Study could help better management of Alzheimer’s patients

New Delhi (India Science Wire): A team of researchers at the Bengaluru-based
Indian Institute of Science (IISc) has developed a GPU-based machine learning algorithm
that promises to help identify early signs of aging or deterioration of brain function before
they manifest behaviourally in Alzheimer’s patients.

Millions of neurons fire in the brain every second, generating electrical pulses that travel
across neuronal networks from one point in the brain to another through connecting cables
or “axons”. These connections are essential for computations that the brain performs.
Understanding brain connectivity is critical for uncovering brain-behaviour relationships at
scale.

The axons are like information highways. Bundles of axons are shaped like tubes, and water
molecules move through them, along their length, in a directed manner. A type of scan
called diffusion Magnetic Resonance Imaging (dMRI) is used to track these movements.
However, the data obtained from the scans only provide the net flow of water molecules at
each point in the brain which is not enough to pinpoint the connections.

“Imagine that the water molecules are cars. The information obtained from the scans is just
the direction and speed of the vehicles at each point in space and time, but with no
information about the roads. The task at hand is similar to inferring the networks of roads
by observing these traffic patterns,” explains Devarajan Sridharan, Associate Professor at
the Centre for Neuroscience (CNS), IISc, and corresponding author of the study.

Scientists had previously developed an algorithm called LiFE (Linear Fascicle Evaluation) to
carry out the work. But, one of its challenges was that it worked on traditional central
processing units (CPUs), which made computation time-consuming.

In the new study, Sridharan’s team tweaked their algorithm to cut down the computational
effort involved in several ways, thereby improving LiFE’s performance significantly. To speed

up the algorithm further, the team redesigned it to work on specialised electronic chips –
the kind found in high-end gaming computers – called Graphics Processing Units (GPUs).
This helped them analyse data 100-150 times faster than previous approaches.

The improved algorithm named ReAl-LiFE could also predict how a human test subject
would behave or carry out a specific task. Using the algorithm, the team could explain
variations in behavioural and cognitive test scores across a group of 200 participants.

The researchers noted that such analysis can have medical applications too as 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, using their new algorithm, the team hopes to be able to identify early signs of
aging or deterioration of brain function before they manifest behaviourally in Alzheimer’s
patients.

Besides Sridharan, the study team comprised Varsha Sreenivasan, Sawan Kumar, Partha
Talukdar, and Franco Pestilli. They have published a report on their work in the science
journal Nature Computational Science. (India Science Wire)

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