Pub Date : 2014-11-26DOI: 10.1109/ICBME.2014.7043936
Nazanin Goharian, Hadi Kalani, Sanar Moghimi
The relationship between muscles' electrical activity and body movements is of special importance in many medical applications. In this study, for the first time, we plan to evaluate the efficiency of time-delay parallel cascade identification (TDPCI) to predict jaw motion using Electromyography (EMG) signals recorded from two masticatory muscles, namely masseter and temporalis. The Obtained results demonstrate the efficiency of TDPCI in predicting time-varying mastication kinematic parameters based on EMG signals recorded from the two aforementioned muscles. The proposed model has the potential to be employed for controlling masticatory robots controlled by remotely recorded EMG signals.
{"title":"A time-delay parallel cascade identification system for predicting jaw movements","authors":"Nazanin Goharian, Hadi Kalani, Sanar Moghimi","doi":"10.1109/ICBME.2014.7043936","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043936","url":null,"abstract":"The relationship between muscles' electrical activity and body movements is of special importance in many medical applications. In this study, for the first time, we plan to evaluate the efficiency of time-delay parallel cascade identification (TDPCI) to predict jaw motion using Electromyography (EMG) signals recorded from two masticatory muscles, namely masseter and temporalis. The Obtained results demonstrate the efficiency of TDPCI in predicting time-varying mastication kinematic parameters based on EMG signals recorded from the two aforementioned muscles. The proposed model has the potential to be employed for controlling masticatory robots controlled by remotely recorded EMG signals.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121799928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043892
Masoud Pourhaghgouy, A. Zamanian
Porous nanocomposite scaffolds were fabricated by freeze casting method with composition of constant chitosan concentration (3 wt.%) blended with different percentages of (10, 20 and 50 wt.%) bioactive glass nanoparticles (BGNPs) which were synthesized by sol-gel method. Transmission Electron Microscopy (TEM) images proved that the size of synthesized BGNPs with formula of 64Si02.28Ca0.8P205 was lower than 20 nm. Good interfacial bonding between chitosan polymers and BGNPs was performed as proved with Fourier Transform Infrared Spectroscopy (FT-IR) and X-ray diffraction (XRD) analysis. Scanning Electron Microscopy (SEM) images showed that the addition of different percentages of BGNPs had no effect on nanocomposites's morphology and pores size. The scaffold contain 20 wt.% of BGNPs represented the highest water absorption value in comparison with the other scaffolds. As the amount of BGNPs was augmented in each nanocomposite, porosity measurements decreased from 92.22% to 88.98% but the compressive module values and compressive strength values improved from 10.04 to 10.77 MPa and 363 to 419 kPa, respectively.
{"title":"Ice-templated scaffolds of bioglass nanoparticles reinforced-chitosan","authors":"Masoud Pourhaghgouy, A. Zamanian","doi":"10.1109/ICBME.2014.7043892","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043892","url":null,"abstract":"Porous nanocomposite scaffolds were fabricated by freeze casting method with composition of constant chitosan concentration (3 wt.%) blended with different percentages of (10, 20 and 50 wt.%) bioactive glass nanoparticles (BGNPs) which were synthesized by sol-gel method. Transmission Electron Microscopy (TEM) images proved that the size of synthesized BGNPs with formula of 64Si02.28Ca0.8P205 was lower than 20 nm. Good interfacial bonding between chitosan polymers and BGNPs was performed as proved with Fourier Transform Infrared Spectroscopy (FT-IR) and X-ray diffraction (XRD) analysis. Scanning Electron Microscopy (SEM) images showed that the addition of different percentages of BGNPs had no effect on nanocomposites's morphology and pores size. The scaffold contain 20 wt.% of BGNPs represented the highest water absorption value in comparison with the other scaffolds. As the amount of BGNPs was augmented in each nanocomposite, porosity measurements decreased from 92.22% to 88.98% but the compressive module values and compressive strength values improved from 10.04 to 10.77 MPa and 363 to 419 kPa, respectively.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115634987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043938
E. Lotfi, Babak Nadjar Araabi, M. N. Ahmadabadi, L. Schwabe
Neurons in primary visual cortex (VI) optimally respond to stimuli with their preferred orientation. The response of neurons in VI is suppressed by iso-oriented neurons located in their surround. It is very important to understand the circuitry of center-surround interactions. Previous studies in this field followed the approach of postulating models inspired by neuroscience data. While previous models are only postulated, we adopted a strictly data-driven approach and trained a biologically constrained recurrent network model by using supervised learning methods. We have trained a recurrent neural network model constrained by selected biological and anatomical facts. The obtained model describes the near and far surround behavior and the synaptic weights obtained by training are biologically plausible.
