Indexing and Retrieval of Dyanamic Brain Images: Communication Within the Human Brain Author: Arnav Sheth Supervisor: Dr. Lawrence Shepp, Statistics Department, Rutgers University Sponsored by: National Science Foundation, EIA Principal Investigators: Paul Kantor, SCILS/LIS and Steven Hanson, Psychology Abstract Using functional Magnetic Resonance Imaging, there is significant lag in the retrieval of images which prevents the documentation of how various parts of the human brain send and receive signals. This issue in brain imaging is largely left to theory. This poster presents a small model, conceived by Lawrence Shepp, in which the process is assumed to be completely stochastic. This is obviously not accurate, but is a starting point from which a model closer to the actual process can be determined. In its ultimate stage, the model can be used to interpret brain images and data to clearly portray the process by which various parts of the human brain communicate with each other. Brief Introduction to functional Magnetic Resonance Imaging Technique for determining which parts of the brain are activated by different types of physical sensation or activity, such as sight, sound or the movement of a subject's fingers. This "brain mapping" is achieved by setting up an advanced MRI scanner in a special way so that the increased blood flow to the activated areas of the brain shows up on Functional MRI scans. Stimulus: The subject in a typical experiment will lie in the magnet and a particular form of stimulation will be set up. For example, the subject may be told to snap his fingers for a twenty-four second period during the experiment. Then, MRI images of the subject's brain are taken. Two Resolutions: Analysis: High ResolutionLow Resolution Raw Image Colored Blobs 3D Rendering Haemodynamic Response: “Two dominant tissue contrast mechanisms have functional sensitivity in MR imaging and are produced via haemodynamic responses. Note that: Precise changes in brain activation or metabolism are not directly observed, but the effects of local increases in blood flow and microvascular oxygenation on one or more of the above mentioned MR mechanisms can be mapped as a change in raw image intensity.” Noise: The raw images contain “noise” or regions in the brain which are shown as active, but were not really activated. Statistical techniques that are used remove a large proportion of this noise. Stochastic model: Using Monte Carlo simulations, generating random variables from a uniform distribution, a purely stochastic model is produced to model the path of communication from one cortex to another. Four variations: The path of the “particle” is assumed to traverse one pixel at a time or is assumed to traverse five pixels at a time, thus increasing the speed and altering the nature of the path. Further variations on each type of path are the allowing or disallowing of backwards movements of the particle. Varying this characteristic, alters: 1) the speed at which the particle reached its destination; 2) the number of simulations it takes for the destination particle to be reached. Results Movement of One PixelMovement of Five Pixels Conclusion: Applications of the Model Understanding the Brain: By extending the model to three dimensions and controlling the level of randomness within the modeling procedure, the results from this model can be used to better understand the human brain. Improving fMRI Images: The results of the ultimate model should visualize the exact nature of the process by which one cortex sends a signal to another cortex. The visual results shown above, when extended, should be able to eliminate the noise and fill in the gaps that are produced using fMRI. Improving Indexing and Retrieval of Brain Images: The ultimate model should also be able to improve the indexing and retrieval of brain images. By producing clearer fMRI images, known patterns within these images can be identified and stored in a database which would then make retrieval of these images easier and more efficient. The Simulation Model The model is an applet written in the Java programming language. The model is created in two dimensions and assumes that the transmission of signals from one cortex to another is stochastic. Basic Model: The basic model consists of a random number generator and a probability of movement of a third. That is, the chance that the path will go forwards, backwards or diagonally is equal and is one-third. Contrast Generation Contrast Mechanisms: Image intensity observed in MR images is determined by various tissue contrast mechanisms: Proton density T1 and T2 relaxation rates Diffusive processes of proton spin dephasing Loss of proton phase coherence due to tissue magnetic susceptibility variations In-flow of blood plasma protons. BOLD: “Recent data shows that the observed T2* is dependent on the presence of blood deoxygenation and that deoxygenated haemoglobin is a "blood oxygenation level dependent" or "BOLD" effect that can be observed by noninvasive MR imaging at high magnetic fields.” Source: Above image and text from Columbia University fMRI Center. Source: Above image and text from Oxford University fMRI Center website School of Communication, Information and Library Science Research Day March 12, 2003 Rutgers University