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Imagination and reality flow in opposite directions in the brain

Imagination and reality flow in opposite directions in the brain | Science, Technology, and Current Futurism | Scoop.it

As real as that daydream may seem, its path through your brain runs opposite reality. Aiming to discern discrete neural circuits, researchers at the University of Wisconsin-Madison have tracked electrical activity in the brains of people who alternately imagined scenes or watched videos.

 

"A really important problem in brain research is understanding how different parts of the brain are functionally connected. What areas are interacting? What is the direction of communication?" says Barry Van Veen, a UW-Madison professor of electrical and computer engineering. "We know that the brain does not function as a set of independent areas, but as a network of specialized areas that collaborate."

 

Van Veen, along with Giulio Tononi, a UW-Madison psychiatry professor and neuroscientist, Daniela Dentico, a scientist at UW-Madison's Waisman Center, and collaborators from the University of Liege in Belgium, published results recently in the journal NeuroImage. Their work could lead to the development of new tools to help Tononi untangle what happens in the brain during sleep and dreaming, while Van Veen hopes to apply the study's new methods to understand how the brain uses networks to encode short-term memory.

 

During imagination, the researchers found an increase in the flow of information from the parietal lobe of the brain to the occipital lobe -- from a higher-order region that combines inputs from several of the senses out to a lower-order region. In contrast, visual information taken in by the eyes tends to flow from the occipital lobe -- which makes up much of the brain's visual cortex -- "up" to the parietal lobe.

 

"There seems to be a lot in our brains and animal brains that is directional, that neural signals move in a particular direction, then stop, and start somewhere else," says. "I think this is really a new theme that had not been explored."

 

The researchers approached the study as an opportunity to test the power of electroencephalography (EEG) -- which uses sensors on the scalp to measure underlying electrical activity -- to discriminate between different parts of the brain's network.

Brains are rarely quiet, though, and EEG tends to record plenty of activity not necessarily related to a particular process researchers want to study.

 

To zero in on a set of target circuits, the researchers asked their subjects to watch short video clips before trying to replay the action from memory in their heads. Others were asked to imagine traveling on a magic bicycle -- focusing on the details of shapes, colors and textures -- before watching a short video of silent nature scenes.

Using an algorithm Van Veen developed to parse the detailed EEG data, the researchers were able to compile strong evidence of the directional flow of information.

 

"We were very interested in seeing if our signal-processing methods were sensitive enough to discriminate between these conditions," says Van Veen, whose work is supported by the National Institute of Biomedical Imaging and Bioengineering. "These types of demonstrations are important for gaining confidence in new tools."


Via Dr. Stefan Gruenwald
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Vloasis's curator insight, November 22, 2014 11:10 AM

So imagination input flows from the parietal to the occipital lobe, while visual input flows vice versa.

Diane Johnson's curator insight, November 23, 2014 8:46 AM

Interesting findings from electrical and computer engineering studies. Useful connections to the information processing DCI's.

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New Study Extends “Mind Reading” Research to Feelings by Applying Machine Learning Techniques to fMRI Data

New Study Extends “Mind Reading” Research to Feelings by Applying Machine Learning Techniques to fMRI Data | Science, Technology, and Current Futurism | Scoop.it

For the first time, scientists at Carnegie Mellon University have identified which emotion a person is experiencing based on brain activity.

 

The study, published in the June 19 issue of PLOS ONE, combines functional magnetic resonance imaging (fMRI) and machine learning to measure brain signals to accurately read emotions in individuals. Led by researchers in CMU’s Dietrich College of Humanities and Social Sciences, the findings illustrate how the brain categorizes feelings, giving researchers the first reliable process to analyze emotions. Until now, research on emotions has been long stymied by the lack of reliable methods to evaluate them, mostly because people are often reluctant to honestly report their feelings. Further complicating matters is that many emotional responses may not be consciously experienced.

 

Identifying emotions based on neural activity builds on previous discoveries by CMU’s Marcel Just and Tom M. Mitchell, which used similar techniques to create a computational model that identifies individuals’ thoughts of concrete objects,often dubbed “mind reading.”

 

“This research introduces a new method with potential to identify emotions without relying on people’s ability to self-report,” said Karim Kassam, assistant professor ofsocial and decision sciences and lead author of the study. “It could be used to assess an individual’s emotional response to almost any kind of stimulus, for example, a flag, a brand name or a political candidate.”

 

One challenge for the research team was find a way to repeatedly and reliably evoke different emotional states from the participants. Traditional approaches, such as showing subjects emotion-inducing film clips, would likely have been unsuccessful because the impact of film clips diminishes with repeated display. The researchers solved the problem by recruiting actors from CMU’s School of Drama.

 

“Our big breakthrough was my colleague Karim Kassam’s idea of testing actors, who are experienced at cycling through emotional states. We were fortunate, in that respect, that CMU has a superb drama school,” saidGeorge Loewenstein, the Herbert A. Simon University Professor of Economics and Psychology.

 

For the study, 10 actors were scanned at CMU’s Scientific Imaging & Brain Research Center while viewing the words of nine emotions: anger, disgust, envy, fear, happiness, lust, pride, sadness and shame. While inside the fMRI scanner, the actors were instructed to enter each of these emotional states multiple times, in random order.


Via Dr. Stefan Gruenwald
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Machine Learning and Big Data Are Changing the Face of Biological Sciences

Until recently, the wet lab has been a crucial component of every biologist. Today's advances in the production of massive amounts of data and the creation of machine-learning algorithms for processing that data are changing the face of biological science—making it possible to do real science without a wet lab. David Heckerman shares several examples of how this transformation in the area of genomics is changing the pace of scientific breakthroughs.


Via Szabolcs Kósa, Dr. Stefan Gruenwald
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davidgibson's curator insight, May 28, 2013 11:05 PM

This 36 min video is well worth the time spent - to get an idea (hopefully a transferrable one) about Big Data and the frontiers of science. In this case both "wet lab" (test tubes microscopes) and "dry lab" (computer modeling with machine learning) and needed and so is content as well as computational literacy.