Application of EEG Time-Varying Networks in the Evaluation of Dynamic Functional Brain Networks

Hasan

et al. 2023

CSIT

Self Cite

COVID-19 damaged the quality of sleep and mental stamina worldwide despite public health initiatives. Problems with sleep can damage health and academic performance, thus university students should know their frequency and causes. This study explored university students’ COVID-19 Anxiety, Mental Stress, and Sleep Disorders. The internet-deployed transversal analysis includes 443 Indian and Ethiopian students from April 1 to 13, 2021. After creating a Google form link, respondents received the survey via WhatsApp, E-mail, Telegram, and others. Pittsburgh Sleep Quality Index examined student sleep concerns. Descriptive and inferential studies estimated sleep disruption frequency and causes. Logistic regression and chi-squared testing found sleep problems in Indian and Ethiopian university students. The researcher examined SPSS 25 data. 54.7% had sleep difficulties. Being female [Conditional Odds Ratio = 4.32, 95 percent Confidence interval (Lower-1.807)–(Upper-10.370)], smoking [2.81, 95 percent CI (Lower-1.609)–(Upper-4.920)], spending 14 days in quarantine [1.87, 95 percent CI (Lower-1.042)–(Upper-3.373)], and having a family member with COVID-19 [1.94, 95 percent CI (Lower-1.anxiety [Conditional Odds Ratio = 4.01, 95 percent CI (Lo Ethiopian and Indian pupils sleep poorly. Female gender, smoking, 14-day quarantine, and sleep troubles were connected to COVID-19 dread, COVID-19-infected family member, viral exposure, depression, anxiety, and stress in Indian and Ethiopian university students. Interventions should increase university students’ sleep.

Section: Discussionmentioning

confidence: 99%

COVID-19 epidemic anxiety, mental stress, and sleep disorders in developing country university students

Hasan

et al. 2023

CSIT

Self Cite

“…We utilized the time-varying networks that reveal information propagation over time (40) to analyze the task-related differences between TD and ASD children.…”

Section: Time-varying Network and Degreementioning

confidence: 99%

Altered dynamic network interactions in children with ASD during face recognition revealed by time-varying EEG networks

Chen

Jiang

Lü

et al. 2023

Preprint

Background: Although the electrophysiological event-related potential (ERP) in face processing (e.g., N170) is widely accepted as a face-sensitivity biomarker that is deficient in children with autism spectrum disorders (ASD), the time-varying brain networks during face recognition are still awaiting further investigation.Methods: To explore the social deficits in ASD, especially the time-varying brain networks during face recognition, the current study analyzed the N170, cortical activity, and time-varying networks under three tasks (face-upright, face-inverted, and house-upright) in ASD and typically developing (TD) children.Results: The results revealed a smaller N170 amplitude in ASD compared with TD, along with decreased cortical activity mainly in occipitotemporal areas. Concerning the time-varying networks, the atypically stronger information flow and brain network connections across frontal, parietal, and temporal regions in ASD were reported, which reveals greater effort was exerted by ASD to obtain comparable performance to the TD children, although the amplitude of N170 was still smaller than that of the TD children.Conclusion: Different brain activation states and interaction patterns of brain regions during face-processing were discovered between ASD and TD. These findings shed light on the face-processing mechanisms in children with ASD and provide new insight for understanding the social dysfunction of ASD.

Altered dynamic network interactions in children with ASD during face recognition revealed by time-varying EEG networks

Chen,

Jiang,

et al. 2023

Cerebral Cortex

Although the electrophysiological event-related potential in face processing (e.g. N170) is widely accepted as a face-sensitivity biomarker that is deficient in children with autism spectrum disorders, the time-varying brain networks during face recognition are still awaiting further investigation. To explore the social deficits in autism spectrum disorder, especially the time-varying brain networks during face recognition, the current study analyzed the N170, cortical activity, and time-varying networks under 3 tasks (face-upright, face-inverted, and house-upright) in autism spectrum disorder and typically developing children. The results revealed a smaller N170 amplitude in autism spectrum disorder compared with typically developing, along with decreased cortical activity mainly in occipitotemporal areas. Concerning the time-varying networks, the atypically stronger information flow and brain network connections across frontal, parietal, and temporal regions in autism spectrum disorder were reported, which reveals greater effort was exerted by autism spectrum disorder to obtain comparable performance to the typically developing children, although the amplitude of N170 was still smaller than that of the typically developing children. Different brain activation states and interaction patterns of brain regions during face processing were discovered between autism spectrum disorder and typically developing. These findings shed light on the face-processing mechanisms in children with autism spectrum disorder and provide new insight for understanding the social dysfunction of autism spectrum disorder.