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Brain patterns underlying mothers’ responses to infant cries

Infant cries activate specific brain regions related to movement and speech, according to a National Institutes of Health study of mothers in 11 countries. The findings, led by researchers at NIH’s Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), identify behaviors and underlying brain activities that are consistent among mothers from different cultures. Understanding these reactions may help in identifying and treating caregivers at risk for child maltreatment and other problematic behaviors.

The study team conducted a series of behavioral and brain imaging studies using functional magnetic resonance imaging (fMRI). In a group of 684 new mothers in Argentina, Belgium, Brazil, Cameroon, France, Israel, Italy, Japan, Kenya, South Korea and the United States, researchers observed and recorded one hour of interaction between the mothers and their 5-month-old babies at home. The team analyzed whether mothers responded to their baby’s cries by showing affection, distracting, nurturing (like feeding or diapering), picking up and holding, or talking. Regardless of which country they came from, mothers were likely to pick up and hold or talk to their crying infant.

Through fMRI studies of other groups of women, the team found that infant cries activated similar brain regions in new and experienced mothers: the supplementary motor area, which is associated with the intention to move and speak; the inferior frontal regions, which are involved in the production of speech; and the superior temporal regions that are linked to sound processing.

Overall, the findings suggest that mothers’ responses to infant cries are hard-wired and generalizable across cultures. The study also builds upon earlier work showing that women’s and men’s brains respond differently to infant cries.

At age 23, Tina Fey was working at a YMCA. At age 23, Oprah was fired from her first reporting job.  At age 24, Stephen King was working as a janitor and living in a trailer. 

At age 27, Vincent Van Gogh failed as a missionary and decided to go to art school.   At age 28, J.K. Rowling was a suicidal single parent living on welfare.

At age 28, Wayne Coyne ( from The Flaming Lips) was a fry cook. At age 30, Harrison Ford was a carpenter.  At age 30, Martha Stewart was a stockbroker.  At age 37, Ang Lee was a stay-at-home-dad working odd jobs. Julia Child released her first cookbook at age 39, and got her own cooking show at age 51. Vera Wang failed to make the Olympic figure skating team, didn’t get the Editor-in-Chief position at Vogue, and designed her first dress at age 40. Stan Lee didn’t release his first big comic book until he was 40. Alan Rickman gave up his graphic design career to pursue acting at age 42. Samuel L. Jackson didn’t get his first movie role until he was 46.

Morgan Freeman landed his first movie role at age 52. Kathryn Bigelow only reached international success when she made The Hurt Locker at age 57. Grandma Moses didn’t begin her painting career until age 76. Louise Bourgeois didn’t become a famous artist until she was 78. Whatever your dream is, it is not too late to achieve it. You aren’t a failure because you haven’t found fame and fortune by the age of 21. Hell, it’s okay if you don’t even know what your dream is yet. Even if you’re flipping burgers, waiting tables or answering phones today, you never know where you’ll end up tomorrow. Never tell yourself you’re too old to make it. 

Never tell yourself you missed your chance. 

Never tell yourself that you aren’t good enough. 

You can do it. Whatever it is. 

Neurons That Distinguish Between Reality and Imagination Discovered

Neurons found to be abnormal in psychosis play an important role in our ability to distinguish between what is real and what is perceived, researchers say.

New Western University research shows that neurons in the part of the brain found to be abnormal in psychosis are also important in helping people distinguish between reality and imagination.

The researchers, Dr. Julio Martinez-Trujillo, principal investigator and professor at Western University’s Schulich School of Medicine & Dentistry and Dr. Diego Mendoza-Halliday, postdoctoral researcher at M.I.T., investigated how the brain codes visual information in reality versus abstract information in our working memory and how those differences are distributed across neurons in the lateral prefrontal cortex region of the brain. The results were published today in Nature Communications.

“Neuronal population coding of perceived and memorized visual features in the lateral prefrontal cortex” by Diego Mendoza-Halliday & Julio C. Martinez-Trujillo in Nature Communications. Published online June 1 2017 doi:10.1038/ncomms15471

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Stay Focused, If You Can

What makes some people better able to resist temptation than others? Lucina Uddin and Jason Nomi, cognitive neuroscientists at the University of Miami College of Arts and Sciences collaborated with Rosa Steimke, a visiting postdoctoral researcher in the Brain Connectivity and Cognition Laboratory at UM, to explore this question.

Steimke conducted a study as part of her dissertation work at Charité University in Berlin, Germany, in which participants were asked to perform a simple task: focus on one side of a screen where a letter – either an “E” or “F” – would quickly appear then disappear, and press a button indicating which letter they saw.

But before the letter appeared on the screen, an image would pop up to the right, and—this is where it gets interesting—the images were quite sensual and erotic. Not surprisingly, participants’ eyes definitely wandered to the right for a quick peek, which was captured by eye-tracking equipment.

“Using this setup, we were able to challenge participants’ self-control in the face of temptation,” said Steimke.

Adds Uddin, “This study is about individual differences in the ability to control impulses and behavior.”

According to previous research, the brain’s “cognitive control network” is typically involved in behavior that requires self-control. Here, the researchers explored another potential candidate brain system known as the “salience network.” The salience network is a collection of regions in the brain that selects which stimuli are deserving of our attention, such as a driver responding to a pedestrian running across the street or a large billboard along the highway.

The cognitive control network is related to ‘’top-down’’ effortful control of attention while the salience network is related to ‘’bottom-up’’ automatic direction of attention.

“We were interested in comparing the roles of these two networks in self-control behavior,” said Nomi.