{"title":"Biological constrained learning of parameters in a recurrent neural network-based model of the primary visual cortex","authors":"E. Lotfi, Babak Nadjar Araabi, M. N. Ahmadabadi, L. Schwabe","doi":"10.1109/ICBME.2014.7043938","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043938","url":null,"abstract":"Neurons in primary visual cortex (VI) optimally respond to stimuli with their preferred orientation. The response of neurons in VI is suppressed by iso-oriented neurons located in their surround. It is very important to understand the circuitry of center-surround interactions. Previous studies in this field followed the approach of postulating models inspired by neuroscience data. While previous models are only postulated, we adopted a strictly data-driven approach and trained a biologically constrained recurrent network model by using supervised learning methods. We have trained a recurrent neural network model constrained by selected biological and anatomical facts. The obtained model describes the near and far surround behavior and the synaptic weights obtained by training are biologically plausible.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116610675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043901
M. Khalilian, M. R. Valojerdi, A. Rouhollahi
The zona pellucida (ZP) is the extracellular coat that surrounds mammalian oocytes. The precise determination of ZP hardness is mainly unknown due to the lack of appropriate measuring systems and modelling methods. In this study, we have used experimental and numerical models to explain the mechanical behavior of a single oocyte cell to improve the assisted reproductive technology (ART) outcomes by assessing oocyte/embryo quality. This paper presents the development of a microinjection model to estimate the ZP hardness and an experimental procedure to obtain the required data for this model. Our results show that the estimated penetration force provides a performance target for the penetration process during intracytoplasmic sperm injection (ICSI), while the estimated corresponding hardness serves as an indicator of the amount of deformation experienced by the oocyte before penetration. Evaluation of these results shows that a routine assessment of ZP hardness under microinjection would allow for the identification of certain oocyte pools for which further manipulation is recommended in order to increase injection, hatching and finally ART outcomes.
{"title":"Numerical and experimental estimating zona pellucida hardness under microinjection to assess oocyte quality","authors":"M. Khalilian, M. R. Valojerdi, A. Rouhollahi","doi":"10.1109/ICBME.2014.7043901","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043901","url":null,"abstract":"The zona pellucida (ZP) is the extracellular coat that surrounds mammalian oocytes. The precise determination of ZP hardness is mainly unknown due to the lack of appropriate measuring systems and modelling methods. In this study, we have used experimental and numerical models to explain the mechanical behavior of a single oocyte cell to improve the assisted reproductive technology (ART) outcomes by assessing oocyte/embryo quality. This paper presents the development of a microinjection model to estimate the ZP hardness and an experimental procedure to obtain the required data for this model. Our results show that the estimated penetration force provides a performance target for the penetration process during intracytoplasmic sperm injection (ICSI), while the estimated corresponding hardness serves as an indicator of the amount of deformation experienced by the oocyte before penetration. Evaluation of these results shows that a routine assessment of ZP hardness under microinjection would allow for the identification of certain oocyte pools for which further manipulation is recommended in order to increase injection, hatching and finally ART outcomes.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121066801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043932
Aida Fooladivanda, S. B. Shokouhi, M. Mosavi, N. Ahmadinejad
Accurate breast MRI segmentation is an important processing step in Computer Aided Diagnosis (CAD) systems and breast density assessment. Most of the atlas-based breast segmentation methods employ breast area as the template. Instead, we use both pectoral muscle and chest region model as the template, because there is great variability in breast shape and signal intensity. Pectoral muscle and chest region place in similar locations with similar shape and signal intensity. We demonstrate the high quality of the defined template for our atlas-based system. The presented approach is validated with a dataset of 2800 bilateral axial breast MR images from 50 women that include all of Breast Imaging Reporting and Data System (BI-RADS) breast density range. Five quantitative metrics as Dice Similarity Coefficient (DSC), Jaccard Coefficient (JC), total overlap, False Negative (FN) and False Positive (FP) are computed to compare similarity between automatic and manual segmentations. Our proposed algorithm obtains DSC, JC, total overlap, FN and FP values of 0.85, 0.75, 0.83, 0.16 and 0.11, respectively.
