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BACKGROUND: Due to the rapid growth of metropolises and the insufficiency of public transportation, nowadays, many people travel on these vehicles in a standing position. This position leads to discomfort and the risk of falling or non-collision incidents for the passengers. OBJECTIVE: The present study was conducted to analyze an innovative sit-standing seat to prevent falls and non-collision injuries in standing passengers. METHODS: A total of sixteen participated in this study. EMG signal and Borg scale were used to assess muscle activity and discomfort, respectively. RESULTS: The mean Borg scale score for perceived discomfort was lower in the sit-standing position than the standing position in all body organs, except for the hips. Also, in the sit-standing position compared to the standing position, the muscle activity of the soleus and medial gastrocnemius muscles was significantly lower in the constant velocity and entire phases in both legs, lower in the right leg in the acceleration phase and lower in the left leg in the deceleration phase. CONCLUSIONS: So, this seat can be used as an innovative idea to improve the ergonomic condition of standing passengers to prevent falls and non-collision injuries on transit buses.

Background: Many studies have used Cerebral Blood Volume (CBV) for gliomas grading and there has been in good agreement between CBV and tumor grade. Almost all of those studies have emphasized the importance of leakage correction due to the underestimation/overestimation of CBV caused by T1/T2* leakage effect in enhanced cases of tumors, especially high grade ones. Objectives: The aim of this study is to investigate two methods of CBV estimation in two groups of gliomas with the same grade and different appearance on post contrast T1 images (Enhanced vs. Non-enhanced ones).Material and Methods: In this retrospective study, eight glioma patients with histopatologically confirmed grade III were equally divided into two groups (with enhancement (group 1) and without enhancement (group 2)), and retrospectively studied. Imaging was performed on a 3 tesla MR Scanner and included gradient-echo DSC, 3D T1-weighted dataset and FLAIR images. The conventional method of CBV measurement (Integration over the whole curve of CTC- method 1) and the GVF fitting (method 2) was done using Matlab. Results: The observed mean rCBV in the tumor ROI was 2.85 and 2.12 for group 1 with method 1 and 2, respectively. Mean rCBV in the tumor ROI for group 2 was 1.24 and 1.11 with method 1 and 2, respectively. Conclusion: In conclusion, this pilot study demonstrated that with combined use of pre-bolus and accounting for T2* effect, CBV could be considered as a criterion for the categorization of glioma tumors.

Contains fulltext : 240530.pdf (Publisher’s version ) (Closed access) Background: Nicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention. Methods: Eleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence. Results: The cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p < 0.001) and methamphetamine (AUC = 0.71, p < 0.001) relative to non-dependent controls. Classifications related to nicotine dependence proved modest (AUC = 0.62, p = 0.014). Conclusions: Stimulant dependence was related to FA disturbances within tracts consistent with a role in addiction. The multivariate pattern of white matter differences proved sufficient to identify individuals with stimulant dependence, particularly for cocaine and methamphetamine. 10 p.

Background Pain is an unpleasant sensory and emotional experience followed by anxiety, depression, and frustration. Functional Near-Infrared Spectroscopy (fNIRS) as an optical technique identifies the brain functional networks by investigating connectivity between functionally linked of different anatomical regions in response to pain stimulation. Methods In this research, fNIRS was performed in order to study the difference in effective functional connectivity of the brain prefrontal cortex between the two modes of pain and rest based on the dynamic causal modeling (DCM) method. Effective functional connectivity changes in the prefrontal cortex between pain and rest states were calculated using DCM approach to investigate (1) areas known for pain sensation and (2) to analyze inter-network functional connectivity strength (FCS) by selecting several brain functional networks based on the analysis findings. All analyses were performed using toolboxes SPM-fNIRS and SPM8, Matlab software. Results Regional hemodynamics changes caused deoxyhemoglobin concentration to decrease in the prefrontal cortex of both hemispheres, particularly on the right side. We found a simultaneous increase in the concentration of oxyhemoglobin in the prefrontal cortex of the left hemisphere in comparison to the right hemisphere, that there was a trend toward reduction in oxyhemoglobin concentration. The results indicate that during the cold pain stimulation, the connectivities between prefrontal cortex regions were significantly changed. Specifically, a significantly consistent increase in the RPFC to MPFC connectivity was found while a significant consistent decrease was observed in the both MPFC to LPFC and LPFC to MPFC connectivities. Conclusion This study contributes to the pain research field to identify the directionality and causality of neuronal connections in the prefrontal cortex by applying DCM to fNIRS data. The results suggest that the proposed method infers directional interactions between hidden neuronal states in the brain under neuronal dynamic conditions based on optical density changes measurement.

Objective: Due to the reduced physical ability of elderly, the occurrence of non-collision incidents is higher for these passengers in standing position. Therefore, the purpose of the present study...

