Acoustic Neurofeedback Increases Beta ERD During Mental Rotation Task

The effects of transcranial magnetic stimulation on cognitive flexibility among undergraduates with insomnia symptoms: A prospective, single-blind, randomized control trial

Backgrounds

Repetitive transcranial magnetic stimulation(rTMS) has been widely used in the treatment of insomnia, but there is a lack of research on whether this method could enhance the cognitive flexibility(CF) of individuals with insomnia symptoms.

Objectives

To investigate the effects of rTMS on the CF of undergraduates with insomnia symptoms.

Methods

29 participants were randomly assigned into Active group(n = 15) and Sham group(n = 14), receiving 1 Hz rTMS interventions targeting the left dorsolateral prefrontal cortex for 2 weeks, comprising 10 sessions (active vs sham stimulation). Sleep quality and CF were assessed using the Pittsburgh Sleep Quality Index(PSQI), Insomnia Severity Index(ISI), Cognitive Flexibility Inventory(CFI), and the Number-Letter Task (N-L task) at baseline(T0), post-intervention(T1), and 8 weeks' follow-up(T2), and event-related potential(ERP) data during the N-L task were recorded.

Results

Following the intervention, compared to the Sham group, the ISI and PSQI scores in the Active group were significantly decreased, and the CFI score was significantly increased (P < 0.01); the results of the N-L task indicated that at T1, the switch cost of reaction time and accuracy for the Sham group were significantly higher than those for the Active group(P < 0.05). ERP analysis indicated that at T2, under switch conditions, the amplitude of the frontal area P2 in the Active group was significantly greater than that in the Sham group, and the beta-band ERD at parietal region in the Active group was significantly lower than that in the Sham group (P < 0.05).

Conclusions

rTMS could improve sleep quality and enhance the CF of undergraduates with insomnia symptoms.

Clinical Trials Registration

The effect of transcranial magnetic stimulation on cognitive flexibility in college students with insomnia (ChiCTR2400081263) URL: https://www.chictr.org.cn/showproj.html?proj=202951.

Brain Punch: K-1 Fights Affect Brain Wave Activity in Professional Kickboxers

BACKGROUND

Kickboxing is a popular striking combat sport, and K-1 is a type of kickboxing. Direct head blows can cause significant long-term injury and affect brain wave activity.

OBJECTIVES

We aim to compare the changes in brain wave activities of fighters during a K-1 kickboxing contest to those in a control group, who were striking a punching bag and were not hit by another K-1 athlete.

METHODS

A total of 100 professional Polish K-1 kickboxers were split evenly into experimental (n = 50, age 25.5 ± 4.63 years) and control (n = 50, age 26.6 ± 5.22 years) groups. We used quantitative electroencephalography (QEEG) to assess the spectrum of brain wave activity (delta, theta, alpha, sensorimotor rhythm (SMR), beta-1 and beta-2) before and after an intervention (experimental: K-1 contest, control: simulated contest), with eyes open and then closed. The number of direct blows to the head was also recorded for all bouts. Comparative and statistical analyses between selected variables were performed.

RESULTS

K-1 fighters showed elevated baseline brain activity for the entire delta band (p < 0.001). There was significant variation in brain activity among the experimental group following the intervention and compared with the control group for all wave types (p < 0.001). No significant variation in activity was found in the control group. The number of direct head blows was positively correlated with brain activity, at delta and beta-2 wave frequencies.

CONCLUSIONS

K-1 kickboxing is associated with detectable changes in brain wave activity. It is presently unclear what the long-term effects of these changes in brain wave activities are, and longitudinal studies are necessary to study the brain health of kickboxers.

Estimating person-specific neural correlates of mental rotation: A machine learning approach

Using neurophysiological measures to model how the brain performs complex cognitive tasks such as mental rotation is a promising way towards precise predictions of behavioural responses. The mental rotation task requires objects to be mentally rotated in space. It has been used to monitor progressive neurological disorders. Up until now, research on neural correlates of mental rotation have largely focused on group analyses yielding models with features common across individuals. Here, we propose an individually tailored machine learning approach to identify person-specific patterns of neural activity during mental rotation. We trained ridge regressions to predict the reaction time of correct responses in a mental rotation task using task-related, electroencephalographic (EEG) activity of the same person. When tested on independent data of the same person, the regression model predicted the reaction times significantly more accurately than when only the average reaction time was used for prediction (bootstrap mean difference of 0.02, 95% CI: 0.01-0.03, p < .001). When tested on another person's data, the predictions were significantly less accurate compared to within-person predictions. Further analyses revealed that considering person-specific reaction times and topographical activity patterns substantially improved a model's generalizability. Our results indicate that a more individualized approach towards neural correlates can improve their predictive performance of behavioural responses, particularly when combined with machine learning.

