Beyond the neuron: Unveiling the role of reactive astrocytes in epileptic seizure dynamics through self-organized bistability.

During epileptic seizures, brain activity and connectivity undergo dramatic changes. Brain networks transition from a balanced resting state to a hyperactive and hypersynchronous state. However, the mechanisms driving these state transitions remain unclear.

Stochastic cloning of dynamical systems with hidden variables.

We present an approach combining reservoir computing and nonlinear dynamics to replicate the behavior of stochastic systems, even when only partial observations are available. Unlike conventional RC applications, our approach systematically evaluates the conditions under which a system can be "strongly" cloned (exact trajectory prediction) versus "weakly" cloned (statistical replication), leveraging external noise excitation to infer hidden dynamics. By applying external noise and analyzing the system response, we demonstrate the feasibility of our approach both theoretically and experimentally.

Hypergraph representation of multilayer brain network enhances autism spectrum disorder detection.

We present a hypergraph-based framework for analyzing functional brain networks in children with autism spectrum disorder (ASD) using resting-state electroencephalography data. Moving beyond conventional multilayer network approaches, our method captures previously undetectable higher-order connectivity patterns through a two-stage analysis: (1) constructing multilayer networks via recurrence quantification analysis to model within- and cross-frequency interactions and (2) transforming these networks into hypergraphs to better represent complex neural relationships. Our results identify distinctive connectivity signatures in ASD, particularly in bilateral frontal regions, with hypergraph representations revealing patterns obscured in traditional analyses.

Explosive synchronization in generalized multiplex network with competitive and cooperative interlayer interactions.

Explosive synchronization represents an abrupt first-order transition to coherence in coupled dynamical systems, with significant implications for real-world networks such as neural systems, power grids, and social networks. In this study, we investigate explosive synchronization in adaptive multiplex networks of an arbitrary number of layers with the coexistence of competitive and cooperative interlayer interactions, where the dynamics of a node in one layer is influenced by the coherence of its counterparts in other layers. In addition to these interlayer interactions, our model incorporates interlayer adaptive coupling that can be simultaneously cooperative and competitive.

Toward a Biologically Plausible SNN-Based Associative Memory with Context-Dependent Hebbian Connectivity.

In this paper, we propose a spiking neural network model with Hebbian connectivity for implementing energy-efficient associative memory, whose activity is determined by input stimuli. The model consists of three interacting layers of Hodgkin-Huxley-Mainen spiking neurons with excitatory and inhibitory synaptic connections. Information patterns are stored in memory using a symmetric Hebbian matrix and can be retrieved in response to a specific stimulus pattern.

Strong and weak prediction of stochastic dynamics using reservoir computing.

We propose an approach to replicate a stochastic system and forecast its dynamics using a reservoir computing (RC). We show that such machine learning models enable the prediction of the behavior of stochastic systems in a wide range of control parameters. However, the quality of forecasting depends significantly on the training approach used for the RC.

Neural Correlates of Social Touch Processing: An fMRI Study on Brain Functional Connectivity.

Background: The significance of tactile stimulation in human social development and personal interaction is well documented; however, the underlying cerebral processes remain under-researched. This study employed functional magnetic resonance imaging (fMRI) to investigate the neural correlates of social touch processing, with a particular focus on the functional connectivity associated with the aftereffects of touch.

Methods: A total of 27 experimental subjects were recruited for the study, all of whom underwent a 5-minute calf and foot massage prior to undergoing resting-state fMRI.

Novel efficient reservoir computing methodologies for regular and irregular time series classification.

Time series is a data structure prevalent in a wide range of fields such as healthcare, finance and meteorology. It goes without saying that analyzing time series data holds the key to gaining insight into our day-to-day observations. Among the vast spectrum of time series analysis, time series classification offers the unique opportunity to classify the sequences into their respective categories for the sake of automated detection.

Differential Hemodynamic Responses to Motor and Tactile Imagery: Insights from Multichannel fNIRS Mapping.

Tactile and motor imagery are crucial components of sensorimotor functioning and cognitive neuroscience research, yet the neural mechanisms of tactile imagery remain underexplored compared to motor imagery. This study employs multichannel functional near-infrared spectroscopy (fNIRS) combined with image reconstruction techniques to investigate the neural hemodynamics associated with tactile (TI) and motor imagery (MI). In a study of 15 healthy participants, we found that MI elicited significantly greater hemodynamic responses (HRs) in the precentral area compared to TI, suggesting the involvement of different cortical areas involved in two different types of sensorimotor mental imagery.

Yes or no? A study of ErrPs in the "guess what I am thinking" paradigm with stimuli of different visual content.

Error-related potentials (ErrPs) have attracted attention in part because of their practical potential for building brain-computer interface (BCI) paradigms. BCIs, facilitating direct communication between the brain and machines, hold great promise for brain-AI interaction. Therefore, a comprehensive understanding of ErrPs is crucial to ensure reliable BCI outcomes.

