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Showing 21 results for Electroencephalography
Fereshteh Ghadiri, Ali Gorji,
Volume 5, Issue 3 (7-2017)
Abstract
Introduction: Considering the prevalence of epilepsy and its great impact on patients’ lives, diverse diagnostic and therapeutic techniques have been developed. Long term monitoring (LTM), simultaneous recordings of seizure attack and electroencephalograms, facilitates achieving definite diagnosis and appropriate treatment. Conclusion: Therefore, understanding the foundations of this technique helps neurologists and other interested parties to apply it appropriately.
Parviz Bahrami,
Volume 6, Issue 3 (7-2018)
Abstract
While frontal lobe epilepsy accounts for only 10-20% of patients in surgical series, the prevalence in non-surgical cohorts is probably higher. Frontal lobe epilepsy (FLE) probably represents 20-30% of partial seizures.
Clinical diagnosis:
The seizures which most of the time occur without warning, are often short and are followed by very rapid recovery. They frequently occur from sleep, and may occur in clusters of 5-6 or more per night, usually with partial recovery between, but status epilepticus is also common.
Seizure manifestation:
The seizure semiology is dependent on the area of cortex activated during a seizure and therefore can give important clues as to the presumed epileptogenic zone.
Frontal lobe seizure semiology with predominantly positive motor symptoms can be grouped into three main categories:
- Frontal clonic seizures
- Bilateral asymmetrical tonic seizure
- Complex motor seizure
Rarer seizure types include: seizures characterized by brief lapses of awareness, akinetic seizures, aphasic seizures or seizures characterized by early head version without loss of awareness.
Electroencephalography:
Interictal EEG recordings are often challenging and it is reported that up to 40% of patients with FLE do not have Interictal epileptiform discharges. The yield of prolonged video EEG recordings and careful review of EEG samples with closely spaced midline electrodes may be of higher yield.
Imaging:
CT scan, MRI, PET and SPECT are used for determination of the lesion or abnormality. MRI can detect small area of dysplasia and heterotopia.
Treatment:
The pharmacological treatment of FLE is as for other focal epilepsies. There are no good comparative drug trials specific to FLE. Surgery is less successful than for TLE with complete remission after focal resection in only 20-40%, even in the most highly selected cases.
Emad Aazr, Imanollah Bigdeli, Ali Ghanaei Chamanabad,
Volume 7, Issue 2 (4-2019)
Abstract
Introduction: Alpha frequency band in the range of 8-12 Hz is associated with cognitive functions, such as creativity. The aim of this study was to examine the activity of the alpha frequency band in two divergent and convergent thinking positions. Materials and Methods: In accordance to the available sampling and voluntary participation, thirty-eight postgraduate students of the Ferdowsi University of Mashhad (19 males and 19 female) were chosen. Two subjects were excluded from the analysis due to artifacts in their brain waves. This study was a quasi-experimental research with repeated measures. The EEG was recorded during the performance of four tasks (alternative uses test, counting the snakes, counting numbers test, and Missionaries and Cannibals task) in two divergent and convergent thinking. Results: The findings revealed that there is no significant difference between the activity of alpha waves in the left and right hemispheres in divergent thinking as well as in convergent thinking. In addition, the results of the analysis of variance of repeated measures indicated that in divergent thinking activity of alpha waves in the temporal, central and frontal areas were synchronized, while in the convergent thinking position de-synchronization of alpha waves was observed. Conclusion: Alpha band power changes in two divergent and convergent thinking positions represented of different functional mechanisms of alpha waves in these two thinking positions in different regions of the brain.
Davoud Sadeh, Hamidreza Saeednia, Peter Steidl, Kambiz Heidarzadeh,
Volume 7, Issue 3 (7-2019)
Abstract
Introduction: The purpose of this study was to investigate the neural effects of brand social responsibility (BSR) on consumer behavior. In the version of third marketing, consideration of the human spirit and its responsibility as a competitive strategy has been proposed. Materials and Methods: The investigation method was an exploratory-laboratory. Electrocardiographic instruments were used to record brain signals through the EEG EPOC + 14 Electrode wireless device (emotive. co). After cleaning of signals by independent component analysis with EEGLAB software through the LORETA algorithm, the brain activity was localized. The study was performed on a population of a scent consumers. An advertise with the nature of the social responsibility of the brand was shown to the experimental group. Brand consumers were selected as the control group. This group was not aware of the social responsibility. Results: The results showed that the left hemisphere was mostly activated in the experimental group, whereas different regions in right hemisphere was activated in the control group. Conclusion: This study suggested that the behavior triggered by sensory stimuli is due to the activation of both left-orientation and right-orientation of the brain. The localization of the brain activity (left or right) can be regulated in favor of a brand with respect to social responsibility.
