AI that can shoot down fighter planes helps treat bipolar disorder

The artificial intelligence that can blow human pilots out of the sky in air-to-air combat accurately predicted treatment outcomes for bipolar disorder, according to a new medical study by the University of Cincinnati.

The findings open a world of possibility for using AI, or machine learning, to treat disease, researchers said.

David Fleck, an associate professor at the UC College of Medicine, and his co-authors used artificial intelligence called "genetic fuzzy trees" to predict how bipolar patients would respond to lithium.

Bipolar disorder, depicted in the TV show "Homeland" and the Oscar-winning "Silver Linings Playbook," affects as many as six million adults in the United States or 4 percent of the adult population in a given year.

"In psychiatry, treatment of bipolar disorder is as much an art as a science," Fleck said. "Patients are fluctuating between periods of mania and depression. Treatments will change during those periods. It's really difficult to treat them appropriately during stages of the illness."

The study authors found that even the best of eight common models used in treating bipolar disorder predicted who would respond to lithium treatment with 75 percent accuracy. By comparison, the model UC researchers developed using AI predicted how patients would respond to lithium 100 percent of the time. Even more impressively, the UC model predicted the actual reduction in manic symptoms after lithium treatment with 92 percent accuracy.

The study authors found that even the best of the eight most common treatments was only effective half the time. But the model UC researchers developed using AI predicted how patients would respond to lithium treatment with 88 percent accuracy and 80 percent accuracy in validation.

It turns out that the same kind of artificial intelligence that outmaneuvered Air Force pilots last year in simulation after simulation at Wright-Patterson Air Force Base is equally adept at making beneficial decisions that can help doctors treat disease. The findings were published this month in the journal Bipolar Disorders.

"What this shows is that an effort funded for aerospace is a game-changer for the field of medicine. And that is awesome," said Kelly Cohen, a professor in UC's College of Engineering and Applied Science.

Cohen's doctoral graduate Nicholas Ernest is founder of the company Psibernetix, Inc., an artificial intelligence development and consultation company. Psibernetix is working on applications such as air-to-air combat, cybersecurity and predictive analytics. Ernest's fuzzy logic algorithm is able to sort vast possibilities to arrive at the best choices in literally the blink of an eye.

"Normally the problems our AIs solve have many, many googolplexes of possible solutions -- effectively infinite," study co-author Ernest said.

His team developed a genetic fuzzy logic called Alpha capable of shooting down human pilots in simulations, even when the computer's aircraft intentionally was handicapped with a slower top speed and less nimble flight characteristics. The system's autonomous real-time decision-making shot down retired U.S. Air Force Col. Gene Lee in every engagement.

"It seemed to be aware of my intentions and reacting instantly to my changes in flight and my missile deployment," Lee said last year. "It knew how to defeat the shot I was taking. It moved instantly between defensive and offensive actions as needed."

The American Institute of Aeronautics and Astronautics honored Cohen and Ernest this year for their "advancement and application of artificial intelligence to large scale, meaningful and challenging aerospace-related problems."

Cohen spent much of his career working with fuzzy-logic based AI in drones. He used a sabbatical from the engineering college to approach the UC College of Medicine with an idea: What if they could apply the amazing predictive power of fuzzy logic to a particularly nettlesome medical problem?

Medicine and avionics have little in common. But each entails an ordered process -- a vast decision tree -- to arrive at the best choices. Fuzzy logic is a system that relies not on specific definitions but generalizations to compensate for uncertainty or statistical noise. This artificial intelligence is called "genetic fuzzy" because it constantly refines its answer, tossing out the lesser choices in a way analogous to the genetic processes of Darwinian natural selection.

Cohen compares it to teaching a child how to recognize a chair. After seeing just a few examples, any child can identify the object people sit in as a chair, regardless of its shape, size or color.

"We do not require a large statistical database to learn. We figure things out. We do something similar to emulate that with fuzzy logic," Cohen said.