Decreased Event-Related Desynchronization of Mental Rotation Tasks in Young Tibetan Immigrants

The present study aimed to explore the cortical activity underlying mental rotation in high-altitude immigrants via the event-related desynchronization (ERD), the electroencephalogram time–frequency analysis, and source localization based on electroencephalographic data. When compared with the low-altitude individuals, the reaction time of mental rotation tasks was significantly slower in immigrants who had lived in high-altitude areas for 3 years. The time–frequency analysis showed that the alpha ERD and the beta ERD within the time window (400–700 ms) were decreased during the mental rotation tasks in these immigrants. The decreased ERD was observed at the parietal–occipital regions within the alpha band and at the central–parietal regions within the beta band. The decreased ERD might embody the sensorimotor-related cortical activity from hypoxia, which might be involved in cognitive control function in high-altitude immigrants, which provided insights into the neural mechanism of spatial cognition change on aspect of embodied cognition due to high-altitude exposure.

Improving Debt Literacy by 2/3 Through Four Simple Infographics Requires Numeracy and Not Focusing on Negatives of Debt

Borrowing behavior may be more resistant to formal educational treatments than other financial behaviors. In order to study the process and results of infographics-based debt education, we used eye tracking technology (SMI RED 500 Hz) to monitor the oculomotor behavior of 108 participants (68 females) aged 18 to 60 who were shown 4 infographics. The study used an experimental design with repeated measures and an internal comparison group. We also used scales of debt literacy and a set of information literacy scales: numerical, graph, and linguistic. The results confirm that short-term infographics-based debt education can improve debt literacy significantly. The difference in processing the educational contents that were not known to participants before the educational session suggests that participants with better information literacy make more considerable debt literacy progress. Specifically, we found that numerical literacy is a significant mediator of debt education results, depending on the initial level of debt literacy; this relation is moderated by the focus of visual attention on negatives of debt. We found no significant relationship between debt literacy education results and those of graph and linguistic literacy.

Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools

The electroencephalographic activity of particular brain areas during the decision making process is still little known. This paper presents results of experiments on the group of 30 patients with a wide range of psychiatric disorders and 41 members of the control group. All subjects were performing the Iowa Gambling Task that is often used for decision process investigations. The electroencephalographical activity of participants was recorded using the dense array amplifier. The most frequently active Brodmann Areas were estimated by means of the photogrammetry techniques and source localization algorithms. The analysis was conducted in the full frequency as well as in alpha, beta, gamma, delta, and theta bands. Next the mean electric charge flowing through each of the most frequently active areas and for each frequency band was calculated. The comparison of the results obtained for the subjects and the control groups is presented. The difference in activity of the selected Brodmann Areas can be observed in all variants of the task. The hyperactivity of amygdala is found in both the patients and the control group. It is noted that the somatosensory association cortex, dorsolateral prefrontal cortex, and primary visual cortex play an important role in the decision-making process as well. Some of our results confirm the previous findings in the fMRI experiments. In addition, the results of the electroencephalographic analysis in the broadband as well as in specific frequency bands were used as inputs to several machine learning classifiers built in Azure Machine Learning environment. Comparison of classifiers' efficiency is presented to some extent and finding the most effective classifier may be important for planning research strategy toward finding decision-making biomarkers in cortical activity for both healthy people and those suffering from psychiatric disorders.

Azure Machine Learning tools efficiency in the electroencephalographic signal P300 standard and target responses classification

The Event-Related Potentials were investigated on a group of 70 participants using the dense array electroencephalographic amplifier with photogrammetry geodesic station. The source localisation was computed for each participant. The activity of brodmann areas (BAs) involved in the brain cortical activity of each participant was measured. Then the mean electric charge flowing through particular areas was calculated. The five different machine learning tools (logistic regression, boosted decision tree, Bayes point machine, classic neural network and averaged perceptron classifier) from the Azure ecosystem were trained, and their accuracy was tested in the task of distinguishing standard and target responses in the experiment. The efficiency of each tool was compared, and it was found out that the best tool was logistic regression and the boosted decision tree in our task. Such an approach can be useful in eliminating somatosensory responses in experimental psychology or even in establishing new communication protocols with mildly mentally disabled subjects.