Sajjad Basharpoor, Nasim Zakibakhsh Mohammadi, Mohammad Narimani, ,
Volume 8, Issue 2 (3-2020)
Abstract
Introduction: The prevalence of borderline personality disorder is more than other type of personality disorders. It has been shown that there are some neurological deficits in patients with borderline personality disorder such as cognitive inhibition and self-injury, but the few studies aimed to decrease this deficit have been conducted. The purpose of this study was to investigate the effectiveness of emotional working memory training on the EEG asymmetry index in the beta band between two hemispheres in people with borderline personality disorder. Materials and Methods: The method of the current study was experimental and its design was pretest-posttest with the control group. All students of the University of Mohaghegh Ardebili with borderline personality disorder in 96-97 academic years comprised the statistical population of this study. Forty people selected by screening method via scale of borderline personality disorder trait and the structural clinical interview for mental disorders (SCID-II) and assigned to two experimental and control groups. The experimental group received 10 sessions of emotional working memory training, but no intervention was provided for the control group. The EEG device was used to record the electrical activity of the brain from the frontal lobe (Fp1, Fp2, F3, F4, F7, F8). Results: The results showed that the mean scores of the interhemispheric beta asymmetry have increased in the experimental group compared to the control group at the post-test stage. The emotional working memory training can lead to increased interhemispheric beta asymmetry. This intervention can increase the beta asymmetry by enhancing the beta wave in left hemisphere rather than right. Conclusion: the emotional working memory training can be the main axis to improve interhemispheric beta asymmetry in people with borderline personality disorder.
Zahra Aminiroshan, Seyed Morteza Azimzade, Mehdi Talebpour, Majid Ghoshuni,
Volume 8, Issue 2 (3-2020)
Abstract
Introduction: Advertising will be useless if they fail to attract the attention of the audience. The purpose of this study was to investigate the changes in the frontal and prefrontal area in connection with the power of the alpha band while watching commercial advertisement. Materials and Methods: The research design was semi experimental and 30 participants (15 men and 15 women) watched two advertises (sports and non-sports). In this research, the Neuro Guide software was used to convert electro-encephalographic data to quantitative data. Results: The results of the study showed that alpha power decreased in most of the channels among all individuals during watching the sports advertisements. This decrease was not observed when watching non-sports advertisements. This difference was also examined in terms of gender effect and the results showed that there was no significant difference between male and female. Conclusion: Our data suggested that the use of sport elements may enhance attention toward advertisement and lead to a long-lasting and efficacious memory retention.
Seyedeh Maryam Moshirian Farahi, Mohammad Javad Asghari Ebrahimabad, Imanollah Bigdeli, Ali Gorji,
Volume 8, Issue 3 (6-2020)
Abstract
Introduction: Adolescent brain development is recognized by changes in the brain structure and functions. Emotional processing could be affected by these brain changes. The aim of present study was to predict the dynamic emotional processing valences based on absolute brainwaves power (delta, theta, beta, and alpha bands) of five cortical regions. Materials and Methods: The study population was 50 healthy adolescents living in Mashhad, Iran. The Tools included mental state interview, EEG device, and Dynamic Emotional Processing Valences Task. Results: To predict the valences of facial expressions, one model was extracted for sadness and disgust based on the stepwise regression. The beta band in frontal area (for fear), theta band in frontal and beta in central area (for surprise), and beta band in frontal as well as theta band in frontal cortical regions were extracted. Conclusion: The hypotheses of predictably of dynamic facial expressions is supported by cortical electrophysiological activities during adolescence, and these cortical activities have a number of differences and similarities in comparison to adulthood. Finally, it is recommended that methods, such as Neurofeedback, could be applied to modulate adolescence emotional problems.