Cohen found a receptive audience in Fleck, who was working with UC's former Center for Imaging Research. After all, who better to tackle one of medical science's hardest problems than a rocket scientist? Cohen, an aerospace engineer, felt up to the task.

Ernest said people should not conflate the technology with its applications. The algorithm he developed is not a sentient being like the villains in the "Terminator" movie franchise but merely a tool, he said, albeit a powerful one with seemingly endless applications.

"I get emails and comments every week from would-be John Connors out there who think this will lead to the end of the world," Ernest said.

Ernest's company created EVE, a genetic fuzzy AI that specializes in the creation of other genetic fuzzy AIs. EVE came up with a predictive model for patient data called the LITHium Intelligent Agent or LITHIA for the bipolar study.

"This predictive model taps into the power of fuzzy logic to allow you to make a more informed decision," Ernest said.

And unlike other types of AI, fuzzy logic can describe in simple language why it made its choices, he said.

The researchers teamed up with Dr. Caleb Adler, the UC Department of Psychiatry and Behavioral Neuroscience vice chairman of clinical research, to examine bipolar disorder, a common, recurrent and often lifelong illness. Despite the prevalence of mood disorders, their causes are poorly understood, Adler said.

"Really, it's a black box," Adler said. "We diagnose someone with bipolar disorder. That's a description of their symptoms. But that doesn't mean everyone has the same underlying causes."

Selecting the appropriate treatment can be equally tricky.

"Over the past 15 years there has been an explosion of treatments for mania. We have more options. But we don't know who is going to respond to what," Adler said. "If we could predict who would respond better to treatment, you would save time and consequences."

With appropriate care, bipolar disorder is a manageable chronic illness for patients whose lives can return to normal, he said.

UC's new study, funded in part by a grant from the National Institute of Mental Health, identified 20 patients who were prescribed lithium for eight weeks to treat a manic episode. Fifteen of the 20 patients responded well to the treatment.

The algorithm used an analysis of two types of patient brain scans, among other data, to predict with 100 percent accuracy which patients responded well and which didn't. And the algorithm also predicted the reductions in symptoms at eight weeks, an achievement made even more impressive by the fact that only objective biological data were used for prediction rather than subjective opinions from experienced physicians.

"This is a huge first step and ultimately something that will be very important to psychiatry and across medicine," Adler said.

How much potential does this have to revolutionize medicine?

"I think it's unlimited," Fleck said. "It's a good result. The best way to validate it is to get a new cohort of individuals and apply their data to the system."

Cohen is less reserved in his enthusiasm. He said the model could help personalize medicine to individual patients like never before, making health care both safer and more affordable. Fewer side-effects means fewer hospital visits, less secondary medication and better treatments.

Now the UC researchers and Psibernetix are working on a new study applying fuzzy logic to diagnosing and treating concussions, another condition that has bedeviled doctors.

"The impact on society could be profound," Cohen said.

Mathematicians deliver formal proof of Kepler Conjecture

A team led by mathematician Thomas Hales has delivered a formal proof of the Kepler Conjecture, which is the definitive resolution of a problem that had gone unsolved for more than 300 years. The paper is now available online through Forum of Mathematics, Pi, an open access journal published by Cambridge University Press. This paper not only settles a centuries-old mathematical problem, but is also a major advance in computer verification of complex mathematical proofs.

The Kepler Conjecture was a famous problem in discrete geometry, which asked for the most efficient way to cram spheres into a given space. The answer, while not difficult to guess (it's exactly how oranges are stacked in a supermarket), had been remarkably difficult to prove. Hales and Ferguson originally announced a proof in 1998, but the solution was so long and complicated that a team of a dozen referees spent years working on checking it before giving up.

