Sharif Digital Repository

Functional behavior of the brain can be captured using functional Magnetic Resonance Imaging (fMRI). Even though fMRI signals have temporal and spatial structures, most studies have neglected the temporal structure when inferring mental states (brain decoding). This has two main side effects: 1. Degradation in brain decoding performance due to lack of temporal information in the model, 2. Inability to provide temporal interpretability. Few studies have targeted this issue but have had less success due to the burdening challenges related to high feature-to-instance ratio. In this study, a novel model for incorporating temporal information while maintaining a low feature-to-instance ratio, is... 

Humans have a unique ability to use language for social communication. The neural architecture for language comprehension and production may have prominently emerged in the brain areas that were originally involved in social cognition. Here, we directly tested the fundamental link between language and social processing using functional magnetic resonance data (MRI) data from over 1,000 human subjects. Cortical activations in language and social tasks showed a striking similarity with a complementary hemispheric lateralization. Within core language areas, left-lateralized activations in the language task were mirrored by right-lateralized activations in the social task. Outside these areas,... 

Content-based image retrieval (CBIR) makes use of low-level image features, such as color, texture and shape, to index images with minimal human interaction. Considering the gap between low-level image features and the high-level semantic concepts in the CBIR, we proposed an image retrieval system for brain magnetic resonance images based on saliency map. The saliency map of an image contains important image regions which are visually more conspicuous by virtue of their contrast with respect to surrounding regions. First, the proposed approach exploits the ant colony optimization (ACO) technique to measure the image's saliency through ants' movements on the image. The textural features are... 

Superparamagnetic iron oxide (SPIO) nanomaterials are widely used as magnetic resonance imaging (MRI) contrast agents (CAs). These CAs significantly shorten transverse relaxation time (T2) and so decrease the intensity of the T2-weighted MRI (negative contrast imaging). However, the partial-volume effect is known to be one of the problems in negative contrast MRI. In this work, SPIO nanoparticles were modified by dextran and graphene oxide (GO) nanosheets to achieve a positive contrast MRI with high intensity. This modification resulted in shortening the longitudinal relaxation time (T1) of the SPIO nanoparticles (in addition to the T2 shortening).... 

Independent Component Analysis (ICA) can be used to decompose functional Magnetic Resonance Imaging (fMRI) data into a set of statistically independent images which are likely to be the sources of fMRI data. After applying ICA, a set of independent components are produced, and then, a "meaningful" subset from these components must be identified, because a large majority of components are non-interesting. So, interpreting the components is an important and also difficult task. In this paper, we propose a criterion based on the entropy of time courses to automatically select the components of interest. This method does not require to know the stimulus pattern of the experiment  

Graphene oxide (GO) has never been considered as a Magnetic Resonance Imaging (MRI) contrast agent since it was conceived as a diamagnetic material. There is a possibility that introduction of structural defects or manipulation of oxygen functionalities in GO change its magnetic response and provided a chance for GO to be a contrast agent for MRI. For this purpose, reduced graphene oxide (RGO) was treated by irradiation and annealing procedures. The study on the magnetic properties of the samples confirmed that the competition between the structural defects and oxygen functionalities to magnetic moments determines the magnetism in RGO. © 2018  

Parkinson's disease (PD) is the second most common and progressive neurological disorder. Parkinson's signs are caused by dysfunction in PD patient's brain network. Newly, resting state functional magnetic resonance imaging has been utilized to assess the altered functional connectivity in PD patients. In this study, we investigated the properties of the brain network topology in 19 PD patients compared to 17 normal healthy group by means of graph theory. In addition, we used four different graph formation methods to explore linear and nonlinear relationships between fMRI signals. Each correlation measure created a weighted graph for each subject. Different graph characteristics have been... 

Integrating high spatial resolution of functional magnetic resonance imaging (fMRI) and high temporal resolution of electroencephalogram (EEG) is promising in simultaneous EEG and fMRI analysis, especially for epileptic patients. The EEG recorded inside an MR scanner is interfered with MR artifacts. In this article, we propose new artifact reduction approaches and compare them with the conventional artifact reduction methods. Our proposed approaches are based on generalized eigenvalue decomposition (GEVD) and median filtering. The proposed methods are applied on experimental simultaneous EEG and fMRI recordings of an epileptic patient. The results show significant improvement over... 

