Abstract
Functional Magnetic Resonance Imaging (fMRI), a non-invasive technique, is used for the recognition of different Cerebral Blood Flow (CBF) and Blood Oxygenated level dependent (BOLD) measures which result into the identification of various neural activities related to different physiological processes such as Hunger Regulation, Water Balancing etc. Different BOLD contrast levels (blood oxygenated and deoxygenated level) specify diversity in various state of human brain functioning subject to various tasks. The proposed model is a hybrid combination of Sparse method (Carroll et al., 2009) and Hypothalamic Hunger Regulation Model i.e. Sparse matrix for Hypothalamic BOLD Signal method (SMHB Method). SMHB method is dynamic and linear in nature. It defines the sparse parameters which act on the mapping between the fMRI signal for hunger regulation process and sparse representation of the signal segmented from the input image by which every voxel of fMRI signal in temporal domain can be expressed as a sparse signal. A sparse model provides a well define results for task based localized activity. It can be applied on a single image as well as an fMRI dataset. The implementation of SMHB method divided into different sub-modules such as Input image analysis and visualization, Linear Voxel Module and Neuro Activation Module. Our study have completed first two module with different pre-processing techniques used for image analysis and linear representation of each voxels of fMRI signal in the form of sparse parameters.
Highlights
The complex technique of Functional Magnetic Resonance Imaging (fMRI) has been studied through different models and numerical simulation
The complex technique of fMRI has been studied through different models and numerical simulation
Blood Oxygenated level dependent (BOLD) fMRI simulators and software has been developed under the challenging environment created by these physiological based parameters
Summary
The complex technique of fMRI has been studied through different models and numerical simulation. The fMRI signal depends on neurovascular factors, brain activity for physiological and homeostatic functions, oxygen metabolism, neurovascular coupling and more [7]. The Homeostatic functions has been described with mathematical perspective under consideration of various spatial and statistical parameters like fractals, entropy, membership function, wavelets and correlation, variance and Skewness respectively [1][2]. An accurate analysis of BOLD contrast gives correct interpretation of the physiological functions. Different biophysical models calibrated and quantify the functional changes. The basic key features of such methods are mono-variant and multivariant, Linear and Non Linear, convolution, regression and covariance etc. BOLD fMRI simulators and software has been developed under the challenging environment created by these physiological based parameters
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