Neuroscientists watch imagination happening in the brain

“You may say I’m a dreamer, but I’m not the only one,” sang John Lennon in his 1971 song Imagine.

And thanks to the dreams of a BYU student, we now know more about where and how imagination happens in our brains.

Stefania Ashby and her faculty mentor devised experiments using MRI technology that would help them distinguish pure imagination from related processes like remembering.

“I was thinking a lot about planning for my own future and imagining myself in the future, and I started wondering how memory and imagination work together,” Ashby said. “I wondered if they were separate or if imagination is just taking past memories and combining them in different ways to form something I’ve never experienced before.”

There’s a bit of scientific debate over whether memory and imagination truly are distinct processes. So Ashby and her faculty mentor devised MRI experiments to put it to the test.

They asked study participants to provide 60 personal photographs for the “remember” section of the experiment. Participants also filled out a questionnaire beforehand to determine which scenarios would be unfamiliar to them and thus a better fit for the “imagine” section.

The researchers then showed people their own photographs during an MRI session to elicit brain activity that is strictly memory-based. A statistical analysis revealed distinctive patterns for memory and imagination.

“We were able to see the distinctions even in those small regions of the hippocampus,” Ashby said. “It’s really neat that we can see the difference between those two tasks in that small of a brain region.”

Ashby co-authored the study with BYU psychology and neuroscience professor Brock Kirwan for the journal Cognitive Neuroscience. Kirwan studies memory at Brigham Young University, and Ashby is one of many students that he has mentored.

“Stefania came in really excited about this project, she pitched it to me, and basically sold it to me right there,” Kirwan said. “It was really cool because it gave me a chance to become more immersed and really broaden my horizons.”

Stefania graduated in 2011 and is currently working as a research associate at UC Davis, where she uses neuroimaging to study individuals at risk of psychotic disorders such as schizophrenia. Her plan is to earn a Ph.D. in neuroscience and continue researching.

A long childhood feeds the hungry human brain

A five-year old’s brain is an energy monster. It uses twice as much glucose (the energy that fuels the brain) as that of a full-grown adult, a new study led by Northwestern University anthropologists has found.

The study helps to solve the long-standing mystery of why human children grow so slowly compared with our closest animal relatives.

It shows that energy funneled to the brain dominates the human body’s metabolism early in life and is likely the reason why humans grow at a pace more typical of a reptile than a mammal during childhood.

Results of the study will be published the week of Aug. 25 in the journal Proceedings of the National Academy of Sciences.

"Our findings suggest that our bodies can’t afford to grow faster during the toddler and childhood years because a huge quantity of resources is required to fuel the developing human brain," said Christopher Kuzawa, first author of the study and a professor of anthropology at Northwestern’s Weinberg College of Arts and Sciences. "As humans we have so much to learn, and that learning requires a complex and energy-hungry brain."

Kuzawa also is a faculty fellow at the Institute for Policy Research at Northwestern.

The study is the first to pool existing PET and MRI brain scan data — which measure glucose uptake and brain volume, respectively — to show that the ages when the brain gobbles the most resources are also the ages when body growth is slowest. At 4 years of age, when this “brain drain” is at its peak and body growth slows to its minimum, the brain burns through resources at a rate equivalent to 66 percent of what the entire body uses at rest.

The findings support a long-standing hypothesis in anthropology that children grow so slowly, and are dependent for so long, because the human body needs to shunt a huge fraction of its resources to the brain during childhood, leaving little to be devoted to body growth. It also helps explain some common observations that many parents may have.

"After a certain age it becomes difficult to guess a toddler or young child’s age by their size," Kuzawa said. "Instead you have to listen to their speech and watch their behavior. Our study suggests that this is no accident. Body growth grinds nearly to a halt at the ages when brain development is happening at a lightning pace, because the brain is sapping up the available resources."

It was previously believed that the brain’s resource burden on the body was largest at birth, when the size of the brain relative to the body is greatest. The researchers found instead that the brain maxes out its glucose use at age 5. At age 4 the brain consumes glucose at a rate comparable to 66 percent of the body’s resting metabolic rate (or more than 40 percent of the body’s total energy expenditure).

"The mid-childhood peak in brain costs has to do with the fact that synapses, connections in the brain, max out at this age, when we learn so many of the things we need to know to be successful humans," Kuzawa said.

"At its peak in childhood, the brain burns through two-thirds of the calories the entire body uses at rest, much more than other primate species," said William Leonard, co-author of the study. "To compensate for these heavy energy demands of our big brains, children grow more slowly and are less physically active during this age range. Our findings strongly suggest that humans evolved to grow slowly during this time in order to free up fuel for our expensive, busy childhood brains."

