“Wakefulness” Part of the Brain Attacked First in Alzheimer’s, Study Says

Lea Grinberg, a neuropathologist and associate professor at the UCSF Memory and Aging Center in San Francisco's Mission Bay, holds slides of brain tissue used for research on August 15, 2019. (Lindsey Moore/KQED)

People who donate their bodies to science might never have dreamed what information lies deep within their brains. Even when that information has to do with sleep. Scientists used to believe that people who napped a lot were at risk for developing Alzheimer’s disease. But Lea Grinberg with the UCSF Memory and Aging Center started to wonder if “risk” was too light a term — what if, instead, napping indicated an early stage of Alzheimer’s?

About a decade ago, Grinberg — a neuropathologist and associate professor — was working with her team to map a protein called tau in donated brains. Some of their data, revealed drastic differences between healthy brains and those from Alzheimer’s patients in the parts of the brain responsible for wakefulness.

Lea Grinberg uses a program that takes a microscope’s magnification of brain tissue on a slide and projects it on a computer screen on August 15, 2019. The different colors represent different biological features in the brain tissue sample, including neurons and tau protein. (Lindsey Moore/KQED)

Wakefulness centers in the brain showed the buildup of tau — a protein that clogs neurons, Grinberg says, and lets debris accumulate. Gradually, these clogged neurons die. Some areas of the diseased brains had lost as much as 75% of their neurons. That may have led to the excessive napping scientists had observed before. Although the team only studied brains from 13 Alzheimer’s patients and 7 healthy individuals, Grinberg says that the degeneration caused by Alzheimer’s was so profound they were sure of its significance.

“We are kind of changing our understanding of what Alzheimer’s disease is,” she says. “It’s not only a memory problem, but it’s a problem in the brain that causes many other symptoms.”

Although these symptoms aren’t as severe as complete loss of memory or motor functions, Grinberg says they can still hold real consequences for a person’s quality of life. “Because if you don’t sleep well every day and if you… are not in the mood to do things like you were before, it’s very disappointing, right? My grandparents were like this.”

Grinberg says it’s important to know whether napping could be an early sign of Alzheimer’s, for treating symptoms and developing drugs that could slow the progression of the disease. Although there are no prescription drugs available to treat tau buildup, she says, a few are in clinical trials.

A public health professor and neuroscientist at UC Berkeley says the new information offers hope to researchers. William Jagust, who has studied Alzheimer’s for over 30 years, says the results could help select patients for clinical trials of new drugs that require early treatment. “It’s also just very important for understanding the evolution of Alzheimer’s disease with the hope that we eventually will have a drug,” he adds.

It’ll be awhile before doctors can diagnose anyone with Alzheimer’s based on how often they doze off. “There’s no practical application of this to clinical medicine as of today,” Jagust says, “but I think it’s on the cutting edge of the very, very important questions.”

By: Allessandra DiCorato

Source: “Wakefulness” Part of the Brain Attacked First in Alzheimer’s, Study Says | KQED

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How The Brain Distinguishes Memories From Perceptions

Kristina Armitage/Quanta Magazine

The neural representations of a perceived image and the memory of it are almost the same. New work shows how and why they are different. Memory and perception seem like entirely distinct experiences, and neuroscientists used to be confident that the brain produced them differently, too. But in the 1990s neuroimaging studies revealed that parts of the brain that were thought to be active only during sensory perception are also active during the recall of memories.

“It started to raise the question of whether a memory representation is actually different from a perceptual representation at all,” said Sam Ling, an associate professor of neuroscience and director of the Visual Neuroscience Lab at Boston University. Could our memory of a beautiful forest glade, for example, be just a re-creation of the neural activity that previously enabled us to see it?

“The argument has swung from being this debate over whether there’s even any involvement of sensory cortices to saying ‘Oh, wait a minute, is there any difference?’” said Christopher Baker, an investigator at the National Institute of Mental Health who runs the learning and plasticity unit. “The pendulum has swung from one side to the other, but it’s swung too far.”

Even if there is a very strong neurological similarity between memories and experiences, we know that they can’t be exactly the same. “People don’t get confused between them,” said Serra Favila, a postdoctoral scientist at Columbia University and the lead author of a recent Nature Communications study. Her team’s work has identified at least one of the ways in which memories and perceptions of images are assembled differently at the neurological level.

Blurry Spots

When we look at the world, visual information about it streams through the photoreceptors of the retina and into the visual cortex, where it is processed sequentially in different groups of neurons. Each group adds new levels of complexity to the image: Simple dots of light turn into lines and edges, then contours, then shapes, then complete scenes that embody what we’re seeing.

In the new study, the researchers focused on a feature of vision processing that’s very important in the early groups of neurons: where things are located in space. The pixels and contours making up an image need to be in the correct places or else the brain will create a shuffled, unrecognizable distortion of what we’re seeing.

The researchers trained participants to memorize the positions of four different patterns on a backdrop that resembled a dartboard. Each pattern was placed in a very specific location on the board and associated with a color at the center of the board. Each participant was tested to make sure that they had memorized this information correctly — that if they saw a green dot, for example, they knew the star shape was at the far left position.

Then, as the participants perceived and remembered the locations of the patterns, the researchers recorded their brain activity. The brain scans allowed the researchers to map out how neurons recorded where something was as well as how they later remembered it. Each neuron attends to one space, or “receptive field,” in the expanse of your vision, such as the lower left corner.

A neuron is “only going to fire when you put something in that little spot,” Favila said. Neurons that are tuned to a certain spot in space tend to cluster together, making their activity easy to detect in brain scans. Previous studies of visual perception established that neurons in the early, lower levels of processing have small receptive fields, and neurons in later, higher levels have larger ones.

This makes sense because the higher-tier neurons are compiling signals from many lower-tier neurons, drawing in information across a wider patch of the visual field. But the bigger receptive field also means lower spatial precision, producing an effect like putting a large blob of ink over North America on a map to indicate New Jersey. In effect, visual processing during perception is a matter of small crisp dots evolving into larger, blurrier but more meaningful blobs.