{"title":"Atlas-based automatic breast MRI segmentation using pectoral muscle and chest region model","authors":"Aida Fooladivanda, S. B. Shokouhi, M. Mosavi, N. Ahmadinejad","doi":"10.1109/ICBME.2014.7043932","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043932","url":null,"abstract":"Accurate breast MRI segmentation is an important processing step in Computer Aided Diagnosis (CAD) systems and breast density assessment. Most of the atlas-based breast segmentation methods employ breast area as the template. Instead, we use both pectoral muscle and chest region model as the template, because there is great variability in breast shape and signal intensity. Pectoral muscle and chest region place in similar locations with similar shape and signal intensity. We demonstrate the high quality of the defined template for our atlas-based system. The presented approach is validated with a dataset of 2800 bilateral axial breast MR images from 50 women that include all of Breast Imaging Reporting and Data System (BI-RADS) breast density range. Five quantitative metrics as Dice Similarity Coefficient (DSC), Jaccard Coefficient (JC), total overlap, False Negative (FN) and False Positive (FP) are computed to compare similarity between automatic and manual segmentations. Our proposed algorithm obtains DSC, JC, total overlap, FN and FP values of 0.85, 0.75, 0.83, 0.16 and 0.11, respectively.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123244255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043897
H. Bazmara, M. Sefidgar, M. Bazargan, M. Musavi, M. Soltani
Lumen formation is the key event in vascular morphogenic events. Acquiring lumenal compartment in endothelial cells (EC) depends on mechanical and biochemical signals that EC receive from its environment. In this article, a mu I ti sta lo cell based model is developed to simulate lumen formation and development in a single EC. In cellular scale, cellular Pott's model is used for EC growth and interaction with heterogeneous structure of extracellular matrix (ECM). In molecular scale, the signaling cascade of lumen formation is obtained and a Boolean network is used to model receptor cross talk and intracellular signaling molecules interactions. The results show development of lumen inside an EC.
管腔形成是血管形态发生事件中的关键事件。内皮细胞(EC)获得腔室取决于其从环境中接收的机械和生化信号。在本文中，建立了一个基于mu - I - I - lo细胞的模型来模拟单个EC中腔体的形成和发展。在细胞尺度上，细胞波特模型用于细胞外基质(ECM)异质结构与细胞外基质(ECM)的相互作用。在分子尺度上，获得了管腔形成的信号级联，并使用布尔网络来模拟受体串扰和细胞内信号分子的相互作用。结果显示了EC内管腔的发育。
{"title":"A multi-scale cell-based model of lumen formation in single endothelial cell","authors":"H. Bazmara, M. Sefidgar, M. Bazargan, M. Musavi, M. Soltani","doi":"10.1109/ICBME.2014.7043897","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043897","url":null,"abstract":"Lumen formation is the key event in vascular morphogenic events. Acquiring lumenal compartment in endothelial cells (EC) depends on mechanical and biochemical signals that EC receive from its environment. In this article, a mu I ti sta lo cell based model is developed to simulate lumen formation and development in a single EC. In cellular scale, cellular Pott's model is used for EC growth and interaction with heterogeneous structure of extracellular matrix (ECM). In molecular scale, the signaling cascade of lumen formation is obtained and a Boolean network is used to model receptor cross talk and intracellular signaling molecules interactions. The results show development of lumen inside an EC.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122635385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043950
Fatemeh Ghoreishian, M. Pooyan
A mathematical model of Parkinsonian tremor is presented in this research. This model contains structures involved in tremor genesis from brain to muscle. The result of this study is compared with physiological parkinsonian tremor by using the correlation dimension, the largest Lyapunov exponent and the Kolmogorov entropy. The correlation dimension represents the complexity and the largest Lyapunov exponent and the Kolmogorov entropy indicates the chaoticity of the system. This comparison shows that the obtained result based on the purposed model is close to experimental data, so the presented model is an accurate and applicable model.