Parisa Ghafourian, Majid Ghoshuni, Iraj Vosoogh,
Volume 8, Issue 3 (6-2020)
Abstract
Introduction: Recent studies have proven that anxiety disorders have the highest abundance throughout the world. Almost everyone has experienced an anxiety. This anxiety can have an agonizing impact on a person’s life; however, anxiety can also be invigorating. Invigorating anxiety pushes a person to work with a goal in mind, while the more detrimental type of anxiety limits his or her attention. The purpose of this study was to investigate the brain function in test anxiety during answering mathematical questions. Material and Methods: In this experiment, 22 participants (9 male and 13 female) divided in two groups of test and control. Speelberger and ASRS anxiety tests were taken from all of the participants. Then electroencephalogram signal was recorded on 19 channels for 5 minutes with their eyes open and they were taking a conceptual math test simultaneously. In comparison with the control group, the test group had a shorter time to answer the questions and the video of the test group subjects was recorded while answering to a serious tester. After signal preprocessing, using Neuroguide software, frequency band powers of brain signal was extracted and the inattentional index (Theta/Beta ratio) was compared between the test and control groups using paired sample t-test. Results: In the test group, a significant decrease in theta to beta index was observed during math test compared to the eyes open condition on T3 (p=0.077, t=1.96), T4 (p=0.026, t=2.619)), T5 (p=0.084, t=1.91) channels. Besides, a nearly significant correlation (r=0.4055, p=0.0612) was found between false answers and percent change of theta/beta index during math test compared to rest condition. Conclusion: Anxiety in the test group was invigorating and reduced test error and inattention index. In the healthy subjects, due to the imposing anxiety from tester, the level of attention of the subjects increased significantly and their error in answering the questions decreased.
Saman Fouladi, Ali Asghar Safaei,
Volume 9, Issue 1 (12-2020)
Abstract
Introduction: Alzheimer's disease is a brain disorder that gradually destroys cognitive function and eventually the ability to carry out daily routine tasks. Early diagnosis of this disease has attracted the attention of many physicians and scholars, and several methods have been used to detect it in early phases. Evaluation of artificial neural networks is low-cost with no side effect method that is used for diagnosing and predicting Alzheimer's disease in subjects with mild cognitive impairment based on electroencephalogram signals. Materials and Methods: for this systematic review, keywords "Alzheimer's", "Artificial Neural Network" and "EEG" were searched in IEEE, PubMed central, ScienceDirect, and Google Scholar databases between 2000 to 2019. Then, they were selected for critical evaluation based on the most relevance to the subject under study. Results: The search result in these databases was 100 articles. Excluding unrelated articles, only 30 articles were studied. In the present study, different types of artificial neural networks were described, Next, the accuracy of the classification obtained by these methods was investigated. The results have shown that some methods, despite being less used in research or have simple architecture, have the highest accuracy for classification. In many studies, artificial neural networks have also been considered in comparison with other classification methods and the results show the superiority of these methods. Conclusion: Artificial neural networks can be used as a tool for early detection of Alzheimer's disease. This approach can be evaluated for its classification accuracy, speed, architecture, and common use. Some networks are accurate at classifying and understanding data, but are slow or require specific hardware/software environments. Some other networks work better with simple architectures than complex networks. Furthermore, changing the architecture of conventional networks or combining them with other methods resulted in significantly different results. Increasing accuracy of classification of these networks in the diagnosis of mild cognitive impairment could help to predict Alzheimer's disease appropriately.
Nazanin Mohammadkhani Ghiasvand, Foad Ghaderi,
Volume 9, Issue 1 (12-2020)
Abstract
Introduction: Epilepsy is one of the most common brain disorders that greatly affect patients life. However, early detection of seizure attacks can significantly improve their quality of life. In this study, we evaluated a deep neural network to learn robust features from electroencephalography (EEG) signals to automatically detect and predict seizure attacks. Materials and Methods: The architecture consists of convolutional neural networks and long short-term memory networks. It is designed to simultaneously capture spectral, temporal, and spatial information. Moreover, the architecture does not rely on explicit channel selection algorithms. The method is applied to the Children's Hospital of Boston-Massachusetts Institute of Technology dataset (CHB-MIT). To evaluate the method, the proposed model is trained in the patient-specific approach. Results: The proposed architecture achieves a sensitivity of 90.7 ± 7.9 percent, a false prediction rate of 0.12/h, and a mean prediction time of 36.8 minutes. Moreover, in the cases of focal seizures, the proposed model estimates the seizure focus. Conclusion: The proposed model achieved a high capability in seizure prediction. Moreover, by using the automated feature selection of the deep learning algorithm, the patterns of the pre-ictal period in EEG signals were determined. Furthermore, by specifying the seizure focus, the model can help neurologists to take further curative actions.