Uddin and her team have taken a new approach to studying brain activity and its moment-to-moment variations using a method called “dynamic functional network connectivity.” Using this method, the team was able to examine whether the cognitive control or salience network was more closely linked to participants’ tendency to glance at the sensual pictures when they knew the goal was to focus on the letter.

Surprisingly, they found no links between cognitive control network dynamics and individual differences in performance of the task. However, those individuals whose brains showed a specific pattern of salience network dynamics were better able to perform the task. Specifically, for some people their salience networks were not as well-connected with the visual networks in the brain. Individuals who showed this pattern were better able to resist tempting distractors and perform the task.

“Researchers normally study connectivity using traditional approaches, but we used the dynamic approach, which gave us new insight that traditional connectivity analysis did not reveal,” said Uddin. “When we looked at the moment-to-moment, dynamic measures of connectivity we saw the relationship with individual differences in eye-gazing behavior emerge.”

The study, “Salience network dynamics underlying successful resistance of temptation,” is published in the journal SCAN.

Brain waves reflect different types of learning

Figuring out how to pedal a bike and memorizing the rules of chess require two different types of learning, and now for the first time, researchers have been able to distinguish each type of learning by the brain-wave patterns it produces.

These distinct neural signatures could guide scientists as they study the underlying neurobiology of how we both learn motor skills and work through complex cognitive tasks, says Earl K. Miller, the Picower Professor of Neuroscience at the Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences, and senior author of a paper describing the findings in the Oct. 11 edition of Neuron.

When neurons fire, they produce electrical signals that combine to form brain waves that oscillate at different frequencies. “Our ultimate goal is to help people with learning and memory deficits,” notes Miller. “We might find a way to stimulate the human brain or optimize training techniques to mitigate those deficits.”

The neural signatures could help identify changes in learning strategies that occur in diseases such as Alzheimer’s, with an eye to diagnosing these diseases earlier or enhancing certain types of learning to help patients cope with the disorder, says Roman F. Loonis, a graduate student in the Miller Lab and first author of the paper. Picower Institute research scientist Scott L. Brincat and former MIT postdoc Evan G. Antzoulatos, now at the University of California at Davis, are co-authors.

Explicit versus implicit learning

Scientists used to think all learning was the same, Miller explains, until they learned about patients such as the famous Henry Molaison or “H.M.,” who developed severe amnesia in 1953 after having part of his brain removed in an operation to control his epileptic seizures. Molaison couldn’t remember eating breakfast a few minutes after the meal, but he was able to learn and retain motor skills that he learned, such as tracing objects like a five-pointed star in a mirror.

“H.M. and other amnesiacs got better at these skills over time, even though they had no memory of doing these things before,” Miller says.

The divide revealed that the brain engages in two types of learning and memory — explicit and implicit.

Explicit learning “is learning that you have conscious awareness of, when you think about what you’re learning and you can articulate what you’ve learned, like memorizing a long passage in a book or learning the steps of a complex game like chess,” Miller explains.

“Implicit learning is the opposite. You might call it motor skill learning or muscle memory, the kind of learning that you don’t have conscious access to, like learning to ride a bike or to juggle,” he adds. “By doing it you get better and better at it, but you can’t really articulate what you’re learning.”

Many tasks, like learning to play a new piece of music, require both kinds of learning, he notes.

Brain waves from earlier studies

When the MIT researchers studied the behavior of animals learning different tasks, they found signs that different tasks might require either explicit or implicit learning. In tasks that required comparing and matching two things, for instance, the animals appeared to use both correct and incorrect answers to improve their next matches, indicating an explicit form of learning. But in a task where the animals learned to move their gaze one direction or another in response to different visual patterns, they only improved their performance in response to correct answers, suggesting implicit learning.

What’s more, the researchers found, these different types of behavior are accompanied by different patterns of brain waves.

During explicit learning tasks, there was an increase in alpha2-beta brain waves (oscillating at 10-30 hertz) following a correct choice, and an increase delta-theta waves (3-7 hertz) after an incorrect choice. The alpha2-beta waves increased with learning during explicit tasks, then decreased as learning progressed. The researchers also saw signs of a neural spike in activity that occurs in response to behavioral errors, called event-related negativity, only in the tasks that were thought to require explicit learning.

The increase in alpha-2-beta brain waves during explicit learning “could reflect the building of a model of the task,” Miller explains. “And then after the animal learns the task, the alpha-beta rhythms then drop off, because the model is already built.”

By contrast, delta-theta rhythms only increased with correct answers during an implicit learning task, and they decreased during learning. Miller says this pattern could reflect neural “rewiring” that encodes the motor skill during learning.

“This showed us that there are different mechanisms at play during explicit versus implicit learning,” he notes.

Future Boost to Learning

Loonis says the brain wave signatures might be especially useful in shaping how we teach or train a person as they learn a specific task. “If we can detect the kind of learning that’s going on, then we may be able to enhance or provide better feedback for that individual,” he says. “For instance, if they are using implicit learning more, that means they’re more likely relying on positive feedback, and we could modify their learning to take advantage of that.”

The neural signatures could also help detect disorders such as Alzheimer’s disease at an earlier stage, Loonis says. “In Alzheimer’s, a kind of explicit fact learning disappears with dementia, and there can be a reversion to a different kind of implicit learning,” he explains. “Because the one learning system is down, you have to rely on another one.”

Earlier studies have shown that certain parts of the brain such as the hippocampus are more closely related to explicit learning, while areas such as the basal ganglia are more involved in implicit learning. But Miller says that the brain wave study indicates “a lot of overlap in these two systems. They share a lot of the same neural networks.”

15+ Things We Wish People Understood About Anxiety

It is utterly exhausting living in a near constant state of fight or flight for so long.

Continue Reading 

Myths & Facts about Mental Illness 

source: Global Medical Education

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