But when Favila and her colleagues looked at how perceptions and memories were represented in the various areas of the visual cortex, they discovered major differences. As participants recalled the images, the receptive fields in the highest level of visual processing were the same size they had been during perception — but the receptive fields stayed that size down through all the other levels painting the mental image. The remembered image was a large, blurry blob at every stage.

This suggests that when the memory of the image was stored, only the highest-level representation of it was kept. When the memory was experienced again, all the areas of the visual cortex were activated — but their activity was based on the less precise version as an input. So depending on whether information is coming from the retina or from wherever memories are stored, the brain handles and processes it very differently.

Some of the precision of the original perception gets lost on its way into memory, and “you can’t magically get it back,” Favila said. A “really beautiful” aspect of this study was that the researchers could read out the information about a memory directly from the brain rather than rely on the human subject to report what they were seeing, said Adam Steel, a postdoctoral researcher at Dartmouth College. “The empirical work that they did, I think, is really outstanding.”

A Feature or a Bug?

But why are memories recalled in this “blurrier” way? To find out, the researchers created a model of the visual cortex that had different levels of neurons with receptive fields of increasing size. They then simulated an evoked memory by sending a signal through the levels in reverse order. As in the brain scans, the spatial blurriness seen in the level with the largest receptive field persisted through all the rest. That suggests that the remembered image forms in this way due to the hierarchical nature of the visual system, Favila said.

One theory about why the visual system is arranged hierarchically is that it helps with object recognition. If receptive fields were tiny, the brain would need to integrate more information to make sense of what was in view; that could make it hard to recognize something big like the Eiffel Tower, Favila said. The “blurrier” memory image might be the “consequence of having a system that’s been optimized for things like object recognition.”

Yasemin Saplakoglu

By: Yasemin Saplakoglu

Source: How the Brain Distinguishes Memories From Perceptions | Quanta Magazine

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Teaching for Creativity: Flexibility

There is a river of experiences, sensations, thoughts, memories, and visions that roll through the brain from moment to moment.  Each part of this neural river leaves the brain slightly changed.

Because the human brain has neural plasticity, which is the ability to strengthen, weaken, or even prune neurons and synapses, this plasticity at the cellular level allows the brain to adapt, to learn, and to grow at the conscious level, while conscious mental activity guides the plasticity.

The brain adapts to how it is used.  Devote your time to video games and you will develop a brain more adept at video games.  Studying Renaissance architecture will tune your sensibilities to form, shape, and design. During the Renaissance, individual artists were “artist-architect-engineers” skilled in multiple disciplines.  Leonardo Da Vinci’s first job was as a musician.  He painted, sculpted, invented, designed weapons, launched plans to reroute a river, assisted in staging pageants, studied anatomy, and drafted plans for an ideal city.

Today, one is dubbed a Renaissance person for indulging in multiple pursuits or a “jack of all trades, master of none.”  A jack of all trades is considered shallow, a person with skills in many fields, but no single area of expertise.  In the 21st century, we expect expertise.  For better or worse, we live in an era of specialization.  The specialized fields of the last century have become narrower and more focused: for example, a doctor to an oncologist to a small cell oncologist.

And we expect specialists with a single area of expertise to solve problems in their domain.  Of course, we want the specialists to have thorough mastery of their domains.  But too much focus down one microscope and soon everything looks the same.  As the saying goes: to a man with a hammer everything looks like a nail.  The vision necessary for innovation becomes blurred.

Innovation comes from flexible thinking.

Flexibility is the ability to bend without breaking.  Cognitive flexibility is the mental ability to adjust focus from narrow to broad, to switch between different task rules, and to hold multiple concepts in mind at the same time.   Or as F. Scott Fitzgerald wrote:

“The test of a first-rate intelligence is the ability to hold two opposed ideas in the mind at the same time, and still retain the ability to function.” Our 21st century specialists concentrate on a narrowing band of information. Innovation, however, requires new information or seeing known information in a new perspective.  New information and perspective come from exposure to different, often unrelated domains.  Innovation grows from cross-pollination between disciplines, and what you see outside your normal scope of vision.

Watch Flies, See Clues

The river is caramel brown, stagnant in the tropical sun of Southern Thailand.  The torrid heat leaves you in a loop of torpor.  The stillness of the summer heat is punctuated only by the movement of flies.  The lethargy and silence mark the ideal time to think about… robotics.

Dr. Rodney Brooks is a roboticist, inventor, founder of iRobots and the former Panasonic Professor of Robotics at MIT.   In the summer of 1985, he was on vacation from MIT visiting his Thai in-laws who, fearing for his safety, insisted that Brooks stay on the deck of their stilt house built over the river.  Brooks had little to do but watch the flies and the birds and the river.

At the time, Brooks was working with his team at MIT to design and a build an independently mobile robot.  Their prototype robot would move one meter and then stop.  It then spent 15 minutes constructing a new three-dimensional model of the room to be able to move forward another meter. Watching the flies from the stilt house in Thailand, Brooks had an insight.  Their prototype robot drew on all the computer power of the MIT mainframe and could only move one meter every 15 minutes.

I watched these insects moving around on the ground and in the house.  And– these insects certainly didn’t have much in the way of brains, certainly in the number of neurons that we sort of knew that insects had at the time and sort of knew how fast they computed.  They didn’t have as much computation as our big main frames really…well…so it seemed strange to me that they could just wander around– if they’re flying– at a meter per second and our mobile robots could only move at a meter in 15 minutes.  Something, something didn’t compute.

… So, I started to think, well, well, what are they doing? [Flies] can’t be doing what our robots are doing.  And our robots at the time were looking with their cameras and then trying to build a complete 3-dimensional model—computationally. And I guessed that probably the insects weren’t doing that.”

