21 June 2013

Breaking Down Brain Function

With the help of TACC supercomputers, researchers create OpenfMRI project to enable data intensive analyses of the mind. Researchers of neuroscience at The University of Texas at Austin, bridge psychology, neuroscience and computer science to understand how the brain creates cognitive functions. fMRI machines map neuronal activity based on the blood oxygen levels in the brain. When a neuron is active, the brain sends extra oxygenated blood, which has a distinct magnetic signature. By recording these signatures at different locations in the brain, neuroscientists can connect and pinpoint various functions―and potentially dysfunctions―with amazing specificity. The age of telepathy is nearly upon us. Brain researchers can now identify what you are looking at using only fMRI scans of your brain. However, actions, motivations and feelings are still hard to identify. They created OpenfMRI, a web-based, supercomputer-powered workflow that makes it easier for researchers to process, share, compare and rapidly analyze brain scans from many different studies. 


Currently, the project has 18 datasets, consisting of data from almost 350 human subjects. The data comes primarily from four main partners―Stanford, Harvard, the University of Colorado and Washington University. The pipeline that researchers developed allows to automatically process, visualize and analyze raw fMRI data, using the powerful Lonestar supercomputer at the Texas Advanced Computing Center (TACC). When fMRI scans are taken, they contain a lot of noisy information that must be cleaned up. In the automated workflow, the supercomputer first determines what parts of the fMRI images represent brain tissue. Next, it computationally reconstructs the 3D surface of the brain based on structural images and projects the data from the fMRI scans onto that surface. Finally, it takes each subject's brain and warps it to correspond to the average brain so a researcher or doctor can ask, across a group of individuals, which areas are turning on during a specific activity. Each of these steps requires large-scale computational power, but they can be done quickly, using Lonestar.

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