Measurement of cognitive dynamics during video watching through event-related potentials (ERPs) and oscillations (EROs)

Psychodynamic-based virtual reality cognitive training system with personalized emotional arousal elements for mild cognitive impairment patients

BACKGROUND AND OBJECTIVE

Mild cognitive impairment (MCI) is a serious threat to the physical health and quality of life of the elderly, as well as a heavy burden on families and society. The current computer-based rehabilitation training ignores the role of emotions in cognitive impairment rehabilitation, making it difficult to improve patient engagement and efficiency. To address this, a psychodynamics-based cognitive rehabilitation training method with personalized emotional arousal elements was proposed using virtual reality technology.

METHODS

Our proposed method contains four training tasks, which cover (audiovisual memory, attention & processing, working memory, abstract & Logic, spatial pathfinding) and six positive emotional arousal elements (sensory feedback, achievement system, multiplayer interaction, score comparison, relaxation scenarios, and peaceful videos) to motivate participants to persist during cognitive training continuously and maintain a positive mental attitude toward training. The six emotional arousal elements were divided into two personalized combinations-full combination and half combination-based on the results of the pre-assessment and were dynamically distributed throughout both the training tasks and post-training.

RESULTS

Fifteen participants with MCI were recruited to complete the proposed experiment and validate the effectiveness of the system. They were first asked to complete two assessments (e.g., the big five scale and the positive and negative affect scale) to investigate their personalities. Based on the results of the assessments, they were provided with a full or half combination of arousal elements in the training tasks and post-training. Finally, the acceptability of the system and task experience were assessed using questionnaires. Notably, there was a significant increase in training scores for participants who completed a six-week training period (66.7%, 33.4%, and 25.0% for attention and processing, working memory, and abstraction and logic, respectively). The results show that positive emotional arousal had a positive effect on the MCI participants. The training tasks and arousal elements can improve cognitive function and enhance the confidence and engagement of participants. There were no significant differences in cognitive domain training scores between the two groups.

CONCLUSIONS

This personalized cognitive training system has the potential to serve as a convenient solution for complementary treatment of MCI.

Brain potentials related to violent video clips

The aim of the present study was to investigate whether affective video can elicit ERPs related to emotional processing. Compared with neutral video clips, violent video clips elicited delayed but amplitude-similar N1 component. The most conspicuous finding was enhanced EPN and LPP components for violent than neutral video clips. These data indicate the possibility of using affective video as stimulus to elicit ERPs and provide new evidence for processing affective stimuli, using real-life video clips with better ecological validity.

Extraction of discriminative features from EEG signals of dyslexic children; before and after the treatment

Dyslexia is a neurological disorder manifested as difficulty reading and writing. It can occur despite adequate instruction, intelligence, and intact sensory abilities. Different electroencephalogram (EEG) patterns have been demonstrated between dyslexic and healthy subjects in previous studies. This study focuses on the difference between patients before and after treatment. The main goal is to identify the subset of features that adequately discriminate subjects before and after a specific treatment plan. The treatment consists of Transcranial Direct Current Stimulation (tDCS) and occupational therapy using the BrainWare SAFARI software. The EEG signals of sixteen dyslexic children were recorded during the eyes-closed resting state before and after treatment. The preprocessing step was followed by the extraction of a wide range of features to investigate the differences related to the treatment. An optimal subset of features extracted from recorded EEG signals was determined using Principal Component Analysis (PCA) in conjunction with the Sequential Floating Forward Selection (SFFS) algorithm. The results showed that treatment leads to significant changes in EEG features like spectral and phase-related EEG features, in various regions. It has been demonstrated that the extracted subset of discriminative features can be useful for classification applications in treatment assessment. The most discriminative subset of features could classify the data with an accuracy of 92% with SVM classifier. The above result confirms the efficacy of the treatment plans in improving dyslexic children's cognitive skills.