Insects are the most ubiquitous and numerous animals on the earth.  Their evolutionary success is not based on intelligence, insight, or computation.  It occurred to Brooks that building a complete model of the world hindered a mobile robot.  Why not give the robot the instincts of a bug?

Roboticists were working from an assumption that the robot needed a control center, a cognition box, as if robots needed the higher cognitive abilities of humans.  Brooks concluded that flies engaged in sensory-motor coupling, that is an action taken in response to the environment.  As Brooks said:

“While watching videos of insects walking over rough terrain, I noticed that many times they would miss a foot placement and stumble.” The other insect legs responded instinctively to overcome the stumble. In another example, an insect calculates how quickly something is looming.  It’s a calculation made on the fly, if you will, without processing the information through a central cognition box.  For Brooks, the key was the simplicity of the cues and responses:

“So, I started thinking about how I can put those simple cues into a robot and then I built … the robot Allen. It could wander around at a slow walking speed, but not only could it wander around and not hit things, it could wander around and not hit crowds of people moving through its… its world.”

It takes a flexible mind to watch flies and see clues to robot movement.  Brooks keeps his mind open to all different kinds of information, both inside and outside the field of robotics:

“I just read a lot of stuff. …It filters by, and I pick stuff out… I got lots of websites and lots of newsfeeds that I just continuously read.  And I get little clues.  Someone says “oh, there’s some interesting thing that happens with this animal” and then I’ll take that, and I’ll go and look up the scientific literature on it.  I’m a sort of a sifter.”

Choose to See More Closely

Present in an unfamiliar environment, Brooks created an unexpected analogy using only what he could see.  He jumped disciplines to see how flying insects might inspire the design of robot movement.  He chose to see the world differently: these may be flying insects, but what could they also be?

H-IQ pushes students and adult participants to look at the world with fresh eyes.  Obviously, Brooks had seen flies before.  We all have.  But on the deck in Thailand, Brooks chose to see more closely: how do they fly with such tiny brains?

After you’ve generated ideas, H-IQ presents questions that help students see ideas from different perspectives.  H-IQ also provides a period of idea incubation.  Incubation— a time without conscious mental effort—will help your idea circulate through your subconscious.

Effective imagination requires finding a balance between thorough knowledge of the domain and an openness to looking outside the domain for new insights.

H-IQ, the Hunter Imagination Questionnaire, will be available soon in a completely revised form. H-IQ will help you capture your ideas and stir up your imagination. But it will also engage you with metacognition—thinking about your own thinking: how do you generate ideas? What works for you?

By: H-IQ is the on-line imagination tool that jumpstarts student creativity and your own

Source: Teaching for Creativity: Flexibility

Related contents:

Spotted around the web: Neuroscience tools, cerebellar enhancers, private equity

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The Science of Mind Reading

One night in October, 2009, a young man lay in an fMRI scanner in Liège, Belgium. Five years earlier, he’d suffered a head trauma in a motorcycle accident, and since then he hadn’t spoken. He was said to be in a “vegetative state.” A neuroscientist named Martin Monti sat in the next room, along with a few other researchers. For years, Monti and his postdoctoral adviser, Adrian Owen, had been studying vegetative patients, and they had developed two controversial hypotheses.

First, they believed that someone could lose the ability to move or even blink while still being conscious; second, they thought that they had devised a method for communicating with such “locked-in” people by detecting their unspoken thoughts.

In a sense, their strategy was simple. Neurons use oxygen, which is carried through the bloodstream inside molecules of hemoglobin. Hemoglobin contains iron, and, by tracking the iron, the magnets in fMRI machines can build maps of brain activity. Picking out signs of consciousness amid the swirl seemed nearly impossible. But, through trial and error, Owen’s group had devised a clever protocol.

They’d discovered that if a person imagined walking around her house there was a spike of activity in her parahippocampal gyrus—a finger-shaped area buried deep in the temporal lobe. Imagining playing tennis, by contrast, activated the premotor cortex, which sits on a ridge near the skull. The activity was clear enough to be seen in real time with an fMRI machine. In a 2006 study published in the journal Science, the researchers reported that they had asked a locked-in person to think about tennis, and seen, on her brain scan, that she had done so.

With the young man, known as Patient 23, Monti and Owen were taking a further step: attempting to have a conversation. They would pose a question and tell him that he could signal “yes” by imagining playing tennis, or “no” by thinking about walking around his house. In the scanner control room, a monitor displayed a cross-section of Patient 23’s brain. As different areas consumed blood oxygen, they shimmered red, then bright orange. Monti knew where to look to spot the yes and the no signals.

He switched on the intercom and explained the system to Patient 23. Then he asked the first question: “Is your father’s name Alexander?” The man’s premotor cortex lit up. He was thinking about tennis—yes.

“Is your father’s name Thomas?”

Activity in the parahippocampal gyrus. He was imagining walking around his house—no.

“Do you have any brothers?”

Tennis—yes.

“Do you have any sisters?”

House—no.

“Before your injury, was your last vacation in the United States?”

Tennis—yes.

The answers were correct. Astonished, Monti called Owen, who was away at a conference. Owen thought that they should ask more questions. The group ran through some possibilities. “Do you like pizza?” was dismissed as being too imprecise. They decided to probe more deeply. Monti turned the intercom back on.

That winter, the results of the study were published in The New England Journal of Medicine. The paper caused a sensation. The Los Angeles Times wrote a story about it, with the headline “Brains of Vegetative Patients Show Life.” Owen eventually estimated that twenty per cent of patients who were presumed to be vegetative were actually awake. This was a discovery of enormous practical consequence: in subsequent years, through painstaking fMRI sessions, Owen’s group found many patients who could interact with loved ones and answer questions about their own care.

