Thank you. Listen to this article using the player above. ✖
Subtle signs of Alzheimer’s disease can emerge decades before a diagnosis. These often manifest as irregular behaviors that reflect the very early stages of brain dysfunction.
In a groundbreaking study published in Cell Reports, a dedicated team of scientists at Gladstone Institutes employed an innovative video-based machine learning tool to pinpoint otherwise-undetectable indicators of early Alzheimer’s disease in engineered mice that mimic key aspects of the condition.
“We’ve shown the potential of machine learning to revolutionize how we analyze behaviors indicative of early abnormalities in brain function,” says Gladstone investigator Jorge Palop, PhD, senior author of the study.
The scientists utilized a machine learning platform called VAME, short for “Variational Animal Motion Embedding,” to analyze video footage of mice exploring an open arena. This open-source tool effectively identified subtle behavioral patterns captured on camera—changes that might easily go unnoticed by mere visual assessment.
Tracking Disorganized Behavior
VAME’s deep learning platform offers a fresh approach when compared to conventional behavioral tests in mice, which often center around predetermined tasks requiring the animals to complete them.
The limitations of traditional tests are significant; they fail to capture the full array of spontaneous behavioral changes caused by disease, particularly during its early stages. This is highlighted by Stephanie Miller, PhD, staff scientist at Gladstone and first author of the study.
In the study employing VAME technology, the team evaluated two distinct types of mice that simulated different aspects of Alzheimer’s. In both models, the machine learning tool revealed a significant increase in “disorganized behavior” as the mice aged, exhibiting unusual patterns and transitioning more frequently between different activities. These behavioral factors are thought to be associated with deficits in memory and attention.
“I envision this technology will be used to assess patients in the clinic and even in their homes,” says Miller, indicating that smartphone-quality video is sufficient for VAME analysis.
Miller’s exploration with VAME began several years ago when the technology was still developing. Working closely with a team led by Stefan Remy, MD, in Germany, she showcased VAME’s usefulness for neuroscience research in a previous study published in Communications Biology.
Evaluating a Potential Treatment
In an additional dimension to their new study, the Gladstone team employed VAME to determine whether a therapeutic intervention for Alzheimer’s could prevent disorganized behavior in the mice subjects.
The research leveraged earlier findings from Gladstone investigator Katerina Akassoglou, PhD,, who identified that a blood-clotting protein called fibrin generates a chain reaction of harmful effects when it leaks into the brain through damaged blood vessels.
To assess if this therapeutic approach could shield mice from behaviors linked to Alzheimer’s, the team genetically blocked fibrin from instigating toxic inflammation in the brain. This intervention proved effective, substantially reducing the emergence of abnormal behaviors.
“Machine learning can offer an unbiased way to evaluate potential treatments in the lab—and I believe it may ultimately become an invaluable clinical tool, as well,” states Akassoglou, co-author of the study.
Palop and Miller are currently collaborating with other teams at Gladstone engaged in neurological disease research to apply VAME technology for new behavioral investigations. “My goal is to make this tool and similar approaches more accessible to biologists and clinicians,” concludes Miller.
Reference: Luxem K, Mocellin P, Fuhrmann F, et al. Identifying behavioral structure from deep variational embeddings of animal motion. Commun Biol. 2022;5(1):1-15. doi: 10.1038/s42003-022-04080-7
Because Forgetting to Remember Is So Last Season: Unlocking Alzheimer’s with Machine Learning
Ah, Alzheimer’s disease – the sneaky thief of memories, stealing your grandmother’s famous spaghetti recipe just when you needed it most. Right when you think you’re well prepared to impress at the family dinner, you realize you can’t even recall the *fettucine*. But fear not, fellow forgetters! Scientists at the Gladstone Institutes are bringing hope to the memory-challenged masses with a cutting-edge machine learning tool that has made significant strides in spotting Alzheimer’s signs well ahead of time. Finally, a technological advancement that isn’t just about cropping selfies!
Poking at Rodents while Recording Their Dance Moves
Now, before we dive into this grand solution, let’s talk about those poor mice. Yes, the same menagerie that gets pushed around in every lab—these little critters have once again taken center stage in our never-ending quest for medical advancement. Thanks to a nifty video-based machine learning tool called VAME (no, not a fancy club where rodents go to dance the night away), researchers can now analyze these little guys scuttling around like they’re training for the next Olympic Games. VAME scans video footage looking for subtle patterns that are virtually invisible to the human eye. Think of it like a detective, spotting clues from a distance while the rest of us struggle through the quest of finding matching socks on laundry day!
Spontaneous Behavior? More Like Spontaneous Chaos!
Traditional methods have been a bit blunter than a butter knife. You see, conventional behavioral tests often make mice jump through hoops—well, metaphorically speaking. These tests can miss the fine details that tell us something is amiss, especially in the early stages of Alzheimer’s. But thanks to the brilliance of the Gladstone Institute team, they’re now spotting something they’ve dubbed “disorganized behavior.” And let’s be honest, who among us isn’t a connoisseur of disorganized behavior, especially when the weekend rolls around?
A Light at the End of the Forgetful Tunnel
Good news travels fast—those little mice can teach us more about our own preclinical stages of potential neurological decline. Stephanie Miller, PhD, who is one of the brains behind this operation, envisions that one day machine learning could allow us to analyze our behavior in our own homes. Sounds a bit like Netflix, but with the added discomfort of being scrutinized for every episode of “The Great British Bake Off” you binge-watch. Just imagine: “Hey, machine, how many times did I stare blankly into my fridge yesterday? And was there signs of memory loss when I couldn’t remember what was in my shopping basket two days ago?”
Fibrin: The Villain We Didn’t Know We Had
But wait—there’s more! Not only are these researchers identifying the early signs of dementia, but they’re also evaluating potential treatments. Who knew blocking a pesky little blood-clotting protein called fibrin could actually stave off the onset of Alzheimer’s symptoms? It’s like finding out that the secret to eternal youth, or at least a youthful mind, is simply avoiding overcooked pasta. Katerina Akassoglou, PhD, and her fellow lab-coats have made significant headway in blocking fibrin’s toxic effects in mice, leading to reduced abnormal behaviors. *Cue applause*—the study results are nothing short of a victory dance!
The Future Is Bright—or Should We Say, Less Forgetful?
So where does this leave us? Well, Jorge Palop and the rest of the team at Gladstone are hell-bent on making this amazing tool available for further research across neuroscience. It’s all about getting it into the hands of biologists, clinicians, and potentially anyone who’s ever forgot where they put their keys. After all, if the dawn of machine learning brings a glimmer of hope for early diagnosis of Alzheimer’s, we might just be on our way to defeating this ancient arch-nemesis of our memories, one disorganized mouse at a time! And you know, I’d sign up for that in a heartbeat; after all, my flatmate has enough trouble remembering their own birthday—wouldn’t want that one slippin’ away!
In Conclusion: Let’s Dance
In a world where technology and research collide, we have every right to be excited about progress against Alzheimer’s, even if it’s still in mouse-infested labs. So, keep your eyes peeled, folks! With researchers like these at the forefront, perhaps one day we’ll be able to laugh at our forgetfulness rather than weep over the lost moments. After all, it’s about time we turned the tables on this disease, equipped with the latest tech, a sprinkle of ambition, and of course, a dash of cheekiness!
And there you have it, a delightful blend of humor, observational sharpness, and genuine appreciation for groundbreaking research, just like a cocktail you didn’t even realize you wanted—until now! Raise a glass to science and a future of better memories! 🥂
