Biomedical Imaging / Medical Imaging
Farzaneh Keyvanfard; Alireza Rahiminasab; Abbas Nasiraei Moghaddam
Volume 15, Issue 3 , December 2021, , Pages 211-220
Abstract
In brain disorders, both the brain structural and functional connectivity are altered and cause different behavioral symptoms. Recognizing these variations can help us to diagnose, treat, and control its progression. Schizophrenia is one of these mental disorders that widely affects the brain structure ...
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In brain disorders, both the brain structural and functional connectivity are altered and cause different behavioral symptoms. Recognizing these variations can help us to diagnose, treat, and control its progression. Schizophrenia is one of these mental disorders that widely affects the brain structure and function. Investigation of brain variations in this disease has commonly been based on voxel-wise analysis or region-based studies. The aim of this study is to evaluate brain structure and function alterations in schizophrenia patients comparing to healthy control from the brain connectivity perspective. For this purpose, using the statistical test method, a comparison was made between all the structural and functional connections in the brain of 92 healthy individuals and 37 schizophrenia patients obtained from diffusion tensor imaging (DTI) and functional Magnetic Resonance Imaging (fMRI) respectively. The findings of this study indicate that the number of altered edges in the brain functional network of patients is about 4 times more than the number of varied structural connections, which indicates the high impact of this disorder on brain function. Also, examination of the number of altered edges connected to each node, the affected areas in this disease were identified and it was shown that the schizophrenia patients’ brain has changed in parts of the brain subnetworks related to the default mode network (DMN), attention, somatomotor and vision networks. It was also shown that the altered brain structural connections of patients are involved in the areas of the superior frontal gyrus, temporal gyrus and part of the occipital cortex which are mostly shown relative increasing of the structural connectivity weights. The results of this study indicate the widespread effect of this disorder on the brain and suggest that the occurrence of some abnormal behaviors in schizophrenia patients may be due to some increased structural connectivity weights.
Bioelectrics
Farzaneh Keyvanfard; Abbas Nasiraei Moghaddam
Volume 13, Issue 2 , August 2019, , Pages 147-158
Abstract
Brain as the most complex organ in the human body has been investigated from various aspects. The greatest origin of this complexity is due to the fact that, despite the fixed architecture of brain structure (physical connections), the functional connectivity is in a constantly changing state, resulting ...
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Brain as the most complex organ in the human body has been investigated from various aspects. The greatest origin of this complexity is due to the fact that, despite the fixed architecture of brain structure (physical connections), the functional connectivity is in a constantly changing state, resulting to different behaviors. In many mental diseases, both brain structural and functional connectivities and their relationship are changed and cause different symptoms. Investigation of brain connectivity variations in the disease may help to better understanding of the relationship between brain structure and function. One of the most severe and debilitating brain disorders is Schizophrenia in which both brain structure and function are involved. Among all available methods, multimodal analysis of data has been recently gained great interest to provide the capability of extracting association between separate neuroimaging data. However, due to their voxel based viewpoint, relationship between brain connectivities cannot be inferred. In this study, the joint independent component analysis (jICA) has been proposed to investigate the relationship between brain functional and structural connectivity. We applied the suggested approach to combine functional and structural connectivity, in order to assess abnormalities underlying schizophrenic patients relative to healthy people. The findings suggest that the correspondence between brain function and structure is not necessarily one-to-one. The results also indicated that variations in several structural fibers, such as superior longitudinal fasciculus and inferior longitudinal fasciculus, are associated with functional changes in the temporal and frontal lobes. Besides, analyzing the nodal strength and shortest path length in the obtained subnetworks demonstrates that the functional subnetworks efficiency in parallel information transfer in schizophrenic patients is reduced. Overall, the outcomes point out the capability of the proposed method to better understanding of brain functional and structural connectivity association and its variations in brain disorders.
Biomedical Image Processing / Medical Image Processing
Maryam Momeni; Hamid Abrishami Moghaddam; Reinhard Grebe; Kamran Kazemi; Fabrice Wallois
Volume 5, Issue 3 , June 2011, , Pages 231-244
Abstract
Reliable gradation of neonatal brain development is important for clinical investigation of neurological disorders. A prerequisite for such quantification of development is knowledge about an appropriate temporal resolvability. For this purpose, we investigated the evolution of macroscopic morphological ...
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Reliable gradation of neonatal brain development is important for clinical investigation of neurological disorders. A prerequisite for such quantification of development is knowledge about an appropriate temporal resolvability. For this purpose, we investigated the evolution of macroscopic morphological features of the neonatal brain to estimate, for the first time, the required temporal interval in the early weeks after birth. In a first step, we constructed two neonatal templates for the age ranges of 39-40 and 41- 42 weeks' gestational age using T1-weighted MR images. We compared the spatial variation of anatomical landmarks and the average and the maximal length of spatial deformation in 25 subjects normalized to the two templates along x, y and z directions. MANOVA confirmed the significant difference between spatial variations of the above macroscopic features in the two age ranges. Furthermore, quantitative analysis of feature scattering yielded the same result even in features for which the null hypothesis was not rejected by MANOVA. We conclude that minimal temporal interval of two weeks is required for acute macroscopic morphological studies of the developing brain in the early weeks after birth.
Biomedical Image Processing / Medical Image Processing
Effat Yahaghi; Yashar Nohi; Amir Movafeghi; Hamid Soltanian Zadeh
Volume 4, Issue 1 , June 2010, , Pages 1-11
Abstract
Magnetic resonance imaging (MRI) is a non-ionizing method for identification and evaluation of soft tissue lesions. Perfusion MRI evaluates soft tissues by measuring changes in magnetization of water molecules due to a contrast agent. To this end, concentration curves in the plasma and tissue are estimated ...
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Magnetic resonance imaging (MRI) is a non-ionizing method for identification and evaluation of soft tissue lesions. Perfusion MRI evaluates soft tissues by measuring changes in magnetization of water molecules due to a contrast agent. To this end, concentration curves in the plasma and tissue are estimated by MRI and effective longitudinal relaxation time (T1eff) of the tissue was calculated. To interpret the results, the effects of water exchange on the effective longitudinal relaxation time should be studied. This work presents such a study in which the equations of two- and three-compartmental models of rat brain tissue are solved using Hion and Runge-Kutta numerical methods for different input functions and simulated by Monte Carlo method. Since the exchange of water and contrast agent among different tissue compartments is a diffusion phenomenon, Monte Carlo method is applicable. Results of the numerical methods were compared with those of Monte Carlo simulation. The results of the two methods were almost identical with a maximum relative difference of less than 1%. In this work, concentration of contrast agent in plasma is estimated from MRI of a rat brain tissue. This data is used in the Monte Carlo method to obtain T1eff and exchange rate constants. An advantage of our method is that T1eff is obtained from real data and not from the curve fitting method as commonly used. We derive concentration of contrast agent as a function of time in extravascular space for different constants (K). Then, the curves of simulated and real data were compared to obtain the exchange rate constant of each compartment. The results showed that K of an abnormal tissue was larger than that of the normal tissues. As such, this parameter may be used for diagnosis and treatment of the soft tissue diseases.
