dysmenorrhea (PDM) is a chronic visceral pain that disrupts the coordinated activity of brain's functional network.

Dynamic nonreversibility view of intrinsic brain organization and brain dynamic analysis of repetitive transcranial magnitude stimulation.

Intrinsic neural activities are characterized as endless spontaneous fluctuation over multiple time scales. However, how the intrinsic brain organization changes over time under local perturbation remains an open question. By means of statistical physics, we proposed an approach to capture whole-brain dynamics based on estimating time-varying nonreversibility and k-means clustering of dynamic varying nonreversibility patterns.

Frequency dependent whole-brain coactivation patterns analysis in Alzheimer's disease.

Background: The brain in resting state has complex dynamic properties and shows frequency dependent characteristics. The frequency-dependent whole-brain dynamic changes of resting state across the scans have been ignored in Alzheimer's disease (AD).

Objective: Coactivation pattern (CAP) analysis can identify different brain states.

Low-frequency repetitive transcranial magnetic stimulation alters the individual functional dynamical landscape.

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive approach to modulate brain activity and behavior in humans. Still, how individual resting-state brain dynamics after rTMS evolves across different functional configurations is rarely studied. Here, using resting state fMRI data from healthy subjects, we aimed to examine the effects of rTMS to individual large-scale brain dynamics.

Applying transcranial magnetic stimulation to rehabilitation of poststroke lower extremity function and an improvement: Individual-target TMS.

Stroke is the leading cause of disability globally in need of novel and effective methods of rehabilitation. Intermittent theta burst stimulation (iTBS) has been adopted as a Level B recommendation for lower limb spasticity in guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS). Nonetheless, the methodological differences and deficits of existing work bring about heterogenous results and therefore limit the universal clinical use of rTMS in lower extremity (LE) rehabilitation.

The longitudinal neural dynamics changes of whole brain connectome during natural recovery from poststroke aphasia.

Poststroke aphasia is one of the most dramatic functional deficits that results from direct damage of focal brain regions and dysfunction of large-scale brain networks. The reconstruction of language function depends on the hierarchical whole-brain dynamic reorganization. However, investigations into the longitudinal neural changes of large-scale brain networks for poststroke aphasia remain scarce.

Improved Functional Organization in Patients With Primary Insomnia After Individually-Targeted Transcranial Magnetic Stimulation.

Primary insomnia (PI) is among the most prevalent sleep-related disorders and has a far-reaching impact on daytime functioning. Repetitive transcranial magnetic stimulation (rTMS) has drawn attention because of its effectiveness and safety. The purpose of the current study was to detect changes in the topological organization of whole-brain functional networks and to determine their associations with the clinical treatment effects of rTMS.

FGF binding protein 3 is required for spinal cord motor neuron development and regeneration in zebrafish.

Fibroblast growth factor binding protein 3 (Fgfbp3) have been known to be crucial for the process of neural proliferation, differentiation, migration, and adhesion. However, the specific role and the molecular mechanisms of fgfbp3 in regulating the development of motor neurons remain unclear. In this study, we have investigated the function of fgfbp3 in morphogenesis and regeneration of motor neuron in zebrafish.

Altered Small-World Functional Network Topology in Patients with Optic Neuritis: A Resting-State fMRI Study.

Aim: This study investigated changes in small-world topology and brain functional connectivity in patients with optic neuritis (ON) by resting-state functional magnetic resonance imaging (rs-fMRI) and based on graph theory.

Methods: A total of 21 patients with ON (8 males and 13 females) and 21 matched healthy control subjects (8 males and 13 females) were enrolled and underwent rs-fMRI. Data were preprocessed and the brain was divided into 116 regions of interest.

Antifungal Mechanism of Dipicolinic Acid and Its Efficacy for the Biocontrol of Pear Valsa Canker.

is a fatal canker pathogen that causes significant reduction of crop yield in pear orchards. invades the trunk phloem, and is difficult to control by chemical treatment. In this work, it was found for the first time that -produced dipicolinic acid (DPA) exhibits antifungal activity against different canker pathogens, including , , , and .

Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer.

Non-coding RNAs are fundamental to the competing endogenous RNA (CeRNA) hypothesis in oncology. Previous work focused on static CeRNA networks. We construct and analyze CeRNA networks for four sequential stages of lung adenocarcinoma (LUAD) based on multi-omics data of long non-coding RNAs (lncRNAs), microRNAs and mRNAs.

The Requirement of Sox2 for the Spinal Cord Motor Neuron Development of Zebrafish.

Sex-determining region Y box 2 (Sox2), expressed in neural tissues, plays an important role as a transcription factor not only in the pluripotency and proliferation of neuronal cells but also in the opposite function of cell differentiation. Nevertheless, how Sox2 is linked to motor neuron development remains unknown. Here, we showed that Sox2 was localized in the motor neurons of spinal cord by hybridization and cell separation, which acted as a positive regulator of motor neuron development.

hNTCP‑expressing primary pig hepatocytes are a valuable tool for investigating hepatitis B virus infection and antiviral drugs.

Primary human hepatocytes (PHHs) are the 'gold standard' for investigating hepatitis B virus (HBV) infection and antiviral drugs. However, poor availability, variation between batches and ethical issues regarding PHHs limit their applications. The discovery of human sodium taurocholate co‑transporting polypeptide (hNTCP) as a functional HBV receptor has enabled the development of a surrogate model to supplement the use of PHHs.

Reinforcement learning meets minority game: Toward optimal resource allocation.

The main point of this paper is to provide an affirmative answer through exploiting reinforcement learning (RL) in artificial intelligence (AI) for eliminating herding without any external control in complex resource allocation systems. In particular, we demonstrate that when agents are empowered with RL (e.g.

A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics.

We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an "S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper.

Self-adaptation of chimera states.

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model.

A network approach to quantifying radiotherapy effect on cancer: Radiosensitive gene group centrality.

Radiotherapy plays a vital role in cancer treatment, for which accurate prognosis is important for guiding sequential treatment and improving the curative effect for patients. An issue of great significance in radiotherapy is to assess tumor radiosensitivity for devising the optimal treatment strategy. Previous studies focused on gene expression in cells closely associated with radiosensitivity, but factors such as the response of a cancer patient to irradiation and the patient survival time are largely ignored.

Cognition-related white matter integrity dysfunction in Alzheimer's disease with diffusion tensor image.

Alzheimer's disease (AD) is a worldwide progressive neurodegenerative disorder in the elderly. Previous research has indicated that Alzheimer's disease impairs white matter (WM) tracts. Anatomical and neuroimaging studies have indicated that WM tracts are associated with cognitive function.

Finite-size scaling of clique percolation on two-dimensional Moore lattices.

Clique percolation has attracted much attention due to its significance in understanding topological overlap among communities and dynamical instability of structured systems. Rich critical behavior has been observed in clique percolation on Erdős-Rényi (ER) random graphs, but few works have discussed clique percolation on finite dimensional systems. In this paper, we have defined a series of characteristic events, i.

Predicting tipping points in mutualistic networks through dimension reduction.

Complex networked systems ranging from ecosystems and the climate to economic, social, and infrastructure systems can exhibit a tipping point (a "point of no return") at which a total collapse of the system occurs. To understand the dynamical mechanism of a tipping point and to predict its occurrence as a system parameter varies are of uttermost importance, tasks that are hindered by the often extremely high dimensionality of the underlying system. Using complex mutualistic networks in ecology as a prototype class of systems, we carry out a dimension reduction process to arrive at an effective 2D system with the two dynamical variables corresponding to the average pollinator and plant abundances.

A geometrical approach to control and controllability of nonlinear dynamical networks.

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired.