What is the common thread among mRNA vaccines, genomic drugs, NASA’s mission to the moon, and the harnessing of nuclear power? They all have been products of science convergence, where knowledge from multiple scientific disciplines is integrated into new overarching knowledge that propels modern civilization. In the last 70 years, convergence achieved more than what science achieved in all its previous multi-millennial history combined. 

In a new article in American Scientist magazine, professors Ioannis Pavlidis (University of Houston), Ergun Akleman (Texas A&M University), and Alexander M. Petersen (University of California, Merced) show that despite appearances to the contrary, convergence is not a new phenomenon that took science by storm, but a streak that runs deep into science’s nature.

Spanning 10 years, the researchers modeled the evolution of convergence by analyzing millions of scientific works using machine learning and other advanced data analytic methods.

In their account, the researchers identify several stages in the evolution of science, each characterized by a different form of convergence. First, polymathic convergence, which characterized early science up to the Renaissance period, is exemplified by famous polymaths, such as Aristotle and Leonardo da Vinci. In polymathic convergence, knowledge integration was taking place within the minds of singular scholars at the time.

This was followed by a period of disciplinary divergence where theories developed within specific disciplines were turned into generalized templates with broader applications - a phenomenon the authors call convergence through divergence. Darwin's theory of evolution in biology, which was used by others to explain economic and social systems, is a case in point.

Then, by the mid-20th century, dawned the era of multi-disciplinary team convergence, where experts from different disciplines were working together toward a common goal. In multi-disciplinary team convergence, knowledge integration has been taking place across teams of scientists with diverse expertise. A famous example of this type of convergence was the Manhattan Project, which ushered humanity into the nuclear era.

“Now in the early 21st century, we have detected the emergence of yet another form of convergence, which we call polymathic team convergence,” said Pavlidis, Eckhard-Pfeiffer Professor of Computer Science and the director of the Computational Physiology Laboratory at UH. “In polymathic team convergence, knowledge integration takes place both within and across scholars, that is, a mix of individual polymathic and multi-disciplinary team convergence. Recent research in brain science exhibits telltale signs of polymathic team convergence.”

"This is not the first theory about the underlying mechanisms of science evolution. However, it is the first scientific evolution theory that is largely based on massive data analysis and modeling, which allows us to not only ‘prove’ the theory's points for the past, but also estimate the confidence about the theory's predictions for the future," said Pavlidis.

Regarding the latter, the team of researchers predicts that convergence by the mid-21st century will evolve into what they call cyborg team convergence, where polymathic scientists will collaborate with artificial intelligence (AI) agents in mixed human-machine teams.

"Early signs of cyborg team convergence are here and are thoroughly described in our article,” Petersen noted.

A new GPU-based machine learning algorithm developed by researchers at the Indian Institute of Science (IISc) can help scientists better understand and predict connectivity between different regions of the brain.

The algorithm, called Regularized, Accelerated, Linear Fascicle Evaluation, or ReAl-LiFE, can rapidly analyze the enormous amounts of data generated from diffusion Magnetic Resonance Imaging (dMRI) scans of the human brain. Using ReAL-LiFE, the team was able to evaluate 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 the Centre for Neuroscience (CNS), IISc, and corresponding author of the study.

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-behavior relationships at scale,” says Varsha Sreenivasan, a Ph.D. student at CNS and the first author of the study. However, conventional approaches to studying brain connectivity typically use animal models and are invasive. dMRI scans, on the other hand, provide a non-invasive method to study brain connectivity in humans. 

The cables (axons) that connect different areas of the brain are its information highways. 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 network of fibers across the brain, called a connectome.

Unfortunately, it is not straightforward to pinpoint these connectomes. The data obtained from the scans only provide the net flow of water molecules at each point in the brain. “Imagine that the water molecules are cars. The obtained information is the direction and speed of the vehicles at each point in space and time with no information about the roads. Our task is similar to inferring the networks of roads by observing these traffic patterns,” explains Sridharan. 

To identify these networks accurately, conventional algorithms closely match the predicted dMRI signal from the inferred connectome with the observed dMRI signal. Scientists had previously developed an algorithm called LiFE (Linear Fascicle Evaluation) to carry out this optimization, but one of its challenges was that it worked on traditional Central Processing Units (CPUs), which made the computation time-consuming. 

In the new study, Sridharan’s team tweaked their algorithm to cut down the computational effort involved in several ways, including removing redundant connections, thereby improving upon LiFE’s performance significantly. To speed up the algorithm further, the team also redesigned it to work on specialized electronic chips, the kind found in high-performance gaming computers, called Graphics Processing Units (GPUs), which helped them analyze data at speeds 100-150 times faster than previous approaches. 

This improved algorithm, ReAl-LiFE, was also able to predict how a human test subject would behave or carry out a specific task. In other words, using the connection strengths estimated by the algorithm for each individual, the team was able to explain variations in behavioral and cognitive test scores across a group of 200 participants. 

Such analysis can have medical applications too. “Data processing on large scales is becoming increasingly necessary for big-data neuroscience applications, especially for understanding healthy brain function and brain pathology,” says Sreenivasan.

For example, using the obtained connectomes, 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. “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 optimization problems in many other fields as well.