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Influenced by Self-Interest, Humans Less Concerned About Inequity To Others

Strongly influenced by their self-interest, humans do not protest being overcompensated, even when there are no consequences, researchers in Georgia State University’s Brains and Behavior Program have found.

This could imply that humans are less concerned than previously believed about the inequity of others, researchers said. Their findings are published in the journal Brain Connectivity. These findings suggest humans’ sense of unfairness is affected by their self-interest, indicating the interest humans show in others’ outcomes is a recently evolved propensity.

It has long been known that humans show sensitivity when they are at a disadvantage by refusing or protesting outcomes more often when they are offered less money than a social partner. But the research team of physics graduate students Bidhan Lamichhane and Bhim Adhikari and Brains and Behavior faculty Dr. Sarah Brosnan, associate professor of psychology, and Dr. Mukesh Dhamala, associate professor of physics and astronomy, reports that, contrary to expectations, humans do not show any sensitivity when they are overcompensated. They conclude that humans are more interested in their own outcomes than those of others.

“A true sense of fairness means that I get upset if I get paid more than you because I don’t think that’s fair,” Brosnan said. “We thought that people would protest quite a bit in the fixed decision game because it’s a cost-free way to say, ‘This isn’t fair.’ But that’s not what we saw at all. People protested higher offers at roughly the same rate that they refused offers where they got more, indicating that this lack of refusal in advantaged situations may not be because of the cost of refusing. It may just be because people don’t care as much as we thought they did if they’re getting more than someone else.”

The researchers also used functional magnetic resonance imaging (fMRI) to study the underlying brain mechanisms from 18 participants, who played three two-person economic exchange games that involved inequity in their favor and not in their favor. Overcompensated offers triggered a different brain circuit than undercompensated offers and indicate that people may be responding to overcompensation as if it were a reward. This could explain the lack of refusals in this unfair situation, researchers said.

Each game involved three offers for how $100 would be split: fair (amount between $40 to $60), unfair-low (disadvantageous to the subject, amount between $0 to $20) and unfair-overcompensated (advantageous to the subject, amount between $80 to $100). Participants played 30 rounds of each game and earned about two percent of the total amount from the games.

In the first two games, the subject received an offer for how much money they would receive and were then asked whether they wanted to reject or accept it. In the Ultimatum Game, if the responder rejected the offer, neither player received any money, leading to a fair outcome. In the Impunity Game, if the subject rejected the offer, only he or she lost the payoff, meaning the outcome was even more unfair than the offer. The subject got nothing, but the partner still got their proposed amount. In the Fixed Decision Game, the subject could choose to protest or not protest the offers, but this didn’t change the outcome for either player. This allowed subjects to protest offers without an associated cost.

The blood-oxygen level dependent signals of the brain were recorded by an MRI scanner as participants played the games. The results of brain response provided new insights into the functional role of the dorsolateral prefrontal cortex and related networks of brain regions for advantageous inequity and protest.

A network of brain regions consisting of the left caudate, right cingulate and right thalamus had a higher level of activity for overcompensated offers than for fair offers. For protest, a different network, consisting of the right dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex and left substantia nigra, came into play. The researchers also mapped out how the brain activity flow occurred within these networks during decision-making.

ADHD children make poor decisions due to less differentiated learning processes

Which shirt do we put on in the morning? Do we drive to work or take the train? From which takeaway joint do we want to buy lunch? We make hundreds of different decisions every day. Even if these often only have a minimal impact, it is extremely important for our long-term personal development to make decisions that are as optimal as possible. People with ADHD often find this difficult, however. They are known to make impulsive decisions, often choosing options which bring a prompt but smaller reward instead of making a choice that yields a greater reward later on down the line. Researchers from the University Clinics for Child and Adolescent Psychiatry, University of Zurich, now reveal that different decision-making processes are responsible for such suboptimal choices and that these take place in the middle of the frontal lobe.

Mathematical models help to understand the decision-making processes

In the study, the decision-making processes in 40 young people with and without ADHD were examined. Lying in a functional magnetic resonance imaging scanner to record the brain activity, the participants played a game where they had to learn which of two images carried more frequent rewards. In order to understand the impaired mechanisms of participants with ADHD better, learning algorithms which originally stemmed from the field of artificial intelligence were used to evaluate the data. These mathematical models help to understand the precise learning and decision-making mechanisms better. “We were able to demonstrate that young people with ADHD do not inherently have difficulties in learning new information; instead, they evidently use less differentiated learning patterns, which is presumably why sub-optimal decisions are often made”, says first author Tobias Hauser.

