Directed connectivity in theta networks supports action-effect integration.

The ability to plan and carry out goal-directed behavior presupposes knowledge about the contingencies between movements and their effects. Ideomotor accounts of action control assume that agents integrate action-effect contingencies by creating action-effect bindings, which associate movement patterns with their sensory consequences. However, the neurophysiological underpinnings of action-effect binding are not yet well understood.

Neural mechanisms of adaptive behavior: Dissociating local cortical modulations and interregional communication patterns.

Adaptive behavior is based on flexibly managing and integrating perceptual and motor processes, and the reconfiguration thereof. Such adaptive behavior is also relevant during inhibitory control. Although research has demonstrated local activity modulations in theta and alpha frequency bands during behavioral adaptation, the communication of brain regions is insufficiently studied.

Neurophysiological dynamics of metacontrol states: EEG insights into conflict regulation.

Understanding the neural mechanisms underlying metacontrol and conflict regulation is crucial for insights into cognitive flexibility and persistence. This study employed electroencephalography (EEG), EEG-beamforming and directed connectivity analyses to explore how varying metacontrol states influence conflict regulation at a neurophysiological level. Metacontrol states were manipulated by altering the frequency of congruent and incongruent trials across experimental blocks in a modified flanker task, and both behavioral and electrophysiological measures were analyzed.

Neural connectivity patterns explain why adolescents perceive the world as moving slow.

That younger individuals perceive the world as moving slower than adults is a familiar phenomenon. Yet, it remains an open question why that is. Using event segmentation theory, electroencephalogram (EEG) beamforming and nonlinear causal relationship estimation using artificial neural network methods, we studied neural activity while adolescent and adult participants segmented a movie.

Interactions of catecholamines and GABA+ in cognitive control: Insights from EEG and H-MRS.

Catecholamines and amino acid transmitter systems are known to interact, the exact links and their impact on cognitive control functions have however remained unclear. Using a multi-modal imaging approach combining EEG and proton-magnetic resonance spectroscopy (H-MRS), we investigated the effect of different degrees of pharmacological catecholaminergic enhancement onto theta band activity (TBA) as a measure of interference control during response inhibition and execution. It was central to our study to evaluate the predictive impact of in-vivo baseline GABA+ concentrations in the striatum, the anterior cingulate cortex (ACC) and the supplemental motor area (SMA) of healthy adults under varying degrees of methylphenidate (MPH) stimulation.

Response stopping under conflict: The integrative role of representational dynamics associated with the insular cortex.

Coping with distracting inputs during goal-directed behavior is a common challenge, especially when stopping ongoing responses. The neural basis for this remains debated. Our study explores this using a conflict-modulation Stop Signal task, integrating group independent component analysis (group-ICA), multivariate pattern analysis (MVPA), and EEG source localization analysis.

Neurophysiological effective network connectivity supports a threshold-dependent management of dynamic working memory gating.

To facilitate goal-directed actions, effective management of working memory (WM) is crucial, involving a hypothesized WM "gating mechanism." We investigate the underlying neural basis through behavioral modeling and connectivity assessments between neuroanatomical regions linked to theta, alpha, and beta frequency bands. We found opposing, threshold-dependent mechanisms governing WM gate opening and closing.

Temporal-spatial convolutional residual network for decoding attempted movement related EEG signals of subjects with spinal cord injury.

Brain Computer Interface (BCI) offers a promising approach to restoring hand functionality for people with cervical spinal cord injury (SCI). A reliable classification of brain activities based on appropriate flexibility in feature extraction could enhance BCI systems performance. In the present study, based on convolutional layers with temporal-spatial, Separable and Depthwise structures, we develop Temporal-Spatial Convolutional Residual Network)TSCR-Net(and Temporal-Spatial Convolutional Iterative Residual Network)TSCIR-Net(structures to classify electroencephalogram (EEG) signals.

Bypassing the volume conduction effect by multilayer neural network for effective connectivity estimation.

Differentiation of real interactions between different brain regions from spurious ones has been a challenge in neuroimaging researches. While using electroencephalographic data, those spurious interactions are mostly caused by the volume conduction (VC) effect between the recording sites. In this study, we address the problem by jointly modeling the causal relationships among brain regions and the mixing effects of volume conduction.

nCREANN: Nonlinear Causal Relationship Estimation by Artificial Neural Network; Applied for Autism Connectivity Study.

Quantifying causal (effective) interactions between different brain regions are very important in neuroscience research. Many conventional methods estimate effective connectivity based on linear models. However, using linear connectivity models may oversimplify the functions and dynamics of the brain.

Estimation of effective connectivity using multi-layer perceptron artificial neural network.

Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output.

Investigation of changes in EEG complexity during memory retrieval: the effect of midazolam.

The aim of this study is applying nonlinear methods to assess changes in brain dynamics in a placebo-controlled study of midazolam-induced amnesia. Subjects injected with saline and midazolam during study, performed old/new recognition memory tests with EEG recording. Based on previous studies, as midazolam causes anterograde amnesia, we expected that midazolam would affect the EEG's degree of complexity.