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: The Stroop test is a well-known model to denote the decline in performance under the incongruent condition, which requires selective attention and control of competitive responses. Functional near-infrared spectroscopy can identify activated brain regions associated with the Stroop interference effect.Objective: This research aims to identify the neural correlates associated with the Stroop tasks within the brain activated regions.Materials and Methods: In this cross sectional study, twelve right-handed healthy controls were investigated by means of a multi-channels fNIRS unit during the execution of the Stroop test. Effective connectivity changes in the prefrontal cortex between Stroop attentional conflict and rest states were calculated using DCM approach to investigate (1) areas known for selective attention and (2) analyze inter-network functional connectivity strength (FCS) by selecting several brain functional networks. Results: The results indicated that an increased activity was recorded in the LDLPFC during incongruent condition, while under neutral condition, the increase in activity was even more pronounced in those areas. Effect of Stroop interference associated with significant consistent causes an increase in the RDLPFC to DMPFC, LDLPFC to DMPFC and LDLPFC to RPFC effective connectivity strengths.Conclusion: This study showed the use of DCM algorithm for fNIRS data with respect to fMRI has provided additional information about the directional connectivity and causal interactions in LPFC networks during a conflict processing. Eventually, high temporal resolution fNIRS can be a promising tool for monitoring functional brain activation under the cognitive paradigms in neurological research and psychotherapy applications.

The goal of this study is to examine the effect of contrast agent (CA) dose and diffusion coefficient on the estimation of vessel size index (VSI). Three groups of four participants were enrolled in this study and two different experiments were performed. Different dose of CA, namely 0.1 mmol/kg and 0.05 mmol/kg were assessed in two groups of normal subjects. Diffusion coefficient effect was assessed in the third group with high-grade glioma. Imaging included gradient echo and spin-echo DSC and DTI on a 3-T MR Scanner. VSI estimation using half of standard dose of CA showed higher values compared to the application of standard, with a ratio of 2 for the WM and 1.5 for the GM. VSI estimates for tumor tissues (22 µm) were considerably higher compared to contra-lateral Normal-Appearing WM (NAWM, 4 µm, P

Background: Presurigical planning for glioma tumor resection and radiotherapy treatment require proper delineation of tumoral and peritumoral areas of brain. Diffusion tensor imaging (DTI) is the most common mathematical model applied for diffusion weighted MRI data. Neurite orientation dispersion and density imaging (NODDI) is another mathematical model for DWI data modeling.Objective: We studied whether extracted parameters of DTI, and NODDI models can be used to differentiate between edematous, tumoral, and normal areas in brain white matter (WM).Material and Methods: 12 patients with peritumoral edema underwent 3T multi-shell diffusion imaging with b-values of 1000 and 2000 smm-2 in 30 and 64 gradient directions, respectively. We fitted DTI and NODDI to data in manually drawn regions of interest and used their derived parameters to characterize edematous, tumoral and normal brain areas.Results: We found that DTI parameters fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) all significantly differentiated edematous from contralateral normal brain WM (p

Magnetic Resonance Imaging (MRI) can be applied to study the effects of rehabilitation strategies for neuroscience research. An MRI-wrist robot is designed and used as a clinical tool to examine the process of the brain plasticity changes. In this robot, the patient actuation is accomplished with two standard air cylinders, located inside the MRI chamber with two degrees of freedom (flexion-extension and ulna-radial deviation) with pneumatic air transmission, consisting of simple mechanism converting rotary motion to linear independently. A pilot study of brain image aiming at revealing more effective therapeutic strategies carried out to confirm the technical aspects of the development and validation. In a healthy subject, both wrist movement of robot and subject demonstrated brain activity in the contralateral primary somatosensory cortex. Because the robot does not move during the patient's body, a stand was designed to allow the wrist robot and patient to fit comfortably within the MRI machine. While all the parts of the robot were carefully selected with strict MRI compatibility requirements, the robot was tested by presenting some pilot imaging data with null effects on the image quality, as well. Finally, the possible further development of the robot has been introduced for a rehabilitation assessment.

Despite one decade of the missing feature theory application in the domain of Robust Automatic Speech Recognition (ASR), this field is still an active area for researchers. In this report using fuzzy concepts, we will present a method for modifying the cluster-based reconstruction of unreliable components of the noisy speech spectrogram. In this simple but effective method using a fuzzy membership function the feature vector component reliability is fuzzified. In the next stage this new parameter is applied as a weighting parameter for summing new reconstructed components and their old noisy values. Experiments were done on the FarsDat database using two recognition models, a Neural Network (NN) and a Hidden Markova Model (HMM). The improvements in the recognition results using this new reconstruction method in low SNRs for the frame-based neural network was approximately 5% and for the phoneme-based HMM was between one and two percent.