On How Memories Pass Between Generations

The BBC highlights a recent study titled “Parental olfactory experience influences behavior and neural structure in subsequent generations” in which the researchers at Emory University in Atlanta, GA trained mice to avoid a smell, and subsequently, these mice passed this aversion on to their “grandchildren.” The results are important for phobia and anxiety research.

Both the mice’s offspring, and their offspring, were “extremely sensitive” to cherry blossom and would avoid the scent, despite never having experiencing it in their lives. Remarkably, these changes extended to changes in brain structure.

From the abstract:

Using olfactory molecular specificity, we examined the inheritance of parental traumatic exposure, a phenomenon that has been frequently observed, but not understood. We subjected F0 mice to odor fear conditioning before conception and found that subsequently conceived F1 and F2 generations had an increased behavioral sensitivity to the F0-conditioned odor, but not to other odors. When an odor (acetophenone) that activates a known odorant receptor (Olfr151) was used to condition F0 mice, the behavioral sensitivity of the F1 and F2 generations to acetophenone was complemented by an enhanced neuroanatomical representation of the Olfr151 pathway. Bisulfite sequencing of sperm DNA from conditioned F0 males and F1 naive offspring revealed CpG hypomethylation in the Olfr151 gene. In addition, in vitro fertilization, F2 inheritance and cross-fostering revealed that these transgenerational effects are inherited via parental gametes. Our findings provide a framework for addressing how environmental information may be inherited transgenerationally at behavioral, neuroanatomical and epigenetic levels.

Fascinating.

How Dogs are Like Humans

A thought-provoking and interesting piece in The New York Times by Gregory Berns, a professor of neuroeconomics at Emory University, on how dogs are like humans in their thought processes. By teaching dogs to sit still in MRI machines, they were able to trace neurobiological evidence of emotions in dogs which are akin to the ones we experience:

By looking directly at their brains and bypassing the constraints of behaviorism, M.R.I.’s can tell us about dogs’ internal states. M.R.I.’s are conducted in loud, confined spaces. People don’t like them, and you have to hold absolutely still during the procedure. Conventional veterinary practice says you have to anesthetize animals so they don’t move during a scan. But you can’t study brain function in an anesthetized animal. At least not anything interesting like perception or emotion.

From the beginning, we treated the dogs as persons. We had a consent form, which was modeled after a child’s consent form but signed by the dog’s owner. We emphasized that participation was voluntary, and that the dog had the right to quit the study. We used only positive training methods. No sedation. No restraints. If the dogs didn’t want to be in the M.R.I. scanner, they could leave. Same as any human volunteer.

My dog Callie was the first. Rescued from a shelter, Callie was a skinny black terrier mix, what is called a feist in the southern Appalachians, from where she came. True to her roots, she preferred hunting squirrels and rabbits in the backyard to curling up in my lap. She had a natural inquisitiveness, which probably landed her in the shelter in the first place, but also made training a breeze.

With the help of my friend Mark Spivak, a dog trainer, we started teaching Callie to go into an M.R.I. simulator that I built in my living room. She learned to walk up steps into a tube, place her head in a custom-fitted chin rest, and hold rock-still for periods of up to 30 seconds. Oh, and she had to learn to wear earmuffs to protect her sensitive hearing from the 95 decibels of noise the scanner makes.

After months of training and some trial-and-error at the real M.R.I. scanner, we were rewarded with the first maps of brain activity. For our first tests, we measured Callie’s brain response to two hand signals in the scanner. In later experiments, not yet published, we determined which parts of her brain distinguished the scents of familiar and unfamiliar dogs and humans.

This is truly fascinating.

I have placed Gregory Berns’s upcoming book, How Dogs Love Us: A Neuroscientist and His Adopted Dog Decode the Canine Brain, into my Amazon queue.

Debunked: “Right-Brain” vs. “Left-Brain” Personalities

For years in popular culture, the terms “left-brained” and “right-brained” have come to signify disparate personality types, with an assumption that some people use the right side of their brain more, (those who are supposedly more creative/artistic) while some use the left side more (those who are more logical/analytical). But newly released research findings from University of Utah neuroscientists assert that there is no evidence within brain imaging that indicates some people are right-brained or left-brained:

Following a two-year study, University of Utah researchers have debunked that myth through identifying specific networks in the left and right brain that process lateralized functions. Lateralization of brain function means that there are certain mental processes that are mainly specialized to one of the brain’s left or right hemispheres. During the course of the study, researchers analyzed resting brain scans of 1,011 people between the ages of seven and 29. In each person, they studied functional lateralization of the brain measured for thousands of brain regions — finding no relationship that individuals preferentially use their left -brain network or right- brain network more often.

Following a two-year study, University of Utah researchers have debunked that myth through identifying specific networks in the left and right brain that process lateralized functions. Lateralization of brain function means that there are certain mental processes that are mainly specialized to one of the brain’s left or right hemispheres. During the course of the study, researchers analyzed resting brain scans of 1,011 people between the ages of seven and 29. In each person, they studied functional lateralization of the brain measured for thousands of brain regions — finding no relationship that individuals preferentially use their left -brain network or right- brain network more often.

“It’s absolutely true that some brain functions occur in one or the other side of the brain. Language tends to be on the left, attention more on the right. But people don’t tend to have a stronger left- or right-sided brain network. It seems to be determined more connection by connection, ” said Jeff Anderson, M.D., Ph.D., lead author of the study, which is formally titled “An Evaluation of the Left-Brain vs. Right-Brain Hypothesis with Resting State Functional Connectivity Magnetic Resonance Imaging.” It is published in the journal PLOS ONE this month.

From the paper’s abstract:

Lateralized brain regions subserve functions such as language and visuospatial processing. It has been conjectured that individuals may be left-brain dominant or right-brain dominant based on personality and cognitive style, but neuroimaging data has not provided clear evidence whether such phenotypic differences in the strength of left-dominant or right-dominant networks exist. We evaluated whether strongly lateralized connections covaried within the same individuals. Data were analyzed from publicly available resting state scans for 1011 individuals between the ages of 7 and 29. For each subject, functional lateralization was measured for each pair of 7266 regions covering the gray matter at 5-mm resolution as a difference in correlation before and after inverting images across the midsagittal plane. The difference in gray matter density between homotopic coordinates was used as a regressor to reduce the effect of structural asymmetries on functional lateralization. Nine left- and 11 right-lateralized hubs were identified as peaks in the degree map from the graph of significantly lateralized connections. The left-lateralized hubs included regions from the default mode network (medial prefrontal cortex, posterior cingulate cortex, and temporoparietal junction) and language regions (e.g., Broca Area and Wernicke Area), whereas the right-lateralized hubs included regions from the attention control network (e.g., lateral intraparietal sulcus, anterior insula, area MT, and frontal eye fields). Left- and right-lateralized hubs formed two separable networks of mutually lateralized regions. Connections involving only left- or only right-lateralized hubs showed positive correlation across subjects, but only for connections sharing a node. Lateralization of brain connections appears to be a local rather than global property of brain networks, and our data are not consistent with a whole-brain phenotype of greater “left-brained” or greater “right-brained” network strength across individuals. Small increases in lateralization with age were seen, but no differences in gender were observed.

So while there are more creative/artistic people in the world, this study purports that the active parts of the brain do not account for said personality traits. You learn something new every day, right?

Can an Alligator Run the Hundred Meter Hurdles?

Gary Marcus, writing in The New Yorker, offers a summary of why artificial intelligence isn’t so intelligent (and has a long way to go to catch up with the human brain). He focuses on the research of Hector Levesque, who is a critic of the modern A.I.:

In a terrific paper just presented at the premier international conference on artificial intelligence, Levesque, a University of Toronto computer scientist who studies these questions, has taken just about everyone in the field of A.I. to task. He argues that his colleagues have forgotten about the “intelligence” part of artificial intelligence.

Levesque starts with a critique of Alan Turing’s famous “Turing test,” in which a human, through a question-and-answer session, tries to distinguish machines from people. You’d think that if a machine could pass the test, we could safely conclude that the machine was intelligent. But Levesque argues that the Turing test is almost meaningless, because it is far too easy to game. Every year, a number of machines compete in the challenge for real, seeking something called the Loebner Prize. But the winners aren’t genuinely intelligent; instead, they tend to be more like parlor tricks, and they’re almost inherently deceitful. If a person asks a machine “How tall are you?” and the machine wants to win the Turing test, it has no choice but to confabulate. It has turned out, in fact, that the winners tend to use bluster and misdirection far more than anything approximating true intelligence. One program worked by pretending to be paranoid; others have done well by tossing off one-liners that distract interlocutors. The fakery involved in most efforts at beating the Turing test is emblematic: the real mission of A.I. ought to be building intelligence, not building software that is specifically tuned toward fixing some sort of arbitrary test.

The crux, it seems to me, is how machines interpret the subtleties of human communication and how we talk. Marcus offers the following example in which a substitute of one word yields disparate answers:

The large ball crashed right through the table because it was made of Styrofoam. What was made of Styrofoam? (The alternative formulation replaces Stryrofoam with steel.)

a) The large ball
b) The table

Continuing, he explains:

These examples, which hinge on the linguistic phenomenon known as anaphora, are hard both because they require common sense—which still eludes machines—and because they get at things people don’t bother to mention on Web pages, and that don’t end up in giant data sets.