The conversations improved their odds of recovery. Still, from a purely scientific perspective, there was something unsatisfying about the method that Monti and Owen had developed with Patient 23. Although they had used the words “tennis” and “house” in communicating with him, they’d had no way of knowing for sure that he was thinking about those specific things. They had been able to say only that, in response to those prompts, thinking was happening in the associated brain areas. “Whether the person was imagining playing tennis, football, hockey, swimming—we don’t know,” Monti told me recently.

During the past few decades, the state of neuroscientific mind reading has advanced substantially. Cognitive psychologists armed with an fMRI machine can tell whether a person is having depressive thoughts; they can see which concepts a student has mastered by comparing his brain patterns with those of his teacher. By analyzing brain scans, a computer system can edit together crude reconstructions of movie clips you’ve watched. One research group has used similar technology to accurately describe the dreams of sleeping subjects.

In another lab, scientists have scanned the brains of people who are reading the J. D. Salinger short story “Pretty Mouth and Green My Eyes,” in which it is unclear until the end whether or not a character is having an affair. From brain scans alone, the researchers can tell which interpretation readers are leaning toward, and watch as they change their minds.

I first heard about these studies from Ken Norman, the fifty-year-old chair of the psychology department at Princeton University and an expert on thought decoding. Norman works at the Princeton Neuroscience Institute, which is housed in a glass structure, constructed in 2013, that spills over a low hill on the south side of campus. P.N.I. was conceived as a center where psychologists, neuroscientists, and computer scientists could blend their approaches to studying the mind; M.I.T. and Stanford have invested in similar cross-disciplinary institutes.

At P.N.I., undergraduates still participate in old-school psych experiments involving surveys and flash cards. But upstairs, in a lab that studies child development, toddlers wear tiny hats outfitted with infrared brain scanners, and in the basement the skulls of genetically engineered mice are sliced open, allowing individual neurons to be controlled with lasers. A server room with its own high-performance computing cluster analyzes the data generated from these experiments.

Norman, whose jovial intelligence and unruly beard give him the air of a high-school science teacher, occupies an office on the ground floor, with a view of a grassy field. The bookshelves behind his desk contain the intellectual DNA of the institute, with William James next to texts on machine learning. Norman explained that fMRI machines hadn’t advanced that much; instead, artificial intelligence had transformed how scientists read neural data.

This had helped shed light on an ancient philosophical mystery. For centuries, scientists had dreamed of locating thought inside the head but had run up against the vexing question of what it means for thoughts to exist in physical space. When Erasistratus, an ancient Greek anatomist, dissected the brain, he suspected that its many folds were the key to intelligence, but he could not say how thoughts were packed into the convoluted mass.

In the seventeenth century, Descartes suggested that mental life arose in the pineal gland, but he didn’t have a good theory of what might be found there. Our mental worlds contain everything from the taste of bad wine to the idea of bad taste. How can so many thoughts nestle within a few pounds of tissue?

Now, Norman explained, researchers had developed a mathematical way of understanding thoughts. Drawing on insights from machine learning, they conceived of thoughts as collections of points in a dense “meaning space.” They could see how these points were interrelated and encoded by neurons. By cracking the code, they were beginning to produce an inventory of the mind. “The space of possible thoughts that people can think is big—but it’s not infinitely big,” Norman said. A detailed map of the concepts in our minds might soon be within reach.

Norman invited me to watch an experiment in thought decoding. A postdoctoral student named Manoj Kumar led us into a locked basement lab at P.N.I., where a young woman was lying in the tube of an fMRI scanner. A screen mounted a few inches above her face played a slide show of stock images: an empty beach, a cave, a forest.

“We want to get the brain patterns that are associated with different subclasses of scenes,” Norman said.

As the woman watched the slide show, the scanner tracked patterns of activation among her neurons. These patterns would be analyzed in terms of “voxels”—areas of activation that are roughly a cubic millimetre in size. In some ways, the fMRI data was extremely coarse: each voxel represented the oxygen consumption of about a million neurons, and could be updated only every few seconds, significantly more slowly than neurons fire.

But, Norman said, “it turned out that that information was in the data we were collecting—we just weren’t being as smart as we possibly could about how we’d churn through that data.” The breakthrough came when researchers figured out how to track patterns playing out across tens of thousands of voxels at a time, as though each were a key on a piano, and thoughts were chords.

The origins of this approach, I learned, dated back nearly seventy years, to the work of a psychologist named Charles Osgood. When he was a kid, Osgood received a copy of Roget’s Thesaurus as a gift. Poring over the book, Osgood recalled, he formed a “vivid image of words as clusters of starlike points in an immense space.” In his postgraduate days, when his colleagues were debating how cognition could be shaped by culture, Osgood thought back on this image. He wondered if, using the idea of “semantic space,” it might be possible to map the differences among various styles of thinking.

Osgood conducted an experiment. He asked people to rate twenty concepts on fifty different scales. The concepts ranged widely: BOULDER, ME, TORNADO, MOTHER. So did the scales, which were defined by opposites: fair-unfair, hot-cold, fragrant-foul. Some ratings were difficult: is a TORNADO fragrant or foul? But the idea was that the method would reveal fine and even elusive shades of similarity and difference among concepts.

“Most English-speaking Americans feel that there is a difference, somehow, between ‘good’ and ‘nice’ but find it difficult to explain,” Osgood wrote. His surveys found that, at least for nineteen-fifties college students, the two concepts overlapped much of the time. They diverged for nouns that had a male or female slant. MOTHER might be rated nice but not good, and COP vice versa. Osgood concluded that “good” was “somewhat stronger, rougher, more angular, and larger” than “nice.”

Osgood became known not for the results of his surveys but for the method he invented to analyze them. He began by arranging his data in an imaginary space with fifty dimensions—one for fair-unfair, a second for hot-cold, a third for fragrant-foul, and so on. Any given concept, like TORNADO, had a rating on each dimension—and, therefore, was situated in what was known as high-dimensional space. Many concepts had similar locations on multiple axes: kind-cruel and honest-dishonest, for instance. Osgood combined these dimensions. Then he looked for new similarities, and combined dimensions again, in a process called “factor analysis.”