Nahid Ghoreishi, Samane Zare Molkabad, Somayeh Baratzade, Ateke Goshvarpoor, Ghasem Sadeghi Bajestani,
Volume 9, Issue 2 (3-2021)
Abstract
Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects information processing in the nervous system and the procedure of natural brain evolution. Therefore, the processing and analysis of the brain function of these patients have captivated the attention of many researchers. Qualitative electroencephalography (EEG) is an evaluation method for determining functional brain abnormalities that is different from quantitative EEG. Chaotic tools are used in qualitative EEG whereas linear and nonlinear methods are applied in quantitative EEG. The purpose of the present study is to compare qualitative EEG findings of healthy and autistic subjects. Materials and Methods: In this study, 19 channels of brain signals (Cz, C4, F4, Fz, F3, C3, P3, Pz, P4, T4, F8, Fp2, Fp1, F7, T3, T5, O1, O2, T6) of 6 healthy and 5 autistic subjects were evaluated in two phases. At first 5-minutes with closed eyes and then 5-minutes with opened eyes, the subject's EEG was recorded. After removing the artifacts, the correlation dimension of the signals was calculated, and brain maps were plotted to analyze the changes of correlation dimension on the scalp surface. Results: By comparison of the brain maps of the healthy and autistic groups between the opened and closed eyes periods, we found there was a difference between the brain function of the groups, especially in the T3 and T4 regions of the temporal regions as well as frontal and posterior areas. Conclusion: Using brain maps, correlation dimension mapping on the brain surface provides a better understanding of brain dynamics in autistic subjects.
Mojtaba Mohammadpoor, Atefe Alizadeh,
Volume 9, Issue 2 (3-2021)
Abstract
Introduction: Electroencephalography (EEG) is the most commonly used method to study the function of the brain. This study represents a computerized model for distinguishing between epileptic and healthy subjects using EEG signals with relatively high accuracy. Materials and Methods: The EEG database used in this study was obtained from the data available in Andrzejak. This dataset consists of 5 EEG sets (designated as A to E), each containing 100 EEG sections. Collections A and B comprised EEG signals that have been taken from 5 healthy volunteers. The C and D sets referred to EEGs from patients with focal epilepsy (without ictal recordings) and the E set was derived from a patient with ictal recording. Support vector machines were used after applying principal components analysis or linear discriminant analysis over the features of the signals. MATLAB has been used to implement and test the proposed classification algorithm. To evaluate the proposed method, the confusion matrix, overall success rate, ROC, and the AUC of each class were extracted. K-fold cross-validation technique was used to validate the results. Results: The overall success rate achieved in this study was above 82%. Dimension reduction algorithms can improve its accuracy and speed. Conclusion: It is helpful to be able to predict the occurrence of a seizure early and accurately. Using the computerized model represented in this study could accomplish this goal.
Sajjad Basharpoor, Shirin Ahmadi, Parviz Molavi, Fazeleh Heidari,
Volume 9, Issue 3 (7-2021)
Abstract
Introduction: Attempting to recognize specific QEEG markers in depression and obsessive-compulsive disorders is the one of main interests of research in quantitative electroencephalography. The purpose of the present study was to compare the absolute power of brain waves in the frontal area in people with major depressive disorder and obsessive-compulsive disorder. Materials and Methods: The method of this study is causal-comparative. The statistical population of this study consisted of all individuals with major depressive disorder and obsessive-compulsive disorder referring to the mental health Clinic of Fatemi Hospital in 2019 in Ardabil, Iran. 15 people with major depressive disorder and 15 subjects with obsessive-compulsive disorder were selected by purposeful sampling. Furthermore, 15 normal individuals were selected via the sampling method from the relatives of patients. Psychiatric diagnosis and structured clinical interview, Beck depression inventory, and Foa et al. obsessive-compulsive inventory were used to collect data. The QEEG recording was performed at the Psychological Laboratory of Mohaghegh Ardabili University and the data were analyzed by Neuroguide software. Results: The results showed that the absolute power of delta (F= 3.444), theta (F= 51.566), alpha (F= 217.1144), and beta (F= 175.555) waves differ between people with depressive disorder and obsessive-compulsive disorder compared to the control group. The delta, theta, and alpha absolute power at frontal lobes of patients with obsessive-compulsion significantly increased, and the alpha and beta absolute power at frontal lobes of patients with major depressive disorder significantly decreased compared to the control group. Conclusions: These results showed that the pattern of brain waves can be posed as an index for diagnosing and follow-upping of the therapeutic outcomes of major depression and obsessive-compulsive disorders. Furthermore, it can be used in designing neurofeedback interventions for these disorders.