Explains Henry Cohn, editor of Forum of Mathematics, Pi: "The verdict of the referees was that the proof seemed to work, but they just did not have the time or energy to verify everything comprehensively. The proof was published in 2005, and no irreparable flaws were ever identified, but it was an unsatisfactory situation that the proof was seemingly beyond the ability of the mathematics community to check thoroughly. To address this situation and establish certainty, Hales turned to computers, using techniques of formal verification. He and a team of collaborators wrote out the entire proof in extraordinary detail using strict formal logic, which a computer program then checked with perfect rigor. This paper is the result of their completed work."

Thomas Hales is the Mellon Professor of Mathematics at the University of Pittsburgh. His research spans discrete geometry, representation theory, motivic integration, and formal theorem proving.

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Materials provided by Cambridge University Press.

Multi-dimensional universe' in brain networks

Using mathematics in a novel way in neuroscience, scientists demonstrate that the brain operates on many dimensions, not just the 3 dimensions that we are accustomed to

For most people, it is a stretch of the imagination to understand the world in four dimensions but a new study has discovered structures in the brain with up to eleven dimensions -- ground-breaking work that is beginning to reveal the brain's deepest architectural secrets.

Using algebraic topology in a way that it has never been used before in neuroscience, a team from the Blue Brain Project has uncovered a universe of multi-dimensional geometrical structures and spaces within the networks of the brain.

The research, published today in Frontiers in Computational Neuroscience, shows that these structures arise when a group of neurons forms a clique: each neuron connects to every other neuron in the group in a very specific way that generates a precise geometric object. The more neurons there are in a clique, the higher the dimension of the geometric object.

"We found a world that we had never imagined," says neuroscientist Henry Markram, director of Blue Brain Project and professor at the EPFL in Lausanne, Switzerland, "there are tens of millions of these objects even in a small speck of the brain, up through seven dimensions. In some networks, we even found structures with up to eleven dimensions."

Markram suggests this may explain why it has been so hard to understand the brain. "The mathematics usually applied to study networks cannot detect the high-dimensional structures and spaces that we now see clearly."

If 4D worlds stretch our imagination, worlds with 5, 6 or more dimensions are too complex for most of us to comprehend. This is where algebraic topology comes in: a branch of mathematics that can describe systems with any number of dimensions. The mathematicians who brought algebraic topology to the study of brain networks in the Blue Brain Project were Kathryn Hess from EPFL and Ran Levi from Aberdeen University.

"Algebraic topology is like a telescope and microscope at the same time. It can zoom into networks to find hidden structures -- the trees in the forest -- and see the empty spaces -- the clearings -- all at the same time," explains Hess.

In 2015, Blue Brain published the first digital copy of a piece of the neocortex -- the most evolved part of the brain and the seat of our sensations, actions, and consciousness. In this latest research, using algebraic topology, multiple tests were performed on the virtual brain tissue to show that the multi-dimensional brain structures discovered could never be produced by chance. Experiments were then performed on real brain tissue in the Blue Brain's wet lab in Lausanne confirming that the earlier discoveries in the virtual tissue are biologically relevant and also suggesting that the brain constantly rewires during development to build a network with as many high-dimensional structures as possible.

When the researchers presented the virtual brain tissue with a stimulus, cliques of progressively higher dimensions assembled momentarily to enclose high-dimensional holes, that the researchers refer to as cavities. "The appearance of high-dimensional cavities when the brain is processing information means that the neurons in the network react to stimuli in an extremely organized manner," says Levi. "It is as if the brain reacts to a stimulus by building then razing a tower of multi-dimensional blocks, starting with rods (1D), then planks (2D), then cubes (3D), and then more complex geometries with 4D, 5D, etc. The progression of activity through the brain resembles a multi-dimensional sandcastle that materializes out of the sand and then disintegrates."

The big question these researchers are asking now is whether the intricacy of tasks we can perform depends on the complexity of the multi-dimensional "sandcastles" the brain can build. Neuroscience has also been struggling to find where the brain stores its memories. "They may be 'hiding' in high-dimensional cavities," Markram speculates.

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Materials provided by Frontiers.