Showing 5 results for Ghassemi
Mahdiyeh Sarraf Razavi, Mehdi Tehranidoost, Farnaz Ghassemi, Parivash Purabassi, Athena Taymourtash,
Volume 8, Issue 5 (September & October 2017)
Abstract
Introduction: Children with attention-deficit/hyperactivity disorder (ADHD) have some impairment in emotional relationship which can be due to problems in emotional processing. The present study investigated neural correlates of early stages of emotional face processing in this group compared with typically developing children using the Gamma Band Activity (GBA).
Methods: A total of 19 children diagnosed with ADHD (Combined type) based on DSM-IV classification were compared with 19 typically developing children matched on age, gender, and IQ. The participants performed an emotional face recognition while their brain activities were recorded using an event-related oscillation procedure.
Results: The results indicated that ADHD children compared to normal group showed a significant reduction in the gamma band activity, which is thought to reflect early perceptual emotion discrimination for happy and angry emotions (P<0.05).
Conclusion: The present study supports the notion that individuals with ADHD have some impairments in early stage of emotion processing which can cause their misinterpretation of emotional faces.
Mahdiyeh Sarraf-Razavi, Mehdi Tehrani-Doost, Farnaz Ghassemi, Mohammad Ali Nazari, Zohreh Ziatabar Ahmadi,
Volume 9, Issue 6 (November & December 2018)
Abstract
Introduction: Studies indicate that children with Attention Deficit Hyperactivity Disorder (ADHD) have deficits in social and emotional functions. It can be hypothesized that these children have some deficits in early stages of facial emotion discrimination. Based on this hypothesis, the present study investigated neural correlates of early visual processing during emotional face recognition in this group compared with typically developing children using the Event-Related Potentials (ERPs).
Methods: Nineteen boys between the ages of 7 and 11 years diagnosed with ADHD (Combined type) based on DSM-IV-TR classification were compared with 19 typically developing children matched on age and gender. The participants performed an emotional face recognition task while their brain activities were recorded using the event-related potentials procedure.
Results: A significant reduction in the Early Posterior Negativity (EPN) for happy and angry faces has been revealed in ADHD children compared to normal ones (P<0.05).
Conclusion: The present study supports the notion that individuals with ADHD have some impairments in early stage of emotion processing which can leading to their misinterpretation of emotion in faces.
Vida Mehdizadehfar, Farnaz Ghassemi, Ali Fallah,
Volume 10, Issue 5 (September & October 2019)
Abstract
Introduction: Many theories have been proposed about the etiology of autism. One is related to brain connectivity in patients with autism. Several studies have reported brain connectivity changes in autism disease. This study was performed on Electroencephalogram (EEG) studies that evaluated patients with autism, using functional brain connectivity, and compared them with typically-developing individuals.
Methods: Three scientific databases of ScienceDirect, Medline (PubMed), and BioMed Central were systematically searched through their online search engines. Comprehensive Meta-analysis software analyzed the obtained data.
Results: The systematic search led to 10 papers, in which EEG coherence was used to obtain the brain connectivity of people with autism. To determine the effect size, Cohen’s d parameter was used. In the first meta-analysis, the study of the maximum effect size was considered, and all significant effect sizes were evaluated in the second meta-analysis. The effect size was assessed using a random-effects model in both meta-analyses. The results of the first meta-analysis indicated that heterogeneity was not present among the studies (Q=13.345, P>0.1). The evaluation of all effect sizes in the second meta-analysis showed a significant lack of homogeneity among the studies (Q=56.984, P=0.0001).
Conclusion: On the whole, autism was found to be related to neural connectivity, and the present research showed the difference in the EEG coherence of people with autism and healthy people. These conclusions require further studies with more extensive data, considering different brain regions, and novel analysis techniques for assessing brain connectivity.
Hajar Ahmadieh, Farnaz Ghassemi,
Volume 13, Issue 2 (March & April 2022)
Abstract
Introduction: Alzheimer disease (AD) is the most prevalent neurodegenerative disorder and a type of dementia. About 80% of dementia in older adults is due to AD. According to multiple research articles, AD is associated with several changes in EEG signals, such as slow rhythms, reduction in complexity and functional associations, and disordered functional communication between different brain areas. This research focuses on the entropy parameter.
Methods: In this study, the keywords “Entropy,” “EEG,” and “Alzheimer” were used. In the initial search, 102 articles were found. In the first stage, after investigating the Abstracts of the articles, the number of them was reduced to 62, and upon further review of the remaining articles, the number of articles was reduced to 18. Some papers have used more than one entropy of EEG signals to compare, and some used more than one database. So, 25 entropy measures were considered in this meta-analysis. We used the Standardized Mean Difference (SMD) to find the effect size and compare the effects of AD on the entropy of the EEG signal in healthy people. Funnel plots were used to investigate the bias of meta-analysis.
Results: According to the articles, entropy seems to be a good benchmark for comparing the EEG signals between healthy people and AD people.
Conclusion: It can be concluded that AD can significantly affect EEG signals and reduce the entropy of EEG signals.
Vida Mehdizadehfar, Farnaz Ghassemi, Ali Fallah,
Volume 14, Issue 4 (July & August 2023)
Abstract
Introduction: The electroencephalography signal is well suited to calculate brain connectivity due to its high temporal resolution. When the purpose is to compute connectivity from multi-trial electroencephalography (EEG) data, confusion arises about how these trials involved in calculating the connectivity. The purpose of this paper is to study this confusing issue using simulated and experimental data.
Methods: To this end, Granger causality-based connectivity measures were considered. Using simulations, two signals were generated with known AR (auto-regressive) coefficients and then simple multivariate autoregressive (MVAR) models based on different numbers of trials were extracted. For accurate estimation of the MVAR model, the data samples should be sufficient. Two Granger causality-based connectivity, granger causality (GC) and Partial directed coherence (PDC) were estimated.
Results: Estimating connectivity corresponding to small trial numbers (5 and 10 trials) resulted in an average value of connectivity that is significantly higher and also more variable over different estimates. By increasing the number of trials, the MVAR model has fitted more appropriately to the data and the connectivity values were converged. This procedure was implemented on real EEG data. The obtained results agreed well with the findings of simulated data.
Conclusion: The results showed that the brain connectivity should calculate for each trial, and then average the connectivity values on all trials. Also, the larger the trial numbers, the MVAR model has fitted more appropriately to the data, and connectivity estimations are more reliable.