Elias Mazrooei, Mahdi Azarnoosh, Majid Ghoshuni, Mohammadmehdi Khalilzadeh,
Volume 10, Issue 1 (12-2021)
Abstract
Introduction: The main purpose of this study is to provide a method for early diagnosis of Alzheimer's disease. The disease reduces memory function by destroying nerve cells in the nervous system and reducing nerve connections and interactions. Materials and Methods: The level of the disease should be diagnosed according to the association of the disease with various features in the brain signal and medical images. First, with proper preprocessing, nonlinear properties such as phase diagram, correlation dimension, entropy and Lyapunov exponent are extracted and Elman neural network is used for classification. Then, the correctness of the function of Elman neural network is compared with channel neural network. The use of deep learning methods, including channel neural network, can have more appropriate and accurate results among other classification methods. Results: In the case of using two CNN networks and one MLP network, the accuracy of the results was %98 in healthy individuals, %97.7 in mild patients and %97.5 in severely ill patients. In the case of using a CNN network with a combination of features, brain signal and medical images, in the case of stimulation, the accuracy of the results is %95 in healthy individuals, %92.5 in mild patients and %97.5 in severe patients. As a recall, the accuracy of the results is %75 in healthy individuals, %72.5 in mild patients and %87.5 in severe patients. The accuracy of the results in Elman neural network with the combination of brain signal features and medical images is %94.4 and in the case without combination of features, the accuracy of the results is %92.2. Conclusion: Among the processing methods proposed to classify the three classes of healthy, mild patient and severe patient, the method of combining brain signal characteristics and medical images has increased the accuracy of CNN and Elman classifier results.
Hoda Majdi, Mahdi Azarnoosh, Majid Ghoshuni, Vahidreza Sabzevari,
Volume 10, Issue 2 (3-2022)
Abstract
Introduction: Recognition of mental activities in brain-computer interface systems based on motor imagery has attracted the attention of many researchers. A visibility graph is a powerful method for analyzing the function and connectivity of different areas of the brain. The aim of this study is to improve and develop the visibility graph method for analyzing brain behavior and detecting motor imagery. Materials and Methods: First, brain signals including four motor imagery classes of left-handed, right-handed, foot, and tongue were transformed into three types of visibility graphs, and important features of these graphs were extracted. Then, to reduce features, the method of analysis of variance was used. To classify the motor imagery classes, the support vector machine was used. In most investigations, graph degree distribution has been used to extract information and graph weighting. In the present study, amplitude difference distribution has been used so shorter time series are required. To analyze the function and connectivity of different areas of the brain and to obtain the direction of information flow, a new method called weighted horizontal visibility graph-transfer entropy has been proposed. Results: Increasing the kappa value compared to other studies showed that a weighted horizontal visibility graph is a suitable method for processing brain signals based on motor imagery. A comparison of brain graphs and the direction of information flow in the four classes of motor imagery showed a significant difference between them. Conclusion: Temporal networks provide a better understanding of brain dynamics in brain-computer interface systems based on motor imagery.
