Analyzing effective connectivity of brain using fMRI data : DCM and PPI

Download
2013
Mojtahedi, Sina
In neuroscience and biomedical engineering fields, one of the most important issues nowadays is finding a relationship between different brain regions when it is stimulated. Connectivity is an important research area in neuroscience which tries to determine the relationship between different brain region when the brain is stimulated externally or internally. Three main type of connectivity are discussed in this field: Anatomical, Functional and Effective connectivity. Importance of effective connectivity is its ability to detect brain disorders in early stages. Some brain disorders are Schizophrenia, MS and Major Depression disease. Comparing the effective connectivity between a healthy and unhealthy brain will help to diagnose brain disorder. In this master study, two methods named Dynamic Causal Modeling (DCM) and Psychophysiological Interaction (PPI) are used to compare effective connectivity and neuronal activity between different regions of brain when there are three different stimulations. Since the neural activity is latent in fMRI data, there is a need to a model which is able to transfer data from neuronal level to a visible data like Blood-Oxygen level dependent (BOLD) signal. DCM uses a haemodynamic balloon model (HD) to represent this data transfer. The hemodynamic model must be so that the parameters of neural and BOLD signal be the same. It should be noted that what is looked for is not the BOLD signal but the neuronal activity. In this study, as the first step, we did preprocessing of MR images and after ROI`s are created using the program MARSBAR. Ten ROIs, which are thought to have connections between them are selected by considering the stimulations used in the experiments in obtaining the data used in this thesis. The data used contains fMRI images of 11 healthy subjects. Stimulations of experiment are applied to images got from group analysis of 11 healthy subjects. These Stimulations are then used in preparing the design matrix and the parameters related to DCM. These parameters are the values related to connection matrices defining bilinear dynamic model on ROI. Bayesian method is used to select best model between all these models. Another method of PPI is also applied to analyze effective connectivity between 10 ROIs. This method considers two issues of physiological and psychological effects. Like DCM, the preprocessing steps and ROI selection is done for PPI and hemodynamic model is designed for this method. Neural and hemodynamic responses of ROIs are compared using this method.

Suggestions

A Hierarchical representation and decoding of fMRI data by partitioning a brain network
Moğultay, Hazal; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017)
In this study, we propose a hierarchical network representation of human brain extracted from fMRI data. This representation consists of two levels. In the first level, we form a network among the voxels, smallest building block of fMRI data. In the second level, we define a set of supervoxels by partitioning the first level network into a set of subgraphs, which are assu med to represent homogeneous brain regions with respect to a predefined criteria. For this purpose, we develop a novel brain parcellation...
Describing Morphological Changes of Corpus Callosum via Shape Grammar Based Approach
Turgut, Umut Orcun; Gökçay, Didem (2015-10-18)
Despite modern imaging technologies, problems are faced in quantitative brain morphology studies. Since the structural and functional organization of the human brain is complex, advanced methods are needed. Current methods are incapable of detecting complete shape anomalies. Moreover, the rapidly increasing volume of image data forces development of image analysis methodologies that can be processed fast and locally. All of these requirements create the need for an advanced shape analysis technique to chara...
Investigating pain perception in somatosensory cortex for healthy and fibromyalgia patient populations by using fNIRS
Eken, Aykut; Gökçay, Didem; Kara, Murat; Department of Medical Informatics (2016)
In this study, we investigated the difference in hemodynamic responses between fibromyalgia (FM) and healthy controls via functional near infrared spectroscopy (fNIRS) during application of painful stimulus and transcutaneous electrical nerve stimulation (TENS). We collected several clinical data (pain threshold, Beck Depression Inventory (BDI) score, Fibromyalgia Impact Questionnaire (FIQ) score, pain ratings) before and during the experiment. After data collection, we analyzed it using general linear mode...
Bridging Brain and Educational Sciences: An Optical Brain Imaging Study of Visuospatial Reasoning
Çakır, Murat Perit; Izzetoglu, Meltem; Shewokis, Patricia A.; Izzetoglu, Kurtulus; Onaral, Banu (2011-10-22)
In this paper we present an experimental study where we investigated neural correlates of visuospatial reasoning during math problem solving in a computer-based environment to exemplify the potential for conducting interdisciplinary research that incorporates insights from educational research and cognitive neuroscience. Functional near-infrared spectroscopy (fNIRS) technology is used to measure changes in blood oxygenation in the dorsolateral and inferior prefrontal cortex while subjects attempt to solve t...
Representation of human brain by mesh networks
Önal Ertuğrul, Itır; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017)
In this thesis, we propose novel representations to extract discriminative information in functional Magnetic Resonance Imaging (fMRI) data for cognitive state and gender classification. First, we model the local relationship among a set of fMRI time series within a neighborhood by considering temporal information obtained from all measurements in time series. The estimated local relationships, called Mesh Arc Descriptors (MADs), are employed to represent information in fMRI data. Second, we adapt encoding ...
Citation Formats
S. Mojtahedi, “Analyzing effective connectivity of brain using fMRI data : DCM and PPI,” M.S. - Master of Science, Middle East Technical University, 2013.