Brain Synchronization and Multivariate Autoregressive (MVAR) Modeling in Cognitive Neurodynamics

Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine

In response to the current issues of one-sided effective feature extraction and low classification accuracy in multi-class motor imagery recognition, this study proposes an Electroencephalogram (EEG) signal recognition method based on multi-domain feature fusion and optimized multi-kernel extreme learning machine. Firstly, the EEG signals are preprocessed using the Improved Comprehensive Ensemble Empirical Mode Decomposition (ICEEMD) algorithm combined with the Pearson correlation coefficient to eliminate noise and interference. Secondly, multivariate autoregressive (MVAR) model, wavelet packet decomposition, and Riemannian geometry methods are used to extract features from the time domain, frequency domain, and spatial domain, respectively, to construct a joint time-frequency-space feature vector. Subsequently, kernel principal component analysis (KPCA) is employed to fuse and reduce the dimensionality of the joint features, resulting in a reduced-dimensional fused feature vector. Finally, these feature vectors are input into a Radius-incorporated multi-kernel extreme learning machine (RIO-MKELM) for classification. The experimental results indicate that through multi-domain feature fusion and the incorporation of radius in a multi-kernel extreme learning machine, feature selection can be performed more effectively, eliminating redundant or irrelevant features and retaining the most useful information for classification. This approach enhances classification accuracy and other evaluation metrics, with the final classification accuracy reaching 95.49%, sensitivity at 97.88%, specificity at 98.12%, recall at 97.88%, and F1 Score at 96.67%. The findings of this study are of significant importance for the development and practical application of brain-computer interface (BCI) systems.

Connectome-based prediction of functional impairment in experimental stroke models

Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the neuroVIISAS rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication.

Asymmetry of Directed Brain Connectivity at Birth in Low-Risk Full-Term Newborns

PURPOSE

Functional connectivity hubs were previously identified at the source level in low-risk full-term newborns by high-density electroencephalography (HD-EEG). However, the directionality of information flow among hubs remains unclear. The aim of this study was to study the directionality of information flow among source level hubs in low-risk full-term newborns using HD-EEG.

METHODS

A retrospective analysis of HD-EEG collected from a prospective study. Subjects included 112 low-risk full-term (37-41 weeks' gestation) newborns born in a large delivery center and studied within 72 hours of life by HD-EEG. The directionality of information flow between hubs at the source level was quantified using the partial directed coherence in the delta frequency band. Descriptive statistics were used to identify the maximum and minimum information flow. Differences in information flow between cerebral hemispheres were assessed using Student t-test.

RESULTS

There was higher information flow from the left hemisphere to the right hemisphere hubs (p < 0.05, t-statistic = 2). The brainstem had the highest information inflow and lowest outflow among all the hubs. The left putamen received the lowest information, and the right pallidum had the highest information outflow to other hubs.

CONCLUSIONS

In low-risk full-term newborns, there is a significant information flow asymmetry already present, with left hemisphere dominance at birth. The relationship between these findings and the more prevalent left hemisphere dominance observed in full-term newborns, particularly in relation to language, warrants further study.

Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

Intracranial Directed Connectivity Links Subregions of the Prefrontal Cortex to Major Depression

Understanding the neural basis of major depressive disorder (MDD) is vital to guiding neuromodulatory treatments. The available evidence supports the hypothesis that MDD is fundamentally a disease of cortical disinhibition, where breakdowns of inhibitory neural systems lead to diminished emotion regulation and intrusive ruminations. Recent research also points towards network changes in the brain, especially within the prefrontal cortex (PFC), as primary sources of MDD etiology. However, due to limitations in spatiotemporal resolution and clinical opportunities for intracranial recordings, this hypothesis has not been directly tested. We recorded intracranial EEG from the dorsolateral (dlPFC), orbitofrontal (OFC), and anterior cingulate cortices (ACC) in neurosurgical patients with MDD. We measured daily fluctuations in self-reported depression severity alongside directed connectivity between these PFC subregions. We focused primarily on delta oscillations (1-3 Hz), which have been linked to GABAergic inhibitory control and intracortical communication. Depression symptoms worsened when connectivity within the left vs. right PFC became imbalanced. In the left hemisphere, all directed connectivity towards the ACC, from the dlPFC and OFC, was positively correlated with depression severity. In the right hemisphere, directed connectivity between the OFC and dlPFC increased with depression severity as well. This is the first evidence that delta oscillations flowing between prefrontal subregions transiently increase intensity when people are experiencing more negative mood. These findings support the overarching hypothesis that MDD worsens with prefrontal disinhibition.

