An Exploratory study on default mode network's time course analysis

Akın, Burak
At resting state, some parts of the human brain is more active than the other areas. Furthermore, the resting state activity in these areas exhibit a sudden decrease when the same areas are recruited in a task. Default mode network (DMN) is one of the resting state networks in the human brain, revealed through functional magnetic resonance (fMR) data acquisition while the subject is in an idle state. The aim of this study is to examine time-course properties of resting state default-mode network by using independent component analysis. Two different conditions are used for this purpose, based on Independent Component Analysis (ICA) method: In the first condition, the whole-brain, including GM, WM, CSF areas are admitted for analysis; in the second condition ICA is performed only for the cortical gray matter voxels, which contain the actual neuronal bed from which the BOLD signal in the fMR images are obtained. Our results indicate that performing ICA exclusively on the GM areas does not provide extra benefit, on the contrary, it spoils the inherent characteristics of the fMR signal as observed in some higher order statistics such as increased skewness and kurtosis.


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How neurons influence each other's firing depends on the strength of synaptic connections among them. Motivated by the highly interconnected structure of the brain, in this study, we propose a computational model to estimate the relationships among voxels and employ them as features for cognitive state classification. We represent the sequence of functional Magnetic Resonance Imaging (fMRI) measurements recorded during a cognitive stimulus by a set of local meshes. Then, we represent the corresponding cogni...
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The human brain is a complex and dynamical system, which consists of segregated areas specialized for perceptual or motor processing. Task-specific functions are only carried out by integration of these segregated regions. Thus, in order to understand the human brain, it is very important to understand underlying network structure. There are various metrics to investigate the brain connectivity and each day, new metrics are introduced in the field. This study concentrates on bicoherence analysis. Bicoherenc...
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Aktaş, Hayriye; Gökçay, Didem; Doğru, Ewa; Department of Biomedical Engineering (2013)
During healthy aging, the brain undergoes several structural changes such as brain atrophy, decreased volume of GM and WM and increase in CSF volume. These changes introduce prominent low contrast effects to the MRI images of the aging population, causing segmentation problems in the data processing pipeline. Measures of tissue characteristics such as T1, T2 provide unique and complementary information to widely used measures of brain atrophy. In this study, image quality metrics such as contrast, SNR, CNR ...
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Arslankoz, Kamil; Gökçay, Didem; Department of Medical Informatics (2015)
Functional magnetic resonance imaging (fMRI) utilizes the change in the oxygenation of blood to predict active areas in the brain. fMRI consists of multiple low resolution whole brain images, for which, the contrast difference in corresponding voxels among all images are studied. In this study, an fMRI simulator has been developed which generates customized 4D fMRI data that can be used as a ground truth for comparing/benchmarking different fMRI analysis methods. This simulator can be also used for educatio...
A Sparse Temporal Mesh Model for Brain Decoding
Afrasiyabi, Arman; Onal, Itir; Yarman Vural, Fatoş Tunay (2016-08-23)
One of the major drawbacks of brain decoding from the functional magnetic resonance images (fMRI) is the very high dimension of feature space which consists of thousands of voxels in sequence of brain volumes, recorded during a cognitive stimulus. In this study, we propose a new architecture, called Sparse Temporal Mesh Model (STMM), which reduces the dimension of the feature space by combining the voxel selection methods with the mesh learning method. We, first, select the "most discriminative" voxels usin...
Citation Formats
B. Akın, “An Exploratory study on default mode network’s time course analysis,” M.S. - Master of Science, Middle East Technical University, 2012.