{"title":"A mathematical model for tremor genesis in Parkinson disease from a chaotic view","authors":"Fatemeh Ghoreishian, M. Pooyan","doi":"10.1109/ICBME.2014.7043950","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043950","url":null,"abstract":"A mathematical model of Parkinsonian tremor is presented in this research. This model contains structures involved in tremor genesis from brain to muscle. The result of this study is compared with physiological parkinsonian tremor by using the correlation dimension, the largest Lyapunov exponent and the Kolmogorov entropy. The correlation dimension represents the complexity and the largest Lyapunov exponent and the Kolmogorov entropy indicates the chaoticity of the system. This comparison shows that the obtained result based on the purposed model is close to experimental data, so the presented model is an accurate and applicable model.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130680062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043928
M. Yazdi, Mohammad Khalilzadeh, M. Foroughipour
Image segmentation is mostly used as a fundamental step in medical image processing, especially for clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) algorithm is one of the well known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MR images to high number of clusters with different noise levels and real images because of spatial complexities. Anatomical segmentation usually requires information derived from the manual segmentation done by experts, prior knowledge can be useful to modify image segmentation methods. In this article we proposed a method to modify FCM algorithm using expert manual segmentation as prior knowledge. We developed combination of FCM algorithm and prior knowledge in order to modify segmentation of brain MR images with high noise level and spatial complexities. In real images, we had considerable improvement in similarity index of three classes (white matter, gray matter, cerebrospinal fluid) and in simulated images with different noise levels evaluation criteria of white matter and gray matter improved.
{"title":"Fuzzy c-means clustering method based on prior knowledge for brain MR image segmentation","authors":"M. Yazdi, Mohammad Khalilzadeh, M. Foroughipour","doi":"10.1109/ICBME.2014.7043928","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043928","url":null,"abstract":"Image segmentation is mostly used as a fundamental step in medical image processing, especially for clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) algorithm is one of the well known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MR images to high number of clusters with different noise levels and real images because of spatial complexities. Anatomical segmentation usually requires information derived from the manual segmentation done by experts, prior knowledge can be useful to modify image segmentation methods. In this article we proposed a method to modify FCM algorithm using expert manual segmentation as prior knowledge. We developed combination of FCM algorithm and prior knowledge in order to modify segmentation of brain MR images with high noise level and spatial complexities. In real images, we had considerable improvement in similarity index of three classes (white matter, gray matter, cerebrospinal fluid) and in simulated images with different noise levels evaluation criteria of white matter and gray matter improved.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121187854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043905
Seyed Mohammad Reza Noori, Amin Hekmatmanesh, M. Mikaeili, K. Sadeghniiat-haghighi
K-complexes like spindles are hallmark patterns of stage 2 sleep. Due to correlation between these patterns and some diseases, it is necessary to develop algorithms to detect them. In this study, a new method is used to detect K-complexes automatically. 10 time-series and chaotic features were used in order to extract the K-complex waves from stage 2 sleep. To use the most effective features, feature space dimension is reduced with Sequential Forward Selection method. The reduced feature space is classified using Generalized Radial Basis Function Extreme Learning Machine (MELM-GRBF) algorithm. GRBFs make the modification of the RBF possible by adjusting a new parameter τ. We're applied this methodology to K-complex classification for the first time. The classifier gives noticeably better results compared to ELM-RBF method for sensitivity and accuracy 61.00 ± 6.6 and 96.15 ± 3.7, respectively.