Objective Extraction of Evoked Event-Related Oscillation from Time-Frequency Representation of Event-Related Potentials

Evoked event-related oscillations (EROs) have been widely used to explore the mechanisms of brain activities for both normal people and neuropsychiatric disease patients. In most previous studies, the calculation of the regions of evoked EROs of interest is commonly based on a predefined time window and a frequency range given by the experimenter, which tends to be subjective. Additionally, evoked EROs sometimes cannot be fully extracted using the conventional time-frequency analysis (TFA) because they may be overlapped with each other or with artifacts in time, frequency, and space domains. To further investigate the related neuronal processes, a novel approach was proposed including three steps: (1) extract the temporal and spatial components of interest simultaneously by temporal principal component analysis (PCA) and Promax rotation and project them to the electrode fields for correcting their variance and polarity indeterminacies, (2) calculate the time-frequency representations (TFRs) of the back-projected components, and (3) compute the regions of evoked EROs of interest on TFRs objectively using the edge detection algorithm. We performed this novel approach, conventional TFA, and TFA-PCA to analyse both the synthetic datasets with different levels of SNR and an actual ERP dataset in a two-factor paradigm of waiting time (short/long) and feedback (loss/gain) separately. Synthetic datasets results indicated that N2-theta and P3-delta oscillations can be stably detected from different SNR-simulated datasets using the proposed approach, but, by comparison, only one oscillation was obtained via the last two approaches. Furthermore, regarding the actual dataset, the statistical results for the proposed approach revealed that P3-delta was sensitive to the waiting time but not for that of the other approaches. This study manifested that the proposed approach could objectively extract evoked EROs of interest, which allows a better understanding of the modulations of the oscillatory responses.

Gamma Oscillations Facilitate Effective Learning in Excitatory-Inhibitory Balanced Neural Circuits

Gamma oscillation in neural circuits is believed to associate with effective learning in the brain, while the underlying mechanism is unclear. This paper aims to study how spike-timing-dependent plasticity (STDP), a typical mechanism of learning, with its interaction with gamma oscillation in neural circuits, shapes the network dynamics properties and the network structure formation. We study an excitatory-inhibitory (E-I) integrate-and-fire neuronal network with triplet STDP, heterosynaptic plasticity, and a transmitter-induced plasticity. Our results show that the performance of plasticity is diverse in different synchronization levels. We find that gamma oscillation is beneficial to synaptic potentiation among stimulated neurons by forming a special network structure where the sum of excitatory input synaptic strength is correlated with the sum of inhibitory input synaptic strength. The circuit can maintain E-I balanced input on average, whereas the balance is temporal broken during the learning-induced oscillations. Our study reveals a potential mechanism about the benefits of gamma oscillation on learning in biological neural circuits.

High-Frequency Synchronization Improves Firing Rate Contrast and Information Transmission Efficiency in E/I Neuronal Networks

High-frequency synchronization has been found in many real neural systems and is confirmed by excitatory/inhibitory (E/I) network models. However, the functional role played by it remains elusive. In this paper, it is found that high-frequency synchronization in E/I neuronal networks could improve the firing rate contrast of the whole network, no matter if the network is fully connected or randomly connected, with noise or without noise. It is also found that the global firing rate contrast enhancement can prevent the number of spikes of the neurons measured within the limited time window from being confused by noise, thereby enhancing the information encoding efficiency (quantified by entropy theory here) of the neuronal system. The mechanism of firing rate contrast enhancement is also investigated. Our work implies a possible functional role in information transmission of high-frequency synchronization in neuronal systems.

The late parietal event-related potential component is hierarchically sensitive to chunk tightness during chunk decomposition

The current study analyzed event-related potentials (ERPs) associated with visuo-spatial transformation in order to examine how "chunk tightness" affects the difficulty of chunk decomposition problems. Participants completed a Chinese character decomposition task in three conditions according to the tightness of the to-be-decomposed chunk (tight vs. medium vs. loose). Behavioral data showed that performance became worse (longer reaction time, lower accuracy) as chunk tightness increased. ERP data showed that, as chunk tightness increased, the LPC exhibited a significant decrease at posterior electrode sites. The results indicate that chunk tightness might exert its primary effect on chunk decomposition difficulty by increasing the difficulty of visuo-spatial transformation, a process linked to the parietal LPC.