When you reduce a sauce, you meld and deepen the essential flavors. Osgood did something similar with factor analysis. Eventually, he was able to map all the concepts onto a space with just three dimensions. The first dimension was “evaluative”—a blend of scales like good-bad, beautiful-ugly, and kind-cruel. The second had to do with “potency”: it consolidated scales like large-small and strong-weak. The third measured how “active” or “passive” a concept was. Osgood could use these three key factors to locate any concept in an abstract space. Ideas with similar coördinates, he argued, were neighbors in meaning.

For decades, Osgood’s technique found modest use in a kind of personality test. Its true potential didn’t emerge until the nineteen-eighties, when researchers at Bell Labs were trying to solve what they called the “vocabulary problem.” People tend to employ lots of names for the same thing. This was an obstacle for computer users, who accessed programs by typing words on a command line. George Furnas, who worked in the organization’s human-computer-interaction group, described using the company’s internal phone book.

“You’re in your office, at Bell Labs, and someone has stolen your calculator,” he said. “You start putting in ‘police,’ or ‘support,’ or ‘theft,’ and it doesn’t give you what you want. Finally, you put in ‘security,’ and it gives you that. But it actually gives you two things: something about the Bell Savings and Security Plan, and also the thing you’re looking for.” Furnas’s group wanted to automate the finding of synonyms for commands and search terms.

They updated Osgood’s approach. Instead of surveying undergraduates, they used computers to analyze the words in about two thousand technical reports. The reports themselves—on topics ranging from graph theory to user-interface design—suggested the dimensions of the space; when multiple reports used similar groups of words, their dimensions could be combined.

In the end, the Bell Labs researchers made a space that was more complex than Osgood’s. It had a few hundred dimensions. Many of these dimensions described abstract or “latent” qualities that the words had in common—connections that wouldn’t be apparent to most English speakers. The researchers called their technique “latent semantic analysis,” or L.S.A.

At first, Bell Labs used L.S.A. to create a better internal search engine. Then, in 1997, Susan Dumais, one of Furnas’s colleagues, collaborated with a Bell Labs cognitive scientist, Thomas Landauer, to develop an A.I. system based on it. After processing Grolier’s American Academic Encyclopedia, a work intended for young students, the A.I. scored respectably on the multiple-choice Test of English as a Foreign Language. That year, the two researchers co-wrote a paper that addressed the question “How do people know as much as they do with as little information as they get?”

They suggested that our minds might use something like L.S.A., making sense of the world by reducing it to its most important differences and similarities, and employing this distilled knowledge to understand new things. Watching a Disney movie, for instance, I immediately identify a character as “the bad guy”: Scar, from “The Lion King,” and Jafar, from “Aladdin,” just seem close together. Perhaps my brain uses factor analysis to distill thousands of attributes—height, fashion sense, tone of voice—into a single point in an abstract space. The perception of bad-guy-ness becomes a matter of proximity.

In the following years, scientists applied L.S.A. to ever-larger data sets. In 2013, researchers at Google unleashed a descendant of it onto the text of the whole World Wide Web. Google’s algorithm turned each word into a “vector,” or point, in high-dimensional space. The vectors generated by the researchers’ program, word2vec, are eerily accurate: if you take the vector for “king” and subtract the vector for “man,” then add the vector for “woman,” the closest nearby vector is “queen.”

Word vectors became the basis of a much improved Google Translate, and enabled the auto-completion of sentences in Gmail. Other companies, including Apple and Amazon, built similar systems. Eventually, researchers realized that the “vectorization” made popular by L.S.A. and word2vec could be used to map all sorts of things. Today’s facial-recognition systems have dimensions that represent the length of the nose and the curl of the lips, and faces are described using a string of coördinates in “face space.” Chess A.I.s use a similar trick to “vectorize” positions on the board.

The technique has become so central to the field of artificial intelligence that, in 2017, a new, hundred-and-thirty-five-million-dollar A.I. research center in Toronto was named the Vector Institute. Matthew Botvinick, a professor at Princeton whose lab was across the hall from Norman’s, and who is now the head of neuroscience at DeepMind, Alphabet’s A.I. subsidiary, told me that distilling relevant similarities and differences into vectors was “the secret sauce underlying all of these A.I. advances.”

In 2001, a scientist named Jim Haxby brought machine learning to brain imaging: he realized that voxels of neural activity could serve as dimensions in a kind of thought space. Haxby went on to work at Princeton, where he collaborated with Norman. The two scientists, together with other researchers, concluded that just a few hundred dimensions were sufficient to capture the shades of similarity and difference in most fMRI data. At the Princeton lab, the young woman watched the slide show in the scanner.

With each new image—beach, cave, forest—her neurons fired in a new pattern. These patterns would be recorded as voxels, then processed by software and transformed into vectors. The images had been chosen because their vectors would end up far apart from one another: they were good landmarks for making a map. Watching the images, my mind was taking a trip through thought space, too.

The larger goal of thought decoding is to understand how our brains mirror the world. To this end, researchers have sought to watch as the same experiences affect many people’s minds simultaneously. Norman told me that his Princeton colleague Uri Hasson has found movies especially useful in this regard. They “pull people’s brains through thought space in synch,” Norman said. “What makes Alfred Hitchcock the master of suspense is that all the people who are watching the movie are having their brains yanked in unison. It’s like mind control in the literal sense.”

One afternoon, I sat in on Norman’s undergraduate class “fMRI Decoding: Reading Minds Using Brain Scans.” As students filed into the auditorium, setting their laptops and water bottles on tables, Norman entered wearing tortoiseshell glasses and earphones, his hair dishevelled.