Hossein Khazaei, Elias Mazrooei Rad,
Volume 11, Issue 3 (7-2023)
Abstract
Introduction: An unexpected number of people are at risk of Alzheimer's disease. Therefore, efforts to find effective preventive measures require to be intensified. Materials and Methods: To diagnose Alzheimer's disease through EEG signals using an artificial neural network, the first step involves pre-processing the recorded raw EEG data. This pre-processing includes the application of a 0.5 to 45 Hz bandpass filter to eliminate interference from the city's electrical signals. From the pre-processed data, the feature will be extracted. These features are related to time and frequency domains. Fourier transform, wavelet, first component analysis, nonlinear features of entropy, correlation dimension, and fractal dimension are among the suggested features. The extracted features will be evaluated by analysis of variance or t-test. The features that had the ability to separate different classes and have better statistical distribution in variance analysis or t-test are selected. Results: According to the capabilities of the artificial neural network in identifying different patterns and categorizing information that is set during a learning process, in this research, the artificial neural network will be used to determine the nonlinear mapping between EEG signals and the diagnosis of Alzheimer's disease. The database was divided into two categories: training and testing. In other words, the artificial neural network with the characteristics of the recorded signals as input and sick or healthy as the output of the neural network, and finally the the output of the trained artificial neural network is the diagnosis of sick or healthy data. In the final stage, the performance of the developed neural network will be evaluated and compared. Conclusion: Utilizing both EEG signals and artificial neural networks could represent a novel method for the diagnosis of Alzheimer's Disease in its early stages.
Hadi Akbari, Majid Mazinani, Elias Mazrooei Rad,
Volume 11, Issue 4 (10-2023)
Abstract
Introduction: The main purpose of this research is to explore and analyze changes in various frequency bands of brain signals among two distinct groups: memorizers and non-memorizers of the Quran. This investigation focuses on the execution of visual memory tests using Cantab software, with an emphasis on selecting optimal feature channels and employing different classifiers. Materials and Methods: First, brain signals were recorded from 15 Quran memorizers and 15 non-Quran memorizers during the performance of delayed matching to sample (DMS), Paired Associates Learning (PAL), and Spatial Recognition Memory image memory tests using Cantab software. Following appropriate pre-processing, non-linear features such as Lyapunov profile, correlation dimension, entropy, and detrended fluctuation analysis parameters were extracted. The selection of relevant channels was performed using T-TEST, Sequential Forward Selection, and Genetic Algorithm (GA) methods. Classification involved the use of multi-layer perceptron (MLP), Support Vector Machine, and naïve Bayes algorithms. Results: The selected optimal channels were primarily associated with frontal, parietal, and occipital brain regions involved in the attention network and visual memory of Quran memorizers. In most instances, the average power of low-frequency components in brain signals was found to be higher in memorizers than in non-memorizers. The MLP neural network, utilizing optimal channels selected by the GA method, demonstrated the highest accuracy between memorizers and non-memorizers at 94.79%. Conclusion: Analysis of EEG data revealed that the power ratio of low-frequency components, the power ratio of low-to-high-frequency components, and the power ratio of theta to beta bands indicated an increase in relaxation and patience among the memorizer group during the retrieval phase of visual memory. This enhanced concentration and attention, leading to a higher percentage of correct answers and increased reaction time in the memorizer group during the implementation of visual memory tests using Cantab software. The MLP neural network, employing features selected by the GA method, particularly sample and approximate entropy in D, A5 sub-bands, and in the occipital, parietal, and central brain regions, achieved a superior accuracy percentage in the implementation of the DMS test.
Fateme Asadollahzadeh Shamkhal, Ali Moghimi, Hamidreza Kobravi, Javad Salehi Fadardi,
Volume 12, Issue 1 (12-2023)
Abstract
Introduction: Contamination Obsessive-Compulsive Disorder (C-OCD) is one of the most common subtypes of OCD. Recently, transcranial direct current stimulation (tDCS) has been suggested as a new solution for improving symptoms in patients with OCD. Evaluating the effectiveness of tDCS through electroencephalogram (EEG) signals can provide a better estimate of improvement and reveal how tDCS leads to changes in the dynamics and features of brain signals. Selecting the optimal features of EEG signals among different features is necessary to show the impact of tDCS. Hence, this study aimed to identify features that undergo substantial changes following tDCS intervention. Materials and Methods: 10 patients with C-OCD received 20 minutes of tDCS in 10 sessions. The cathode electrode was placed on the left orbitofrontal cortex, and the anode on the cerebellar area. Before and after receiving tDCS, the Yale-Brown Obsession scale (Y-BOCS) was completed, and EEG signals were recorded at rest with open and closed eyes. Then, features such as Fuzzy Synchronization Likelihood (FSL), power spectrum, and Recurrence Quantification Analysis (RQA) were extracted from the EEG signals. Then, the Relief algorithm selected optimal features based on tDCS effectiveness. Results: The Relief algorithm revealed that RQA indices were more optimal for reflecting tDCS impact compared to other features among those extracted from EEG signals. Moreover, the DET and Lmax values significantly increased after tDCS intervention. Conclusion: By influencing neural interactions and balancing neuronal activity, tDCS has caused changes in the brain complexity of patients with C-OCD. As a result, there is a correlation between the effectiveness estimated by the Y-BOCS and the features selected by the relief algorithm. tDCS alters brain complexity in EEG compared with other features in C-OCD patients.
Elias Mazrooei, Mohammad Reza Dastury, Seyyed Ali Zendehbad,
Volume 12, Issue 3 (6-2024)
Abstract
Introduction: The primary aim of this article is to critically assess and compare conventional diagnostic methods for Alzheimer's disease (AD), with a particular focus on the promising capabilities of biomarkers and brain mapping techniques. As the incidence of AD rises globally, novel diagnostic strategies are needed to improve upon traditional methods, which often lack predictive accuracy and precision. This study provides an in-depth review of advanced diagnostic tools, including Artificial Intelligence (AI) applications (e.g., machine learning and deep learning), brain mapping techniques (e.g., electroencephalography and Magnetic Resonance Imaging), and biomarkers (e.g., tau protein and beta-amyloid), which can be identified through innovative visual and manual techniques. Additionally, the research explores the potential of identifying precursor proteins in the blood of patients with AD before symptom onset, presenting a significant opportunity for early intervention that could greatly impact treatment outcomes. Conclusion: The findings underscore the potential of combining brain mapping methods with manual analysis to facilitate transformational advancements in the diagnostics of AD. This combined approach enhances the detection of structural and functional brain changes associated with AD, contributing to more accurate and earlier diagnoses. Furthermore, brain-derived proteins are present at significantly higher levels in cerebrospinal fluid than in blood, where they are diluted by abundant plasma proteins, such as albumin and immunoglobulins. This observation raises questions about the reliability of current clinical diagnostic practices and emphasizes the importance of validating new diagnostic markers with AI-based manual techniques against neuropathological standards. Finally, the study concludes that AI, when used in conjunction with cognitive assessments, biomarkers, brain mapping approaches, and molecular testing, can substantially enhance diagnostic accuracy and reliability, which are essential for managing and treating patients with AD effectively.
Alaleh Alizadeh,
Volume 12, Issue 4 (11-2024)
Abstract
Introduction: Responsive neurostimulation (RNS) is a promising treatment for drug-resistant epilepsy, but patient response variability necessitates reliable predictive biomarkers. This systematic review synthesizes evidence on potential biomarkers for RNS efficacy, addressing a critical knowledge gap in epilepsy management. Materials and Methods: We systematically searched PubMed, Scopus, and Cochrane Library databases from inception to August 2024 using a comprehensive search strategy combining terms related to "responsive neurostimulation", "epilepsy", and "biomarkers". Studies evaluating predictors of RNS outcomes in drug-resistant epilepsy were included. Exclusion criteria encompassed non-English publications, case reports, and studies lacking outcome data. To minimize bias, study selection and data extraction were conducted in separate phases. The Joanna Briggs Institute critical appraisal tools were used for quality assessment. The primary outcome was the identification of biomarkers associated with RNS efficacy. Results: Eight studies (1 randomized controlled trial, 7 cohort studies) met inclusion criteria, involving 3,695 patients. Neuroimaging biomarkers, particularly structural connectivity patterns on diffusion-weighted imaging, were consistently associated with seizure reduction. Electrophysiological markers, including high-gamma band synchronizability and specific interictal discharge patterns, showed potential in forecasting RNS response. Magnetoencephalography-derived functional connectivity measures demonstrated high predictive accuracy in one study. Clinical factors such as unifocal seizure onset, prior stereoelectroencephalography, and shorter epilepsy duration were frequently associated with improved outcomes. Quality assessment revealed moderate to high methodological rigor across studies, with robust outcome measures and adequate follow-up periods. Conclusion: Emerging neuroimaging, electrophysiological, and clinical biomarkers show promise in predicting RNS efficacy for drug-resistant epilepsy. Integration of these biomarkers may optimize patient selection and improve clinical outcomes. Future research should focus on prospective validation and development of integrated prediction models to enhance clinical applicability.
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