Researchers at Indian Institute of Science (IISc.) have developed a new graphic processing unit (GPU) based machine learning algorithm called Regularised, Accelerated, Linear Fascicle Evaluation (ReAl – LiFE), which will help to obtain a better understanding and in the prediction of connectivity between different regions of human brain.
This algorithm can help analyse extensive data generated from diffusion Magnetic Resonance Imaging (dMRI) scans which helps scientists study the connectivity in the brain at a speed, which is 150 times higher than a regular desktop computer or existing state-of-the-art algorithms. The study has been published in the journal Nature Computational Science.
“Even though it is difficult to pinpoint the connectomes, we are trying to infer information highway network by looking at traffic flow patterns (if molecules are like cars). We look at the movement of water molecules in the brain and we try to infer where the wires are. The water molecules have to travel along the length of the cables (axons), which have connected various parts of the brain. By measuring these lengths of water molecules, we are able to infer which areas are connected,” explained Devarajan Sridharan, Associate Professor at the Centre for Neuroscience (CNS), IISc., and corresponding author of the study.
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