He had the class watch a clip from “Seinfeld” in which George, Susan (an N.B.C. executive he is courting), and Kramer are hanging out with Jerry in his apartment. The phone rings, and Jerry answers: it’s a telemarketer. Jerry hangs up, to cheers from the studio audience.

“Where was the event boundary in the clip?” Norman asked. The students yelled out in chorus, “When the phone rang!” Psychologists have long known that our minds divide experiences into segments; in this case, it was the phone call that caused the division.

Norman showed the class a series of slides. One described a 2017 study by Christopher Baldassano, one of his postdocs, in which people watched an episode of the BBC show “Sherlock” while in an fMRI scanner. Baldassano’s guess going into the study was that some voxel patterns would be in constant flux as the video streamed—for instance, the ones involved in color processing. Others would be more stable, such as those representing a character in the show.

The study confirmed these predictions. But Baldassano also found groups of voxels that held a stable pattern throughout each scene, then switched when it was over. He concluded that these constituted the scenes’ voxel “signatures.” Norman described another study, by Asieh Zadbood, in which subjects were asked to narrate “Sherlock” scenes—which they had watched earlier—aloud.

The audio was played to a second group, who’d never seen the show. It turned out that no matter whether someone watched a scene, described it, or heard about it, the same voxel patterns recurred. The scenes existed independently of the show, as concepts in people’s minds.

Through decades of experimental work, Norman told me later, psychologists have established the importance of scripts and scenes to our intelligence. Walking into a room, you might forget why you came in; this happens, researchers say, because passing through the doorway brings one mental scene to a close and opens another.

Conversely, while navigating a new airport, a “getting to the plane” script knits different scenes together: first the ticket counter, then the security line, then the gate, then the aisle, then your seat. And yet, until recently, it wasn’t clear what you’d find if you went looking for “scripts” and “scenes” in the brain.

In a recent P.N.I. study, Norman said, people in an fMRI scanner watched various movie clips of characters in airports. No matter the particulars of each clip, the subjects’ brains all shimmered through the same series of events, in keeping with boundary-defining moments that any of us would recognize. The scripts and the scenes were real—it was possible to detect them with a machine. What most interests Norman now is how they are learned in the first place.

How do we identify the scenes in a story? When we enter a strange airport, how do we know intuitively where to look for the security line? The extraordinary difficulty of such feats is obscured by how easy they feel—it’s rare to be confused about how to make sense of the world. But at some point everything was new. When I was a toddler, my parents must have taken me to the supermarket for the first time; the fact that, today, all supermarkets are somehow familiar dims the strangeness of that experience.

When I was learning to drive, it was overwhelming: each intersection and lane change seemed chaotic in its own way. Now I hardly have to think about them. My mind instantly factors out all but the important differences.

Norman clicked through the last of his slides. Afterward, a few students wandered over to the lectern, hoping for an audience with him. For the rest of us, the scene was over. We packed up, climbed the stairs, and walked into the afternoon sun.

Like Monti and Owen with Patient 23, today’s thought-decoding researchers mostly look for specific thoughts that have been defined in advance. But a “general-purpose thought decoder,” Norman told me, is the next logical step for the research. Such a device could speak aloud a person’s thoughts, even if those thoughts have never been observed in an fMRI machine. In 2018, Botvinick, Norman’s hall mate, co-wrote a paper in the journal Nature Communications titled “Toward a Universal Decoder of Linguistic Meaning from Brain Activation.”

Botvinick’s team had built a primitive form of what Norman described: a system that could decode novel sentences that subjects read silently to themselves. The system learned which brain patterns were evoked by certain words, and used that knowledge to guess which words were implied by the new patterns it encountered.

The work at Princeton was funded by iARPA, an R. & D. organization that’s run by the Office of the Director of National Intelligence. Brandon Minnery, the iARPA project manager for the Knowledge Representation in Neural Systems program at the time, told me that he had some applications in mind. If you knew how knowledge was represented in the brain, you might be able to distinguish between novice and expert intelligence agents. You might learn how to teach languages more effectively by seeing how closely a student’s mental representation of a word matches that of a native speaker.

Minnery’s most fanciful idea—“Never an official focus of the program,” he said—was to change how databases are indexed. Instead of labelling items by hand, you could show an item to someone sitting in an fMRI scanner—the person’s brain state could be the label. Later, to query the database, someone else could sit in the scanner and simply think of whatever she wanted. The software could compare the searcher’s brain state with the indexer’s. It would be the ultimate solution to the vocabulary problem.

Jack Gallant, a professor at Berkeley who has used thought decoding to reconstruct video montages from brain scans—as you watch a video in the scanner, the system pulls up frames from similar YouTube clips, based only on your voxel patterns—suggested that one group of people interested in decoding were Silicon Valley investors. “A future technology would be a portable hat—like a thinking hat,” he said.

He imagined a company paying people thirty thousand dollars a year to wear the thinking hat, along with video-recording eyeglasses and other sensors, allowing the system to record everything they see, hear, and think, ultimately creating an exhaustive inventory of the mind. Wearing the thinking hat, you could ask your computer a question just by imagining the words. Instantaneous translation might be possible. In theory, a pair of wearers could skip language altogether, conversing directly, mind to mind. Perhaps we could even communicate across species.

Among the challenges the designers of such a system would face, of course, is the fact that today’s fMRI machines can weigh more than twenty thousand pounds. There are efforts under way to make powerful miniature imaging devices, using lasers, ultrasound, or even microwaves. “It’s going to require some sort of punctuated-equilibrium technology revolution,” Gallant said. Still, the conceptual foundation, which goes back to the nineteen-fifties, has been laid.

Recently, I asked Owen what the new thought-decoding technology meant for locked-in patients. Were they close to having fluent conversations using something like the general-purpose thought decoder? “Most of that stuff is group studies in healthy participants,” Owen told me. “The really tricky problem is doing it in a single person. Can you get robust enough data?” Their bare-bones protocol—thinking about tennis equals yes; thinking about walking around the house equals no—relied on straightforward signals that were statistically robust.