Review on the Use of Brain Computer Interface Rehabilitation Methods for Treating Mental and Neurological Conditions.

This review provides a comprehensive examination of recent developments in both neurofeedback and brain-computer interface (BCI) within the medical field and rehabilitation. By analyzing and comparing results obtained with various tools and techniques, we aim to offer a systematic understanding of BCI applications concerning different modalities of neurofeedback and input data utilized. Our primary objective is to address the existing gap in the area of meta-reviews, which provides a more comprehensive outlook on the field, allowing for the assessment of the current landscape and developments within the scope of BCI.

Tactile versus motor imagery: differences in corticospinal excitability assessed with single-pulse TMS.

Tactile Imagery (TI) remains a fairly understudied phenomenon despite growing attention to this topic in recent years. Here, we investigated the effects of TI on corticospinal excitability by measuring motor evoked potentials (MEPs) induced by single-pulse transcranial magnetic stimulation (TMS). The effects of TI were compared with those of tactile stimulation (TS) and kinesthetic motor imagery (kMI).

Self-organized bistability on globally coupled higher-order networks.

Self-organized bistability (SOB) stands as a critical behavior for the systems delicately adjusting themselves to the brink of bistability, characterized by a first-order transition. Its essence lies in the inherent ability of the system to undergo enduring shifts between the coexisting states, achieved through the self-regulation of a controlling parameter. Recently, SOB has been established in a scale-free network as a recurrent transition to a short-living state of global synchronization.

SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation.

The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics.

Studying perceptual bias in favor of the from-above Necker cube perspective in a goal-directed behavior.

When viewing a completely ambiguous image, different interpretations can switch involuntarily due to internal top-down processing. In the case of the Necker cube, an entirely ambiguous stimulus, observers often display a bias in perceptual switching between two interpretations based on their perspectives: one with a from-above perspective (FA) and the other with a from-below perspective (FB). Typically, observers exhibit a priori top-down bias in favor of the FA interpretation, which may stem from a statistical tendency in everyday life where we more frequently observe objects from above.

Topology switching during window thresholding fMRI-based functional networks of patients with major depressive disorder: Consensus network approach.

We present a novel method for analyzing brain functional networks using functional magnetic resonance imaging data, which involves utilizing consensus networks. In this study, we compare our approach to a standard group-based method for patients diagnosed with major depressive disorder (MDD) and a healthy control group, taking into account different levels of connectivity. Our findings demonstrate that the consensus network approach uncovers distinct characteristics in network measures and degree distributions when considering connection strengths.

Investigating the influence of functional electrical stimulation on motor imagery related μ-rhythm suppression.

Background: Motor Imagery (MI) is a well-known cognitive technique that utilizes the same neural circuits as voluntary movements. Therefore, MI practice is widely used in sport training and post-stroke rehabilitation. The suppression of the μ-rhythm in electroencephalogram (EEG) is a conventional marker of sensorimotor cortical activation during motor imagery.

Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex Increases Posterior Theta Rhythm and Reduces Latency of Motor Imagery.

Experiments show activation of the left dorsolateral prefrontal cortex (DLPFC) in motor imagery (MI) tasks, but its functional role requires further investigation. Here, we address this issue by applying repetitive transcranial magnetic stimulation (rTMS) to the left DLPFC and evaluating its effect on brain activity and the latency of MI response. This is a randomized, sham-controlled EEG study.

Toward interpretability of machine learning methods for the classification of patients with major depressive disorder based on functional network measures.

We address the interpretability of the machine learning algorithm in the context of the relevant problem of discriminating between patients with major depressive disorder (MDD) and healthy controls using functional networks derived from resting-state functional magnetic resonance imaging data. We applied linear discriminant analysis (LDA) to the data from 35 MDD patients and 50 healthy controls to discriminate between the two groups utilizing functional networks' global measures as the features. We proposed the combined approach for feature selection based on statistical methods and the wrapper-type algorithm.

Event-Related Desynchronization Induced by Tactile Imagery: an EEG Study.

It is well known that both hand movements and mental representations of movement lead to event-related desynchronization (ERD) of the electroencephalogram (EEG) recorded over the corresponding cortical motor areas. However, the relationship between ERD in somatosensory cortical areas and mental representations of tactile sensations is not well understood. In this study, we employed EEG recordings in healthy humans to compare the effects of real and imagined vibrotactile stimulation of the right hand.

Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI.

Action observation (AO) is widely used as a post-stroke therapy to activate sensorimotor circuits through the mirror neuron system. However, passive observation is often considered to be less effective and less interactive than goal-directed movement observation, leading to the suggestion that observation of goal-directed actions may have stronger therapeutic potential, as goal-directed AO has been shown to activate mechanisms for monitoring action errors. Some studies have also suggested the use of AO as a form of Brain-computer interface (BCI) feedback.

Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach.

Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed.