Background: Quantitative Magnetization Transfer Imaging (QMTI) is often used to quantify the myelin content in multiple sclerosis (MS) lesions and normal appearing brain tissues. Also, automated classifiers such as artificial neural networks (ANNs) can significantly improve the identification and classification processes of MS clinical datasets.Objective: We classified patients with relapsing-remitting multiple sclerosis (RRMS) from healthy subjects using QMTI and T1 longitudinal relaxation time data of brain white matter and the performance of three ANN-based classifiers have been investigated. Materials and Methods: Conventional magnetic resonance imaging (MRI) and quantitative magnetization transfer scans were obtained from RRMS patients (n=30) and age-matched healthy subjects (n=30). After image pre-processing and brain tissue segmentation, QMTI parameters including magnetization transfer ratio (MTR), magnetization transfer rate (Ksat), T1 relaxation time under MT saturation pulse (T1sat) and T1 longitudinal relaxation time were calculated as parametric maps. Three ANN algorithms, including multilayer perceptron (MLP), radial basis function (RBF) and ensemble neural network based on Akaike information criterion (ENN-AIC) input features were extracted in the form of QMTI and T1 mean values. The ANNs quantitative performance is measured by the standard evaluation of confusion matrix criteria.Results: The results indicate that ENN-AIC-based classification method has achieved 90% accuracy, 92% sensitivity and 86% precision compared to other ANN classification models such as RBF and MLP. NPV, FPR and FDR values of the proposed ENN-AIC model were found to be 0.933, 0.125 and 0.133, respectively. A graphical representation of how to track actual data by the predictive values derived from each of the three algorithms, was also presented. It has been demonstrated that ENN-AIC as an effective neural network improves the quality of classification results compared to MLP and RBF.Conclusion: The efficiency and robustness of ENN classifier will greatly enhance with the use of AIC-based combination weights assignment. In addition, this research provides a new direction to classify a large amount of quantitative MRI data that can help the physician in a correct MS diagnosis.
Background:
Cognitive control of brain regions can be determined by the tasks involving the cognitive control such as the color word Stroop task. Stroop task define the reduction in function in incongruent condition, which requires more attention and control of competitive responses.
Objective:
The purpose of this study was to evaluate the activity of brain using the Modified Conflict Stroop Task in Military Personnel.
Material and Methods:
In this applied experimental study, to specify the activity of different regions of brain in response to conflict Persian color-word Stroop task, 20 healthy persons participated in this study. To evaluate selective attention, the traditional color-word Stroop Task Model was modified, and the Stroop test was designed in high- and low-threat zones. We used functional magnetic resonance imaging (fMRI) to evaluate the brain activation during the Stroop task performance. The color-word Stroop task consists of incongruent, congruent, and neutral conditions, and the subjects were requested to carefully choose the correct answer.
Results:
The mean response time was longer in incongruent condition (867.6±193.5ms) compared to congruent and neutral conditions. Analysis of neuroimaging data revealed that the brain conflict-related regions are activated by the Stroop interference. In incongruent trial, the superior frontal gyrus (SFG) and inferior frontal gyrus (IFG) showed the most active and stronger BOLD responses. In congruent trials, the activation in the brain was less and had difference compared with incongruent trials.
Conclusion:
Our result offers that the frontal cortex and the anterior cingulate cortex are sensitive to different trials of Persian Stroop task. Using modified Stroop task, we determined the brain responses to the selective attention test.
Background: Optical imaging has attracted the researcher's attention in recent years as an uncompromising and efficient method to measure the changes in brain cortex activity. Functional Near-Infrared Spectroscopy (fNIRS) is a method that measures hemodynamic changes in the brain cerebral cortex based on optical principles. Objectives: The current study aimed to evaluate the activities of the brain cortex during wrist movement using fNIRS. Methods: In this study, the activity of the brain motor cortex was investigated during right wrist movement in 10 young righthanded volunteers. Data were collected using a 48-channel fNIRS device with two wavelengths of 855 nm and 765 nm. For this experiment, 20 channels were used and the sampling frequency was set at 10 Hz. Results: Signal intensity in the motor cortex was significantly higher during movement than in the rest (P ≤ 0.05). The activation map of wrist movements was separated spatially in the motor cortex. The highest activity was recorded in the primary motor cortex (M1). There was a significant difference in the focus of the maximum activation of the brain between the four main directions. Conclusions: It is possible to differentiate between different directions of movement using near-infrared signals. The presence of directional activation in the cerebral cortex helps confirm the notion that this part of the brain participates in the processing of complex information besides controlling the movement of different parts of the body.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.