It turns out that the same protocol, combined with a series of yes-or-no questions (“Is the pain in the lower half of your body? On the left side?”), still works best. “Even if you could do it, it would take longer to decode them saying ‘it is in my right foot’ than to go through a simple series of yes-or-no questions,” Owen said. “For the most part, I’m quietly sitting and waiting. I have no doubt that, some point down the line, we will be able to read minds. People will be able to articulate, ‘My name is Adrian, and I’m British,’ and we’ll be able to decode that from their brain. I don’t think it’s going to happen in probably less than twenty years.”

In some ways, the story of thought decoding is reminiscent of the history of our understanding of the gene. For about a hundred years after the publication of Charles Darwin’s “On the Origin of Species,” in 1859, the gene was an abstraction, understood only as something through which traits passed from parent to child. As late as the nineteen-fifties, biologists were still asking what, exactly, a gene was made of. When James Watson and Francis Crick finally found the double helix, in 1953, it became clear how genes took physical form. Fifty years later, we could sequence the human genome; today, we can edit it.

Thoughts have been an abstraction for far longer. But now we know what they really are: patterns of neural activation that correspond to points in meaning space. The mind—the only truly private place—has become inspectable from the outside. In the future, a therapist, wanting to understand how your relationships run awry, might examine the dimensions of the patterns your brain falls into.

Some epileptic patients about to undergo surgery have intracranial probes put into their brains; researchers can now use these probes to help steer the patients’ neural patterns away from those associated with depression. With more fine-grained control, a mind could be driven wherever one liked. (The imagination reels at the possibilities, for both good and ill.) Of course, we already do this by thinking, reading, watching, talking—actions that, after I’d learned about thought decoding, struck me as oddly concrete. I could picture the patterns of my thoughts flickering inside my mind. Versions of them are now flickering in yours.

On one of my last visits to Princeton, Norman and I had lunch at a Japanese restaurant called Ajiten. We sat at a counter and went through the familiar script. The menus arrived; we looked them over. Norman noticed a dish he hadn’t seen before—“a new point in ramen space,” he said. Any minute now, a waiter was going to interrupt politely to ask if we were ready to order.

“You have to carve the world at its joints, and figure out: what are the situations that exist, and how do these situations work?” Norman said, while jazz played in the background. “And that’s a very complicated problem. It’s not like you’re instructed that the world has fifteen different ways of being, and here they are!” He laughed. “When you’re out in the world, you have to try to infer what situation you’re in.” We were in the lunch-at-a-Japanese-restaurant situation. I had never been to this particular restaurant, but nothing about it surprised me. This, it turns out, might be one of the highest accomplishments in nature.

Norman told me that a former student of his, Sam Gershman, likes using the terms “lumping” and “splitting” to describe how the mind’s meaning space evolves. When you encounter a new stimulus, do you lump it with a concept that’s familiar, or do you split off a new concept? When navigating a new airport, we lump its metal detector with those we’ve seen before, even if this one is a different model, color, and size. By contrast, the first time we raised our hands inside a millimetre-wave scanner—the device that has replaced the walk-through metal detector—we split off a new category.

Norman turned to how thought decoding fit into the larger story of the study of the mind. “I think we’re at a point in cognitive neuroscience where we understand a lot of the pieces of the puzzle,” he said. The cerebral cortex—a crumply sheet laid atop the rest of the brain—warps and compresses experience, emphasizing what’s important. It’s in constant communication with other brain areas, including the hippocampus, a seahorse-shaped structure in the inner part of the temporal lobe.

For years, the hippocampus was known only as the seat of memory; patients who’d had theirs removed lived in a perpetual present. Now we were seeing that the hippocampus stores summaries provided to it by the cortex: the sauce after it’s been reduced. We cope with reality by building a vast library of experience—but experience that has been distilled along the dimensions that matter. Norman’s research group has used fMRI technology to find voxel patterns in the cortex that are reflected in the hippocampus. Perhaps the brain is like a hiker comparing the map with the territory.

In the past few years, Norman told me, artificial neural networks that included basic models of both brain regions had proved surprisingly powerful. There was a feedback loop between the study of A.I. and the study of the real human mind, and it was getting faster. Theories about human memory were informing new designs for A.I. systems, and those systems, in turn, were suggesting ideas about what to look for in real human brains. “It’s kind of amazing to have gotten to this point,” he said.

On the walk back to campus, Norman pointed out the Princeton University Art Museum. It was a treasure, he told me.

“What’s in there?” I asked.

“Great art!” he said

After we parted ways, I returned to the museum. I went to the downstairs gallery, which contains artifacts from the ancient world. Nothing in particular grabbed me until I saw a West African hunter’s tunic. It was made of cotton dyed the color of dark leather. There were teeth hanging from it, and claws, and a turtle shell—talismans from past kills. It struck me, and I lingered for a moment before moving on.

Six months later, I went with some friends to a small house in upstate New York. On the wall, out of the corner of my eye, I noticed what looked like a blanket—a kind of fringed, hanging decoration made of wool and feathers. It had an odd shape; it seemed to pull toward something I’d seen before. I stared at it blankly. Then came a moment of recognition, along dimensions I couldn’t articulate—more active than passive, partway between alive and dead. There, the chest. There, the shoulders. The blanket and the tunic were distinct in every way, but somehow still neighbors. My mind had split, then lumped. Some voxels had shimmered. In the vast meaning space inside my head, a tiny piece of the world was finding its proper place. ♦

Source: The Science of Mind Reading | The New Yorker

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3 Simple Habits That Can Protect Your Brain From Cognitive Decline

You might think that the impact of aging on the brain is something you can’t do much about. After all, isn’t it an inevitability? To an extent, as we may not be able to rewind the clock and change our levels of higher education or intelligence (both factors that delay the onset of symptoms of aging).