In recent years, the regression dynamic causal modelling (rDCM) method was introduced as a new version of dynamic causal modelling (DCM) to derive effective connectivity in whole-brain networks for functional magnetic resonance imaging (fMRI) data. In this research, we used data obtained while applying the stimulation of audio movie comprised different emotional states. We applied this method to two networks consisting of ten auditory and forty-four regions, respectively. This method was used to study effective connections between emotional states and represent the distinction between emotions. Finally, significant effective connections were found in emotional processing and auditory... 

Our purpose was to determine whether perfusion MR imaging can be used for malignancy determination of tumors. Relative cerebral blood flow (rCBF) is a commonly used perfusion magnetic resonance imaging (MRI) technique for the evaluation of malignancy. The goal of our study was to determine the usefulness of this parameter in malignancy determination of tumors using Independent Component Analysis (ICA)  

Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to... 

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods... 

Schizophrenia is a severe brain disease that influences the behaviour and thought of person. These effects may fail in achieving the expected levels of interpersonal, academic, or occupational functioning. Although the underlying mechanism is not yet clear, the early detection of schizophrenia is an attractive and challenging research area. There are differences in brain connections of patients and healthy people. This study presents a new computer-aided diagnosis (CAD) method to diagnose schizophrenia (SZ) patients from normal control (NC) people by using the rest-state functional magnetic resonance imaging (R-fMRI) data. fMRI data has a huge dimension, and extracting efficient features is... 

Background: Over the last two decades, breast cancer remains the main cause of cancer deaths in women. To treat this type of cancer, radiation therapy (RT) has proved to be efficient. RT for breast cancer is, however, challenged by intrafractional motion caused by respiration. The problem is more severe for the left-sided breast cancer due to the proximity to the heart as an organ-at-risk. While particle therapy results in superior dose characteristics than conventional RT, due to the physics of particle interactions in the body, particle therapy is more sensitive to target motion. Conclusions: This review highlights current and emerging strategies for the management of intrafractional... 

Food choice is one of the most fundamental and most frequent value-based decisions for all animals including humans. However, the neural circuitry involved in food-based decisions is only recently being addressed. Given the relatively fast dynamics of decision formation, electroencephalography (EEG)-informed fMRI analysis is highly beneficial for localizing this circuitry in humans. Here, by using the EEG correlates of evidence accumulation in a simultaneously recorded EEG-fMRI dataset, we found a significant role for the right temporal-parietal operculum (PO) and medial insula including gustatory cortex (GC) in binary choice between food items. These activations were uncovered by using the... 

Molecular imaging (MI) can provide not only structural images utilizing temporal imaging techniques, but also functional and molecular data using a variety of newly developed imaging techniques. Nanotechnology’s application in MI has commanded a lot of attention in recent decades, and it has provided tremendous potential for imaging living subjects. In this study, D-glucosamine conjugated functionalized magnetic iron oxide nanoparticles (Fe3O4-PEG-DG NPs) were prepared and studied as magnetic resonance imaging (MRI) contrast agents. To evaluate their distribution, single-photon emission computed tomography (SPECT) is performed. Fe3O4 NPs are made using a well-known co-precipitation process... 

Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can be gleaned from a collection of univariate effects (UVE) using functional magnetic resonance imaging (fMRI). Here, we argue that regardless of patterns revealed, conventional MVPA is merely a decoding tool with increased sensitivity arising from considering a large number of ‘weak classifiers’ (i.e., single voxels) in higher dimensions. We propose instead that ‘real’ multivoxel... 

Background For decades, contrast agents have been used to reduce longitudinal (T1) or transverse (T2) relaxation times. High toxicity of gadolinium-based contrast agents leads researchers to new T1 contrast agents. Manganese oxide (MnO) nanoparticle (NP) with the lower peril and good enough signal change ability has been offered as a new possibility for magnetic resonance imaging (MRI). Methods The synthesized NPs were investigated for physicochemical and biological properties by X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscope, dynamic light scattering (DLS), inductively coupled plasma, enzyme-linked immunosorbent assay, and 3 T magnetic resonance... 

The joint analysis of multiple datasets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multi-subject datasets by using a deflation based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of... 

Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to...