The algorithm — Regularised, Accelerated, Linear Fascicle Evaluation, or ReAl-LiFE — can rapidly analyse enormous amounts of data generated from diffusion Magnetic Resonance Imaging (dMRI) scans of the brain, IISc said, adding that using this, the team evaluated dMRI data over 150 times faster than existing state-of-the-art algorithms.
“Tasks that previously took hours to days can be completed within seconds to minutes,” says Devarajan Sridharan, associate professor at IISc’s Centre for Neuroscience (CNS), and corresponding author of the study published in Nature Computational Science.
An IIsc statement read: Millions of neurons fire in the brain every second, generating electrical pulses travelling across neuronal networks from one point in the brain to another through connecting cables or “axons”, which are essential for computations that the brain performs.
While understanding brain connectivity is critical for uncovering brain-behaviour relationships at scale, conventional approaches typically use animal models, and are invasive, Varsha Sreenivasan, PhD student at CNS and first author of the study said, adding “dMRI scans, on the other hand, provide a non-invasive method to study brain connectivity in humans.”
Stating that axons are the brain’s information highways, IISc adds that because bundles of axons are shaped like tubes, water molecules move through them, along their length, in a directed manner.
“…dMRI allows scientists to track this movement to create a comprehensive map of the fibre network across the brain, called a connectome. Unfortunately, it isn’t straightforward to pinpoint connectomes. Data obtained from scans only provide the net flow of water molecules at each point in the brain,” IISc said.
Imagine water molecules are cars, Sridharan says, adding: “Information obtained is the direction and speed of vehicles at each point in space and time with no information about roads. Our task is similar to inferring networks of roads by observing traffic patterns.”
To identify these networks accurately, conventional algorithms closely match predicted dMRI signals from inferred connectome with observed dMRI signals. Scientists had previously developed an algorithm called LiFE (Linear Fascicle Evaluation) for this but one of its challenges was that it worked on traditional central processing units (CPUs), which made computation time-consuming.
Now, Sridharan’s team tweaked the algorithm to cut down computational effort involved in several ways, including removing redundant connections, thereby improving upon LiFE’s performance.
“To speed up the algorithm further, they also redesigned it to work on specialised electronic chips – the kind found in high-end gaming computers – called GPUs, which helped analyse data at speeds 100-150 times faster,” IISc said.
ReAl-LiFE was also able to predict how a human test subject would behave or do a specific task. The team was able to explain variations in behavioural and cognitive test scores across 200 participants.
IISc claimed such analysis can have medical applications too, while Sreenivasan said: “Data processing on large scales is becoming increasingly necessary for big-data neuroscience applications, especially for understanding healthy brain function and brain pathology.”
For example, the team hopes to identify early signs of ageing or deterioration of brain function before they manifest behaviourally in Alzheimer’s patients.
“In another study, we found that a previous version of ReAL-LiFE could do better than other competing algorithms for distinguishing patients with Alzheimer’s disease from healthy controls,” says Sridharan.
He adds that their GPU-based implementation is very general, and can be used to tackle optimisation problems in many other fields as well.