Using Partial Directed Coherence to Study Alpha-Band Effective Brain Networks during a Visuospatial Attention Task

Preliminary Evidence of EEG Connectivity Changes during Self-Objectification of Workers

Economic objectification is a form of dehumanization in which workers are treated as tools for enhancing productivity. It can lead to self-objectification in the workplace, which is when people perceive themselves as instruments for work. This can cause burnout, emotional drain, and a modification of self-perception that involves a loss of human attributes such as emotions and reasoning while focusing on others' perspectives for evaluating the self. Research on workers self-objectification has mainly analyzed the consequences of this process without exploring the brain activity that underlies the individual's experiences of self-objectification. Thus, this project explores the electroencephalographic (EEG) changes that occur in participants during an economic objectifying task that resembled a job in an online store. After the task, a self-objectification questionnaire was applied and its resulting index was used to label the participants as self-objectified or non-self-objectified. The changes over time in EEG event-related synchronization (ERS) and partial directed coherence (PDC) were calculated and compared between the self-objectification groups. The results show that the main differences between the groups in ERS and PDC occurred in the beta and gamma frequencies, but only the PDC results correlated with the self-objectification group. These results provide information for further understanding workers' self-objectification. These EEG changes could indicate that economic self-objectification is associated with changes in vigilance, boredom, and mind-wandering.

Therapeutic Effects of Transcranial Magnetic Stimulation on Visuospatial Neglect Revealed With Event-Related Potentials

This study aimed to investigate changes in attention processing after low-frequency repetitive transcranial magnetic stimulation (rTMS) over the left posterior parietal cortex to better understand its role in visuospatial neglect (VSN) rehabilitation. The current study included 10 subacute stroke patients with VSN consecutively recruited from the inpatient stroke rehabilitation center at Xuanwu Hospital (the teaching hospital affiliated with Capital Medical University) between March and November 2019. All patients performed a battery of tasks (including line bisection, line cancellation, and star cancellation tests) two weeks before treatment and at the beginning and end of treatment; the attentive components of the test results were analyzed. In addition, low-frequency rTMS was used to stimulate the left posterior parietal cortex for 14 days and event-related potential data were collected before and after the stimulation. Participants were evaluated using a target-cue paradigm and pencil-paper tests. No significant differences were detected on the battery of tasks before rTMS. However, we found that rTMS treatment significantly improved the response times and accuracy rates of patients with VSN. After rTMS, the treatment side (left) amplitude of P300 following an event-related potential was higher than that before treatment (left target, p = 0.002; right target, p = 0.047). Thus, our findings suggest that rTMS may be an effective treatment for VSN. The observed increase in event-related potential amplitude supports the hypothesized compensational role of the contralesional hemisphere in terms of residual performance. Our results provide electrophysiological evidence that may help determine the mechanisms mediating the therapeutic effects of rTMS.

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.