But adopting specific lifestyle behaviors–whether you’re in your thirties or late forties–can have a tangible effect on how well you age. Even in your fifties and beyond, activities like learning a new language or musical instrument, taking part in aerobic exercise, and developing meaningful social relationships can do wonders for your brain. There’s no question that when we compromise on looking after ourselves, our aging minds pick up the tab.

The Aging Process and Cognitive Decline

Over time, there is a build-up of toxins such as tau proteins and beta-amyloid plaques in the brain that correlate to the aging process and associated cognitive decline. Although this is a natural part of growing older, many factors can exacerbate it. Stress, neurotoxins such as alcohol and lack of (quality and quantity) sleep can speed up the process.

Neuroplasticity–the function that allows the brain to change and develop in our lifetime–has three mechanisms: synaptic connection, myelination, and neurogenesis. The key to resilient aging is improving neurogenesis, the birth of new neurons. Neurogenesis happens far more in babies and children than adults.

A 2018 study by researchers at Columbia University shows that in adults, this type of neuroplastic activity occurs in the hippocampus, the part of the brain that lays down memories. This makes sense as we respond to and store new experiences every day, and cement them during sleep. The more we can experience new things, activities, people, places, and emotions, the more likely we are to encourage neurogenesis.

With all this in mind, we can come up with a three-point plan to encourage “resilient aging” by activating neurogenesis in the brain:

1. Get your heart rate up

Aerobic exercise such as running or brisk walking has a potentially massive impact on neurogenesis. A 2016 rat study found that endurance exercise was most effective in increasing neurogenesis. It wins out over HIIT sessions and resistance training, although doing a variety of exercise also has its benefits.

Aim to do aerobic exercise for 150 minutes per week, and choose the gym, the park, or natural landscape over busy roads to avoid compromising brain-derived neurotrophic factor production (BDNF), a growth factor that encourages neurogenesis that aerobic exercise can boost. However, exercising in polluted areas decreases production.

If exercising alone isn’t your thing, consider taking up a team sport or one with a social element like table tennis. Exposure to social interaction can also increase the neurogenesis, and in many instances, doing so lets you practice your hand-eye coordination, which research has suggested leads to structural changes in the brain that may relate to a range of cognitive benefit. This combination of coordination and socializing has been shown to increase brain thickness in the parts of the cortex related to social/emotional welfare, which is crucial as we age.

2. Change your eating patterns

Evidence shows that calorie restriction, intermittent fasting, and time-restricted eating encourage neurogenesis in humans. In rodent studies, intermittent fasting has been found to improve cognitive function and brain structure, and reduce symptoms of metabolic disorders such as diabetes.

Reducing refined sugar will help reduce oxidative damage to brain cells, too, and we know that increased oxidative damage has been linked with a higher risk of developing Alzheimer’s disease. Twenty-four hour water-only fasts have also been proven to increase longevity and encourage neurogenesis.

Try any of the following, after checking with your doctor:

  • 24-hour water-only fast once a month
  •  Reducing your calorie intake by 50%-60% on two non-consecutive days of the week for two to three months or on an ongoing basis
  • Reducing calories by 20% every day for two weeks. You can do this three to four times a year
  • Eating only between 8 a.m. to 8 p.m., or 12 p.m. to 8 p.m. as a general rule

3. Prioritize sleep

Sleep helps promote the brain’s neural “cleaning” glymphatic system, which flushes out the build-up of age-related toxins in the brain (the tau proteins and beta amyloid plaques mentioned above). When people are sleep-deprived, we see evidence of memory deficits, and if you miss a whole night of sleep, research proves that it impacts IQ. Aim for seven to nine hours, and nap if it suits you. Our need to sleep decreases as we age.

Of course, there are individual exceptions, but having consistent sleep times and making sure you’re getting sufficient quality and length of sleep supports brain resilience over time. So how do you know if you’re getting enough? If you naturally wake up at the same time on weekends that you have to during the week, you probably are.

If you need to lie-in or take long naps, you’re probably not. Try practicing mindfulness or yoga nidra before bed at night, a guided breath-based meditation that has been shown in studies to improve sleep quality. There are plenty of recordings online if you want to experience it.

Pick any of the above that work for you and build it up until it becomes a habit, then move onto the next one and so on. You might find that by the end of the year, you’ll feel even healthier, more energized, and motivated than you do now, even as you turn another year older.

By: Fast Company / Tara Swart

Dr. Tara Swart is a neuroscientist, leadership coach, author, and medical doctor. Follow her on Twitter at @TaraSwart.

Source: Open-Your-Mind-Change

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Critics:

Cognitive deficit is an inclusive term to describe any characteristic that acts as a barrier to the cognition process.

The term may describe

Mild cognitive impairment (MCI) is a neurocognitive disorder which involves cognitive impairments beyond those expected based on an individual’s age and education but which are not significant enough to interfere with instrumental activities of daily living. MCI may occur as a transitional stage between normal aging and dementia, especially Alzheimer’s disease. It includes both memory and non-memory impairments.Mild cognitive impairment has been relisted as mild neurocognitive disorder in DSM-5, and in ICD-11.

The cause of the disorder remains unclear, as well as its prevention and treatment. MCI can present with a variety of symptoms, but is divided generally into two types.

Amnestic MCI (aMCI) is mild cognitive impairment with memory loss as the predominant symptom; aMCI is frequently seen as a prodromal stage of Alzheimer’s disease. Studies suggest that these individuals tend to progress to probable Alzheimer’s disease at a rate of approximately 10% to 15% per year.[needs update]It is possible that being diagnosed with cognitive decline may serve as an indicator of aMCI.

Nonamnestic MCI (naMCI) is mild cognitive impairment in which impairments in domains other than memory (for example, language, visuospatial, executive) are more prominent. It may be further divided as nonamnestic single- or multiple-domain MCI, and these individuals are believed to be more likely to convert to other dementias (for example, dementia with Lewy bodies).

See also

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