{"title":"K-complex identification in sleep EEG using MELM-GRBF classifier","authors":"Seyed Mohammad Reza Noori, Amin Hekmatmanesh, M. Mikaeili, K. Sadeghniiat-haghighi","doi":"10.1109/ICBME.2014.7043905","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043905","url":null,"abstract":"K-complexes like spindles are hallmark patterns of stage 2 sleep. Due to correlation between these patterns and some diseases, it is necessary to develop algorithms to detect them. In this study, a new method is used to detect K-complexes automatically. 10 time-series and chaotic features were used in order to extract the K-complex waves from stage 2 sleep. To use the most effective features, feature space dimension is reduced with Sequential Forward Selection method. The reduced feature space is classified using Generalized Radial Basis Function Extreme Learning Machine (MELM-GRBF) algorithm. GRBFs make the modification of the RBF possible by adjusting a new parameter τ. We're applied this methodology to K-complex classification for the first time. The classifier gives noticeably better results compared to ELM-RBF method for sensitivity and accuracy 61.00 ± 6.6 and 96.15 ± 3.7, respectively.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121636617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-11-01DOI: 10.1109/ICBME.2014.7043916
Arezoo Alizadeh, E. Fatemizadeh, M. Deevband
Autism Spectrum Disorders (ADS), with unknown etiology, is one of the most understudy fields of research worldwide that requires complicated and delicate analytical study methods. The purpose of this study was to compare active regions of Brain Default Mode Network (DMN) using Group Independent Component Analysis (6ICA) among resting state patients with Autism Disorder and healthy subjects. Default Mode Network consists of posterior cingulate cortex (PCC), lateral parietal cortex/angular gyrus retrosplenial cortex, medial prefrontal cortex, superior frontal gyrus, parahippocampal gyrus and temporal lobe shows more prominent activity in passive resting conditions. The diagnosis of autism disorder was According to the clinical ADI-R of ADOS test. After Data preprocessing of rs-fMRI dataset by SPM toolbox, Group independent component analysis was performed in three steps of data reduction, ICA, and back reconstruction using the group ICA of fMRI toolbox (GIFT). Group ICA revealed sixteen Default mode network components which five of them were chosen as DMN components to compare between both groups. The number of voxels in each cluster of autistic individuals was significantly lower than in healthy individuals. Spatial group ICA of fMRI can be a useful approach to determine and study of differences in DMN of brain in patients with autism disorder.
{"title":"Investigation of Brain Default Network's activation in autism spectrum disorders using Group Independent Component Analysis","authors":"Arezoo Alizadeh, E. Fatemizadeh, M. Deevband","doi":"10.1109/ICBME.2014.7043916","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043916","url":null,"abstract":"Autism Spectrum Disorders (ADS), with unknown etiology, is one of the most understudy fields of research worldwide that requires complicated and delicate analytical study methods. The purpose of this study was to compare active regions of Brain Default Mode Network (DMN) using Group Independent Component Analysis (6ICA) among resting state patients with Autism Disorder and healthy subjects. Default Mode Network consists of posterior cingulate cortex (PCC), lateral parietal cortex/angular gyrus retrosplenial cortex, medial prefrontal cortex, superior frontal gyrus, parahippocampal gyrus and temporal lobe shows more prominent activity in passive resting conditions. The diagnosis of autism disorder was According to the clinical ADI-R of ADOS test. After Data preprocessing of rs-fMRI dataset by SPM toolbox, Group independent component analysis was performed in three steps of data reduction, ICA, and back reconstruction using the group ICA of fMRI toolbox (GIFT). Group ICA revealed sixteen Default mode network components which five of them were chosen as DMN components to compare between both groups. The number of voxels in each cluster of autistic individuals was significantly lower than in healthy individuals. Spatial group ICA of fMRI can be a useful approach to determine and study of differences in DMN of brain in patients with autism disorder.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114819583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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