More broadly, they are instances of what I like to call the Long-Tail Problem: common questions can often be answered simply by trawling the Web, but rare questions can still stymie all the resources of a whole Web full of Big Data. Most A.I. programs are in trouble if what they’re looking for is not spelled out explicitly on a Web page. This is part of the reason for Watson’s most famous gaffe—mistaking Toronto for a city in the United States.

Levesque’s paper is short and easily accessible for the layman.

On Animal Intelligence

New research shows that we have grossly underestimated both the scope and the scale of animal intelligence. Primatologist Frans de Waal explains in the Saturday essay for The Wall Street Journal. This example on elephant intelligence is striking:

Experiments with animals have long been handicapped by our anthropocentric attitude: We often test them in ways that work fine with humans but not so well with other species. Scientists are now finally meeting animals on their own terms instead of treating them like furry (or feathery) humans, and this shift is fundamentally reshaping our understanding.

Elephants are a perfect example. For years, scientists believed them incapable of using tools. At most, an elephant might pick up a stick to scratch its itchy behind. In earlier studies, the pachyderms were offered a long stick while food was placed outside their reach to see if they would use the stick to retrieve it. This setup worked well with primates, but elephants left the stick alone. From this, researchers concluded that the elephants didn’t understand the problem. It occurred to no one that perhaps we, the investigators, didn’t understand the elephants.

Think about the test from the animal’s perspective. Unlike the primate hand, the elephant’s grasping organ is also its nose. Elephants use their trunks not only to reach food but also to sniff and touch it. With their unparalleled sense of smell, the animals know exactly what they are going for. Vision is secondary.

But as soon as an elephant picks up a stick, its nasal passages are blocked. Even when the stick is close to the food, it impedes feeling and smelling. It is like sending a blindfolded child on an Easter egg hunt.

On a recent visit to the National Zoo in Washington, I met with Preston Foerder and Diana Reiss of Hunter College, who showed me what Kandula, a young elephant bull, can do if the problem is presented differently. The scientists hung fruit high up above the enclosure, just out of Kandula’s reach. The elephant was given several sticks and a sturdy square box.

Kandula ignored the sticks but, after a while, began kicking the box with his foot. He kicked it many times in a straight line until it was right underneath the branch. He then stood on the box with his front legs, which enabled him to reach the food with his trunk. An elephant, it turns out, can use tools—if they are the right ones.

Worth reading in entirety.

On Plasticity and Social Connections

Barbara Fredrickson’s op-ed titled “Your Phone vs. Your Heart” in The New York Times this weekend hits a nerve (so to speak):

In short, the more attuned to others you become, the healthier you become, and vice versa. This mutual influence also explains how a lack of positive social contact diminishes people. Your heart’s capacity for friendship also obeys the biological law of “use it or lose it.” If you don’t regularly exercise your ability to connect face to face, you’ll eventually find yourself lacking some of the basic biological capacity to do so.

The human body — and thereby our human potential — is far more plastic or amenable to change than most of us realize. The new field of social genomics, made possible by the sequencing of the human genome, tells us that the ways our and our children’s genes are expressed at the cellular level is plastic, too, responsive to habitual experiences and actions.

The gist is that by alienating away from human connection, your brain chemistry/structure changes (the concept is called plasticity). But it can be changed (for the better, in terms of how you feel) if you spend meaningful time with others. So step away from Twitter, slow down on the text messaging, and make plans to go out for dinner with a friend.

On Albert Einstein’s Unusual, but Average-Sized, Brain

After Albert Einstein died in 1955, a pathologist named Thomas Harvey removed Einstein’s brain, photographed it with great care, cut it up into 240 blocks, sliced some of those blocks into slides, and prepared a roadmap to help future scientists navigate the pieces. Slides and photographs were distributed to researchers, but many have since been lost.

Dean Falk, a senior scholar at Santa Fe’s School for Advanced Research, has spent years studying the photographs of Einstein’s brain and is the lead author of a new study, published in the journal Brain, that relies on a collection of rarely seen photographs to analyze it.

Falk’s team compared Einstein’s brain with those of 85 other humans already described in the scientific literature and found that the great physicist did indeed have something special between his ears. Although the brain, weighing 1230 grams, is only average in size, several regions feature additional convolutions and folds rarely seen in other subjects. For example, the regions on the left side of the brain that facilitate sensory inputs into, and motor control of, the face and tongue are much larger than normal; and his prefrontal cortex—linked to planning, focused attention, and perseverance in the face of challenges—is also greatly expanded.

The key takeaway: Einstein’s brain was normal sized, but had a lot more convolutions than that of the average human brain (on record).

Photographs of Einstein’s brain.

The link to the full paper is here.

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(via Washington Post)