Showing 4 results for Maghsoudi
Dr. Nader Maghsoudi, Mr Rasoul Ghasemi, Mrs Zahra Ghaempanah, Dr. Ali Motavalizadeh Ardekani, Mrs Elahe Nooshinfar,
Volume 5, Issue 2 (Spring 2014 2014)
Abstract
Introduction: Brain-Derived Neurotrophic Factor (BDNF) and its receptor, TrkB, in the hippocampus are targets for adverse effects of stress paradigms in addition, BDNF and its receptor play key role in the pathology of brain diseases like depression. In the present study, we evaluated the possible role of hippocampal BDNF in depression during pregnancy,
Methods: To achieve the purpose, repeated restrain stress (1 or 3 hours daily for 7 days) during the last week of pregnancy was used and alteration in the gene expression of hippocampal BDNF and TrkB evaluated by semi-quantitative PCR.
Results: The results showed that in stress group the level of ACTH and Corticosterone is increased showing that our model was efficient in inducing psychological stress we also found that BDNF and TrkB expression are decreased in 3 hours stress group but not in 1 hour stress compared to control group.
Discussion: Our results imply that decrease in BDNF and its receptor could contribute in some adverse effects of stress during pregnancy such as elevation of depressive like behavior.
Arash Maghsoudi, Ahmad Shalbaf,
Volume 12, Issue 6 (November & December 2021)
Abstract
Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signals can help understand disorders, such as attention-deficit hyperactivity, dyscalculia, or autism spectrum disorder where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recognition systems rely on features of a single channel of EEG; however, the relationships between EEG channels in the form of effective brain connectivity analysis can contain valuable information. This study aims to find distinctive, effective brain connectivity features and create a hierarchical feature selection for effectively classifying mental arithmetic and baseline tasks.
Methods: We estimated effective connectivity using Directed Transfer Function (DTF), direct DTF (dDTF) and Generalized Partial Directed Coherence (GPDC) methods. These measures determine the causal relationship between different brain areas. A hierarchical feature subset selection method selects the most significant effective connectivity features. Initially, Kruskal–Wallis test was performed. Consequently, five feature selection algorithms, namely, Support Vector Machine (SVM) method based on Recursive Feature Elimination, Fisher score, mutual information, minimum Redundancy Maximum Relevance (RMR), and concave minimization and SVM are used to select the best discriminative features. Finally, the SVM method was used for classification.
Results: The obtained results indicated that the best EEG classification performance in 29 participants and 60 trials is obtained using GPDC and feature selection via concave minimization method in Beta2 (15-22Hz) frequency band with 89% accuracy.
Conclusion: This new hierarchical automated system could be helpful in the discrimination of mental arithmetic and baseline tasks from EEG signals effectively.
Sara Bagherzadeh, Keivan Maghooli, Ahmad Shalbaf, Arash Maghsoudi,
Volume 14, Issue 1 (January & February 2023)
Abstract
Introduction: Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool, which makes the processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate.
Methods: In this paper, a hybrid approach based on deep features extracted from wavelet CNNs (WCNNs) weighted layers and multiclass support vector machine (MSVM) was proposed to improve the recognition of emotional states from electroencephalogram (EEG) signals. First, EEG signals were preprocessed and converted to Time-Frequency (T-F) color representation or scalogram using the continuous wavelet transform (CWT) method. Then, scalograms were fed into four popular pre-trained CNNs, AlexNet, ResNet-18, VGG-19, and Inception-v3 to fine-tune them. Then, the best feature layer from each one was used as input to the MSVM method to classify four quarters of the valence-arousal model. Finally, the subject-independent leave-one-subject-out criterion was used to evaluate the proposed method on DEAP and MAHNOB-HCI databases.
Results: Results showed that extracting deep features from the earlier convolutional layer of ResNet-18 (Res2a) and classifying using the MSVM increased the average accuracy, precision, and recall by about 20% and 12% for MAHNOB-HCI and DEAP databases, respectively. Also, combining scalograms from four regions of pre-frontal, frontal, parietal, and parietal-occipital and two regions of frontal and parietal achieved the higher average accuracy of 77.47% and 87.45% for MAHNOB-HCI and DEAP databases, respectively.
Conclusion: Combining CNN and MSVM increased the recognition of emotion from EEG signals and the results were comparable to state-of-the art studies.
Sarieh Ghasempour, Nader Maghsoudi, Homa Manaheji, Rasoul Ghasemi, Ali Jaafarisuha, Jalal Zaringhalam,
Volume 15, Issue 5 (September & October 2024)
Abstract
Introduction: Alzheimer disease (AD) is a progressive neurodegenerative disorder that is identified by the gradual decline in memory and cognitive function. It is classified by the deposition of Aβ plaques, the build-up of intracellular neurofibrillary tangle (NFT), and neuron loss. Neurotrophic factors play a critical role in the treatment of AD. However, utilizing such neurotrophins has encountered certain difficulties and side effects. Novel technological advancements prioritize innovative dipeptide usage, which offers fewer side effects.
Methods: The present study endeavors to analyze the compound hexamethylenediamide bis-(N-monosuccinyl-glutamyl-lysine) (lab name: H-MGL), a newly discovered neurotrophin mimetic dipeptide, to alleviate memory impairment in an intracerebroventricular single dose streptozotocin (STZ)-induced Alzheimer model in rats. We arranged 4 groups: Sham and groups receiving STZ and STZ + H-MGL (1 and 2 mg/kg). The H-MGL was administered consecutively for 14 days following the STZ injection. Then, the Morris water maze test was performed.
Results: The findings suggest that administration of STZ caused a significantly increment in mean escape latency and mean traveled distance in acquisition days. H-MGL at a 1 mg/kg dosage failed to yield any notable improvement in rats compared to STZ. By contrast, 2 mg/kg of H-MGL dosage led to a significant decrease in the latency to first platform crossing and frequency of platform crossings.
Conclusion: Consequently, the findings above have engendered the notion that H-MGL partially ameliorates cognitive impairment, so it may hold promise for having low side effects to alleviate cognitive deficits in AD or potentially decrease the symptoms associated with its progression.