Multimodal imaging affords glimpses inside the brain

In order to study the brain processes that triggered these impairments, the authors used multimodal imaging methods, where the participants were examined using a combined measurement of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to record the electrical activity and the blood flow in the brain. It became apparent that participants with ADHD exhibit an altered functioning in the medial prefrontal cortex – a region in the middle of the frontal lobe. This part of the brain is heavily involved in decision-making processes, especially if you have to choose between several options, and in learning from errors. Although a change in activity in this region was already discovered in other contexts for ADHD, the Zurich researchers were now also able to pinpoint the precise moment of this impairment, which already occurred less than half a second after a feedback, i.e. at a very early stage.

Psychologist Tobias Hauser, who is now researching at the Wellcome Trust Centre for Neuroimaging, University College London, is convinced that the results fundamentally improve our understanding of the mechanisms of impaired decision-making behavior in people with ADHD. The next step will be to study the brain messenger substances. “If our findings are confirmed, they will provide key clues as to how we might be able to design therapeutic interventions in future,” explains Hauser.

Literature:

Tobias U. Hauser, Reto Iannaccone, Juliane Ball, Christoph Mathys, Daniel Brandeis, Susanne Walitza & Silvia Brem: Role of Medial Prefrontal Cortex in Impaired Decision Making in Juvenile Attention-Deficit/Hyperactivity Disorder, in: JAMA Psychiatry

Our connection to content

Using neuroscience tools, Innerscope Research explores the connections between consumers and media.

It’s often said that humans are wired to connect: The neural wiring that helps us read the emotions and actions of other people may be a foundation for human empathy.

But for the past eight years, MIT Media Lab spinout Innerscope Research has been using neuroscience technologies that gauge subconscious emotions by monitoring brain and body activity to show just how powerfully we also connect to media and marketing communications.

“We are wired to connect, but that connection system is not very discriminating. So while we connect with each other in powerful ways, we also connect with characters on screens and in books, and, we found, we also connect with brands, products, and services,” says Innerscope’s chief science officer, Carl Marci, a social neuroscientist and former Media Lab researcher.

With this core philosophy, Innerscope — co-founded at MIT by Marci and Brian Levine MBA ’05 — aims to offer market research that’s more advanced than traditional methods, such as surveys and focus groups, to help content-makers shape authentic relationships with their target consumers.

“There’s so much out there, it’s hard to make something people will notice or connect to,” Levine says. “In a way, we aim to be the good matchmaker between content and people.”

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New research sheds light on how children’s brains memorize facts

The scientists also saw changes in the degree to which the hippocampus was connected to other parts of children’s brains, with several parts of the prefrontal, anterior temporal cortex and parietal cortex more strongly connected to the hippocampus after one year. Crucially, the stronger these connections, the greater was each individual child’s ability to retrieve math facts from memory, a finding that suggests a starting point for future studies of math-learning disabilities.

Although children were using their hippocampus more after a year, adolescents and adults made minimal use of their hippocampus while solving math problems. Instead, they pulled math facts from well-developed information stores in the neocortex.

Memory scaffold

“What this means is that the hippocampus is providing a scaffold for learning and consolidating facts into long-term memory in children,” said Menon, who is also the Rachel L. and Walter F. Nichols, MD, Professor at the medical school. Children’s brains are building a schema for mathematical knowledge. The hippocampus helps support other parts of the brain as adultlike neural connections for solving math problems are being constructed. “In adults this scaffold is not needed because memory for math facts has most likely been consolidated into the neocortex,” he said. Interestingly, the research also showed that, although the adult hippocampus is not as strongly engaged as in children, it seems to keep a backup copy of the math information that adults usually draw from the neocortex.

The researchers compared the level of variation in patterns of brain activity as children, adolescents and adults correctly solved math problems. The brain’s activity patterns were more stable in adolescents and adults than in children, suggesting that as the brain gets better at solving math problems its activity becomes more consistent.

The next step, Menon said, is to compare the new findings about normal math learning to what happens in children with math-learning disabilities.

“In children with math-learning disabilities, we know that the ability to retrieve facts fluently is a basic problem, and remains a bottleneck for them in high school and college,” he said. “Is it that the hippocampus can’t provide a reliable scaffold to build good representations of math facts in other parts of the brain during the early stages of learning, and so the child continues to use inefficient strategies to solve math problems? We want to test this.”

