Matt Fuchs is the editor-in-chief of Leaps.org. He is also a contributing reporter to the Washington Post and has written for the New York Times, Time Magazine, WIRED and the Washington Post Magazine, among other outlets. Follow him on Twitter @fuchswriter.
Today's guest is Percy Griffin, director of scientific engagement for the Alzheimer’s Association, a nonprofit that’s focused on speeding up research, finding better ways to detect Alzheimer’s earlier and other approaches for reducing risk. Percy has a doctorate in molecular cell biology from Washington University, he’s led important research on Alzheimer’s, and you can find the link to his full bio in the show notes, below.
Our topic for this conversation is the present and future of the fight against dementia. Billions of dollars have been spent by the National Institutes of Health and biotechs to research new treatments for Alzheimer's and other forms of dementia, but so far there's been little to show for it. Last year, Aduhelm became the first drug to be approved by the FDA for Alzheimer’s in 20 years, but it's received a raft of bad publicity, with red flags about its effectiveness, side effects and cost.
Meanwhile, 6.5 million Americans have Alzheimer's, and this number could increase to 13 million in 2050. Listen to this conversation if you’re concerned about your own brain health, that of family members getting older, or if you’re just concerned about the future of this country with experts predicting the number people over 65 will increase dramatically in the very near future.
Listen to the Episode
4:40 - We talk about the parts of Percy’s life that led to him to concentrate on working in this important area.
6:20 - He defines Alzheimer's and dementia, and discusses the key elements of communicating science.
10:20 - Percy explains why the Alzheimer’s Association has been supportive of Aduhelm, even as others have been critical.
17:58 - We talk about therapeutics under development, which ones to be excited about, and how they could be tailored to a person's own biology.
24:25 - Percy discusses funding and tradeoffs between investing more money into Alzheimer’s research compared to other intractable diseases like cancer, and new opportunities to accelerate progress, such as ARPA-H, President Biden’s proposed agency to speed up health breakthroughs.
27:24 - We talk about the social determinants of brain health. What are the pros/cons of continuing to spend massive sums of money to develop new drugs like Aduhelm versus refocusing on expanding policies to address social determinants - like better education, nutritious food and safe drinking water - that have enabled some groups more than others to enjoy improved cognition late in life.
34:18 - Percy describes his top lifestyle recommendations for protecting your mind.
37:33 - Is napping bad for the brain?
39:39 - Circadian rhythm and Alzheimer's.
42:34 - What tests can people take to check their brain health today, and which biomarkers are we making progress on?
47:25 - Percy highlights important programs run by the Alzheimer’s Association to support advances.
** After this episode was recorded, the Centers for Medicare and Medicaid Services affirmed its decision from last June to limit coverage of Aduhelm. More here.
- Percy Griffin's bio: https://www.alz.org/manh/events/alztalks/upcoming-...
- The Alzheimer's Association's Part the Cloud program: https://alz.org/partthecloud/about-us.asp
- The paradox of dementia rates decreasing: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7455342/
- The argument for focusing more resources on improving institutions and social processes for brain health: https://www.statnews.com/2021/09/23/the-brain-heal...
- Recent research on napping: https://www.ocregister.com/2022/03/25/alzheimers-s...
- The Alzheimer's Association helpline: https://www.alz.org/help-support/resources/helpline
- ALZConnected, a free online community for people affected by dementia https://www.alzconnected.org/
- TrialMatch for people with dementia and healthy volunteers to find clinical trials for Alzheimer's and other dementia: https://www.alz.org/alzheimers-dementia/research_p...
The Friday Five covers five stories in health research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”