The genetic background of Southern Iranian couples before marriage

Genetic service for couples plays an increasingly important role in diagnosis and risk management. This study investigated the status of consanguinity and the medical genetic history (effectiveness and coverage of medical genetic services) in couples residing in a city in southern Iran. We questioned couples who were referred to Behbahan Marital Counseling Center, Behbahan, Iran, during the period from January to November 2014, to obtain information on consanguinity, disease history, and previous referral to a medical genetics center. For the collected data was obtained descriptive statistics with STATA 11.0 software. A total of 500 couples were questioned. Mean age was 24.8 ± 5.2 years. Almost one quarter (23.4) of the couples were consanguineous. Consanguinity was almost twice as common in rural areas as in urban areas (33.9 vs. 19.2, p = 0.001). Only a few couples (~3.0) had ever been referred for genetic counseling. The main reason for previous genetic counseling was consanguinity (85.7). The majority of the participants (96.3) had never been tested for any genetic conditions. Our findings suggest that only a small proportion of couples in Khuzestan Province, Iran (Behbahan City) were receiving adequate genetics care. This may reflect the limited accessibility of such services, and inadequate awareness and education among the care providers. © 2016 Walter de Gruyter GmbH, Berlin/Boston

Automatic ROI Detection in Lumbar Spine MRI

Low back pain (LBP) is one of the most common diseases affecting a large number of people. Diagnosis and treatment of LBP require quick, accurate imaging methods. Magnetic resonance imaging (MRI) is effective in distinguishing between vertebra, intervertebral disc and spinal cord, and thus is used frequently in spinal cord injury (SCI) diagnosis. This paper proposes a fully automated approach to detecting region of interest (ROI) using T2-weighted MRI images. Our dataset included the cases of 100 patients who suffered from LBP. In total, 2000 axial and 1200 sagittal ROI were marked in the Lumbar spine. Extracted ROIs were used in the cascade classifier learner. In this method, ROI detection consists of two processes. First the ROIs are specified using the cascade classifier, and then via a process, non-regions of interest (NROIs) are discarded. Histogram of Oriented Gradient (HOG) was used as the feature descriptor in each stage of the Cascade classifier. This method does not require background knowledge of input images and it is reliable regardless of the images size, contrast and clinical abnormally of cases. The quantitative and qualitative evaluation results of the proposed ROI detector were 83 and above 94, respectively. © 2018 IEEE

Classification of Low and High Schizotypy Levels via Evaluation of Brain Connectivity

Schizotypy is a latent cluster of personality traits that denote a vulnerability for schizophrenia or a type of spectrum disorder. The aim of the study is to investigate parametric effective brain connectivity features for classifying high versus low schizotypy status. Electroencephalography (EEG) signals are recorded from 13 high schizotypy and 11 low schizotypy participants during an emotional auditory odd-ball task. The brain connectivity signals for machine learning are taken after the settlement of event-related potentials. A multivariate autoregressive (MVAR)-based connectivity measure is estimated from the EEG signals using the directed transfer functions (DTFs) method. The values of DTF power in five standard frequency bands are used as features. The support vector machines (SVM) revealed significant differences between high and low schizotypy. The accuracy, specificity, and sensitivity of the results using SVM are as high as 89.21%, 90.3%, and 88.2%, respectively. Our results demonstrate that the effective brain connectivity in prefrontal/parietal and prefrontal/frontal brain regions considerably changes according to schizotypal status. These findings prove that the brain connectivity indices offer valuable biomarkers for detecting the schizotypal personality. Further monitoring of the changes in DTF following the diagnosis of schizotypy may lead to early identification of schizophrenia and other spectrum disorders

State-based decoding of force signals from multi-channel local field potentials

The functional use of brain-machine interfaces (BMIs) in everyday tasks requires the accurate decoding of both movement and force information. In real-word tasks such as reach-to-grasp movements, a prosthetic hand should be switched between reaching and grasping modes, depending on the detection of the user intents in the decoder part of the BMI. Therefore, it is important to detect the rest or active states of different actions in the decoder to produce the corresponding continuous command output during the estimated state. In this study, we demonstrated that the resting and force-generating time-segments in a key pressing task could be accurately detected from local field potentials (LFPs) in rat's primary motor cortex. Common spatial pattern (CSP) algorithm was applied on different spectral LFP sub-bands to maximize the difference between the two classes of force and rest. We also showed that combining a discrete state decoder with linear or non-linear continuous force variable decoders could lead to a higher force decoding performance compared with the case we use a continuous variable decoder only. Moreover, the results suggest that gamma LFP signals (50-100 Hz) could be used successfully for decoding the discrete rest/force states as well as continuous values of the force variable. The results of this study can offer substantial benefits for the implementation of a self-paced, force-related command generator in BMI experiments without the need for manual external signals to select the state of the decoder. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

Tensor factorization approach for ERP-based assessment of schizotypy in a novel auditory oddball task on perceived family stress

Objective. Schizotypy, a potential phenotype for schizophrenia, is a personality trait that depicts psychosis-like signs in the normal range of psychosis continuum. Family communication may affect the social functioning of people with schizotypy. Greater family stress, such as irritability, criticism and less praise, is perceived at a higher level of schizotypy. This study aims to determine the differences between people with high and low levels of schizotypy using electroencephalography (EEG) during criticism, praise and neutral comments. EEGs were recorded from twenty-nine participants in the general community who varied from low schizotypy (LS) to high schizotypy (HS) during a novel emotional auditory oddball task. Approach. We consider the difference in event-related potential (ERP) parameters, namely the amplitude and latency of P300 subcomponents (P3a and P3b), between pairs of target words (standard, positive, negative and neutral). A model based on tensor factorization is then proposed to detect these components from the EEG using the CANDECOMP/PARAFAC (CP) decomposition technique. Finally, we employ the mutual information estimation method to select influential features for classification. Main results. The highest classification accuracy, sensitivity, and specificity of 93.1%, 94.73%, and 90% are obtained via leave-one-out cross validation. Significance. This is the first attempt to investigate the identification of individuals with psychometrically-defined HS from brain responses that are specifically associated with perceiving family stress and schizotypy. By measuring these brain responses to social stress, we achieve the goal of improving the accuracy in detection of early episodes of psychosis