(Image caption: This is the happiness equation, where t is the trial number, w0 is a constant term, other weights w capture the influence of different event types, 0 ≤ γ ≤ 1 is a forgetting factor that makes events in more recent trials more influential than those in earlier trials, CRj is the CR if chosen instead of a gamble on trial j, EVj is the EV of a gamble (average reward for the gamble) if chosen on trial j, and RPEj is the RPE on trial j contingent on choice of the gamble. The RPE is equal to the reward received minus the expectation in that trial EVj. If the CR was chosen, then EVj = 0 and RPEj = 0; if the gamble was chosen, then CRj = 0. The variables in the equation are quantities that the neuromodulator dopamine has been associated with in previous neuroscience studies. Credit: Robb Rutledge, UCL)

Equation to predict happiness

The happiness of over 18,000 people worldwide has been predicted by a mathematical equation developed by researchers at UCL, with results showing that moment-to-moment happiness reflects not just how well things are going, but whether things are going better than expected.

The new equation accurately predicts exactly how happy people will say they are from moment to moment based on recent events, such as the rewards they receive and the expectations they have during a decision-making task. Scientists found that overall wealth accumulated during the experiment was not a good predictor of happiness. Instead, moment-to-moment happiness depended on the recent history of rewards and expectations. These expectations depended, for example, on whether the available options could lead to good or bad outcomes.

The study, published in the Proceedings of the National Academy of Sciences, investigated the relationship between happiness and reward, and the neural processes that lead to feelings that are central to our conscious experience, such as happiness. Before now, it was known that life events affect an individual’s happiness but not exactly how happy people will be from moment to moment as they make decisions and receive outcomes resulting from those decisions, something the new equation can predict.

Scientists believe that quantifying subjective states mathematically could help doctors better understand mood disorders, by seeing how self-reported feelings fluctuate in response to events like small wins and losses in a smartphone game. A better understanding of how mood is determined by life events and circumstances, and how that differs in people suffering from mood disorders, will hopefully lead to more effective treatments.

Research examining how and why happiness changes from moment to moment in individuals could also assist governments who deploy population measures of wellbeing to inform policy, by providing quantitative insight into what the collected information means. This is especially relevant to the UK following the launch of the National Wellbeing Programme in 2010 and subsequent annual reports by the Office for National Statistics on ‘Measuring National Wellbeing’.

For the study, 26 subjects completed a decision-making task in which their choices led to monetary gains and losses, and they were repeatedly asked to answer the question ‘how happy are you right now?’. The participant’s neural activity was also measured during the task using functional MRI and from these data, scientists built a computational model in which self-reported happiness was related to recent rewards and expectations. The model was then tested on 18,420 participants in the game ‘What makes me happy?’ in a smartphone app developed at UCL called 'The Great Brain Experiment'. Scientists were surprised to find that the same equation could be used to predict how happy subjects would be while they played the smartphone game, even though subjects could win only points and not money.

Lead author of the study, Dr Robb Rutledge (UCL Wellcome Trust Centre for Neuroimaging and the new Max Planck UCL Centre for Computational Psychiatry and Ageing), said: “We expected to see that recent rewards would affect moment-to-moment happiness but were surprised to find just how important expectations are in determining happiness. In real-world situations, the rewards associated with life decisions such as starting a new job or getting married are often not realised for a long time, and our results suggest expectations related to these decisions, good and bad, have a big effect on happiness.

"Life is full of expectations - it would be difficult to make good decisions without knowing, for example, which restaurant you like better. It is often said that you will be happier if your expectations are lower. We find that there is some truth to this: lower expectations make it more likely that an outcome will exceed those expectations and have a positive impact on happiness. However, expectations also affect happiness even before we learn the outcome of a decision. If you have plans to meet a friend at your favourite restaurant, those positive expectations may increase your happiness as soon as you make the plan. The new equation captures these different effects of expectations and allows happiness to be predicted based on the combined effects of many past events.

"It’s great that the data from the large and varied population using The Great Brain Experiment smartphone app shows that the same happiness equation applies to thousands people worldwide playing our game, as with our much smaller laboratory-based experiments which demonstrate the tremendous value of this approach for studying human well-being on a large scale."

The team used functional MRI to demonstrate that neural signals during decisions and outcomes in the task in an area of the brain called the striatum can be used to predict changes in moment-to-moment happiness. The striatum has a lot of connections with dopamine neurons, and signals in this brain area are thought to depend at least partially on dopamine. These results raise the possibility that dopamine may play a role in determining happiness.