The Effects of Acute GABA Treatment on the Functional Connectivity and Network Topology of Cortical Cultures.

γ-Aminobutyric acid (GABA) is an inhibitory transmitter, acting on receptor channels to reduce neuronal excitability in matured neural systems. However, electrophysiological responses of whole neuronal ensembles to the exposure to GABA are still unclear. We used micro-electrode arrays (MEAs) to study the effects of the increasing amount of GABA on functional network of cortical neural cultures. Then the recorded data were analyzed by the cross-covariance analysis and graph theory. Results showed that after the GABA treatment, the activity parameters of firing rate, bursting rate, bursting duration and network burst frequency in neural cultures decreased as expected. In addition, the functional connectivity also decreased in similarity, network density, and the size of the largest component. However, small-worldness was not found to be influenced by the acute GABA treatment. Our results support the position that using graph theory to evaluate the functional connectivity of neural cultures may enhance understanding of the pharmacological impact of neurotransmitters on neuronal networks.

The effect of acute glutamate treatment on the functional connectivity and network topology of cortical cultures.

Microelectrode arrays (MEAs) allow the investigation of the pharmacological and toxicological effects of chemicals on cultured neuronal networks. Understanding the functional connections between neurons and the resulting neuronal networks is important for evaluating drugs that affect synaptic transmission. Therefore, we acutely treated a mature cultured neuronal network on MEAs with accumulating amounts of glutamate and recorded their altered electrophysiology. Subsequently, a cross-covariance analysis was applied to process the spiking activity in the network and to evaluate the connections between neurons. Finally, graph theory was used to assess the functional network properties under acute glutamate treatment. Our data demonstrated that glutamate increased the similarity, connectivity weight, density, and largest-component size of the functional network. In addition, the small-world network topology was altered after glutamate treatment. Our results indicate that the graph theory can advance our understanding of the pharmacological significance of neurotransmitters on neuronal networks.

Modulation of Beta Oscillations for Implicit Motor Timing in Primate Sensorimotor Cortex during Movement Preparation.

Motor timing is an important part of sensorimotor control. Previous studies have shown that beta oscillations embody the process of temporal perception in explicit timing tasks. In contrast, studies focusing on beta oscillations in implicit timing tasks are lacking. In this study, we set up an implicit motor timing task and found a modulation pattern of beta oscillations with temporal perception during movement preparation. We trained two macaques in a repetitive visually-guided reach-to-grasp task with different holding intervals. Spikes and local field potentials were recorded from microelectrode arrays in the primary motor cortex, primary somatosensory cortex, and posterior parietal cortex. We analyzed the association between beta oscillations and temporal interval in fixed-duration experiments (500 ms as the Short Group and 1500 ms as the Long Group) and random-duration experiments (500 ms to 1500 ms). The results showed that the peak beta frequencies in both experiments ranged from 15 Hz to 25 Hz. The beta power was higher during the hold period than the movement (reach and grasp) period. Further, in the fixed-duration experiments, the mean power as well as the maximum rate of change of beta power in the first 300 ms were higher in the Short Group than in the Long Group when aligned with the Center Hit event. In contrast, in the random-duration experiments, the corresponding values showed no statistical differences among groups. The peak latency of beta power was shorter in the Short Group than in the Long Group in the fixed-duration experiments, while no consistent modulation pattern was found in the random-duration experiments. These results indicate that beta oscillations can modulate with temporal interval in their power mode. The synchronization period of beta power could reflect the cognitive set maintaining working memory of the temporal structure and attention.

Efficient coding matters in the organization of the early visual system.

Individual areas in the brain are organized into a hierarchical network as a result of evolution. Previous work indicated that the receptive fields (RFs) of individual areas have been evolved to favor metabolically efficient neural codes. In this paper, we propose that not only the RFs of individual areas, but also the organization of adjacent neurons and the hierarchical structure composed of these areas have been evolved to support efficient coding. To verify this hypothesis, we introduce a feed-forward three-layer network to simulate the early stages of human visual system. We emphasize that the network is not a purely feed-forward one since it also includes intra-layer connections, which are essential but usually ignored in the literature. Simulation results strongly reveal that (1) the obtained RFs of the simulated retinal ganglion cells (RGCs) or neurons in the lateral geniculate nucleus (LGN) and V1 simple neurons are consistent to the neurophysiological data; (2) the responses of closer RGCs are more correlated, and V1 simple neurons with similar orientations prefer to cluster together; (3) the hierarchical organization of the early visual system is beneficial for saving energy, which accords with the requirement of metabolically efficient neural coding in the process of human brain evolution.

A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking.

Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA) model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI) competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods.

Improving Generalization Based on l1-Norm Regularization for EEG-Based Motor Imagery Classification.

Multichannel electroencephalography (EEG) is widely used in typical brain-computer interface (BCI) systems. In general, a number of parameters are essential for a EEG classification algorithm due to redundant features involved in EEG signals. However, the generalization of the EEG method is often adversely affected by the model complexity, considerably coherent with its number of undetermined parameters, further leading to heavy overfitting. To decrease the complexity and improve the generalization of EEG method, we present a novel l1-norm-based approach to combine the decision value obtained from each EEG channel directly. By extracting the information from different channels on independent frequency bands (FB) with l1-norm regularization, the method proposed fits the training data with much less parameters compared to common spatial pattern (CSP) methods in order to reduce overfitting. Moreover, an effective and efficient solution to minimize the optimization object is proposed. The experimental results on dataset IVa of BCI competition III and dataset I of BCI competition IV show that, the proposed method contributes to high classification accuracy and increases generalization performance for the classification of MI EEG. As the training set ratio decreases from 80 to 20%, the average classification accuracy on the two datasets changes from 85.86 and 86.13% to 84.81 and 76.59%, respectively. The classification performance and generalization of the proposed method contribute to the practical application of MI based BCI systems.