Detection of quadratic phase coupling by cross-bicoherence and spectral Granger causality in bifrequencies interactions

Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions.

Reduced Prefrontal-Thalamic Theta Flow During Working Memory Retrieval in APP/PS1 Mice

Background

Working memory deficits in Alzheimer's disease (AD) are linked to impairments in the retrieval of stored memory information. However, research on the mechanism of impaired working memory retrieval in Alzheimer's disease is still lacking.

Objective

The medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) are involved in memory retrieval. The purpose of this study is to investigate the functional interactions and information transmission between mPFC and MD in the AD model.

Methods

We recorded local field potentials from mPFC and MD while the mice (APP/PS1 transgenic model and control) performed a T-maze spatial working memory task. The temporal dynamics of oscillatory activity and bidirectional information flow between mPFC and MD were assessed during the task phases.

Results

We mainly found a significant decrease in theta flow from mPFC to MD in APP/PS1 mice during retrieval.

Conclusions

Our results indicate an important role of the mPFC-MD input for retrieval and the disrupted information transfer from mPFC to MD may be the underlying mechanism of working memory deficits in APP/PS1 mice.

[Determining the risk of maladaptation using an electroencephalogram to prevent mental strain]

OBJECTIVE

To identify criteria for electroencephalogram (EEG) synchronization in patients with post-traumatic stress disorder (PTSD) exhibiting high levels of functional stress and signs of maladaptation.

MATERIAL AND METHODS

Two groups of male subjects aged 23-38 years were examined: a group of subjects receiving therapy for PTSD at a medical center; and a group of healthy subjects who regularly practiced psychophysical relaxation sessions. EEG, an innovative method for analyzing brain synchronizing currents, and the Spielberger State-Trait Anxiety Inventory were used.

RESULTS

Criteria for the formation of patterns of synchronization of neural networks were found in subjects of the therapy group, who have a high level of psychofunctional stress and signs of maladaptation against the background of PTSD in combination with severe anxiety and impaired cognitive abilities.

CONCLUSION

Alpha wave synchronization analysis can be used to more accurately diagnose the level of psychofunctional stress in individuals at risk of developing psychophysical disorders. The results of the work suggest the use of technologies that increase the ability to synchronize brain biocurrents to develop programs for correcting psychophysical status, such as relaxation exercises.

Connectome-based prediction of functional impairment in experimental stroke models

Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the neuroVIISAS rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication.

Directional prefrontal-thalamic information flow is selectively required during spatial working memory retrieval

Introduction

Spatial working memory is a kind of short-term memory that allows temporarily storing and manipulating spatial information. Evidence suggests that spatial working memory is processed through three distinctive phases: Encoding, maintenance, and retrieval. Though the medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) are involved in memory retrieval, how the functional interactions and information transfer between mPFC and MD remains largely unclear.

Methods

We recorded local field potentials (LFPs) from mPFC and MD while mice performed a spatial working memory task in T-maze. The temporal dynamics of functional interactions and bidirectional information flow between mPFC and MD was quantitatively assessed by using directed transfer function.

Results

Our results showed a significantly elevated information flow from mPFC to MD, varied in time and frequency (theta in particular), accompanying successful memory retrieval.

Discussion

Elevated theta information flow, a feature that was absent on error trials, indicates an important role of the directional information transfer from mPFC to MD for memory retrieval.