Have We Met Before? Direct Connections Found Between Areas of Brain Responsible for Voice and Face Recognition

Have We Met Before? Direct Connections Found Between Areas of Brain Responsible for Voice and Face Recognition

  12:11:00 AM    Science Lover

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Face and voice are the two main features by which we recognise other people. Researchers at the Max Planck Institute (MPI) for Human Cognitive and Brain Sciences have now discovered that there is a direct structural connection consisting of fibre pathways between voice- and face-recognition areas in the human brain. The exchange of information, which is assumed to take place between these areas via this connection, could help us to quickly identify familiar people in everyday situations and also under adverse conditions.

Direct structural connections exist between the two voice recognition areas (blue and red spheres) and the face recognition area (yellow sphere). In comparison, the connection to the area responsible for more general acoustic information (green sphere) is less strong. The connections appear to be part of larger fibre bundles (shown in grey). (Credit: MPI for Human Cognitive and Brain Sciences)

Theories differ as to what happens in the brain when we recognise familiar persons. Conventionally, it is assumed that voice and face recognition are separate processes which are only combined on a higher processing level. However, recent findings indicate that voice and face recognition are much more closely related. Katharina von Kriegstein, Leader of the Max Planck Research Group "Neural Mechanisms of Human Communication," found in previous research that areas of the brain which are responsible for the identification of faces also become active when we hear a familiar voice. These activations were accompanied by better voice recognition.

"We now assume that areas in the brain which are involved in voice and face recognition interact directly and influence each other," says Helen Blank, a member of von Kriegstein's research group. In a new study, Blank could show that a structural connection between voice and face recognition areas exists. She used diffusion-weighted magnetic resonance imaging, a method with which the course of white matter tracts in the brain can be reconstructed when combined with tractography, a mathematical modelling technique. Blank had located the areas responsible for voice and face recognition in her study participants by measuring the reactions of the brain to different voices and faces using magnetic resonance imaging.

Blank discovered a direct connection consisting of fibre pathways between the voice- and the face-recognition area. "It is particularly interesting that the face recognition area appears to be more strongly connected with the areas involved in voice identification, despite the fact that these areas are further away than areas which process information from voices on a more general level," says the researcher.



This direct connection in our brains could be used in everyday contexts to simulate the faces of our conversation partners, e.g. when we speak on the telephone to a familiar person. However, the precise nature of the information that is exchanged between the voice- and face-recognition areas remains unclear. A forthcoming study which Blank is currently preparing aims to clarify this issue.

Obtaining a more detailed understanding of how the brain works in relation to the processing of such basic tasks as person recognition could be of benefit in many different areas. "The finding is of interest for research on unusual neurological conditions, such as prosopagnosia and phonagnosia, which prevent people from being able to recognise others from their faces or voices," says Blank. The new insights could also stimulate innovations in computer technology and improve person recognition by machines.

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Researchers can predict future actions from human brain activity

Researchers can predict future actions from human brain activity

  2:42:00 AM    Science Lover

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Bringing the real world into the brain scanner, researchers at The University of Western Ontario from The Centre for Brain and Mind can now determine the action a person was planning, mere moments before that action is actually executed.

A volunteer completes tasks while in the functional magnetic
imaging (fMRI) machine. This research project focuses
on understanding how the human brain plans actions.

The findings were published this week in the prestigious Journal of Neuroscience, in the paper, "Decoding Action Intentions from Preparatory Brain Activity in Human Parieto-Frontal Networks."



"This is a considerable step forward in our understanding of how the human brain plans actions," says Jason Gallivan, a Western Neuroscience PhD student, who was the first author on the paper.

University of Western Ontario researchers Jody Culham and Jason Gallivan describe how they can use a fMRI to determine the action a person was planning, mere moments before that action is actually executed. Credit: The University of Western Ontario

Over the course of the one-year study, human subjects had their brain activity scanned using functional magnetic resonance imaging (fMRI) while they performed one of three hand movements: grasping the top of an object, grasping the bottom of the object, or simply reaching out and touching the object. The team found that by using the signals from many brain regions, they could predict, better than chance, which of the actions the volunteer was merely intending to do, seconds later.

"Neuroimaging allows us to look at how action planning unfolds within human brain areas without having to insert electrodes directly into the human brain. This is obviously far less intrusive," explains Western Psychology professor Jody Culham, who was the paper's senior author.

Gallivan says the new findings could also have important clinical implications: "Being able to predict a human's desired movements using brain signals takes us one step closer to using those signals to control prosthetic limbs in movement-impaired patient populations, like those who suffer from spinal cord injuries or locked-in syndrome."

                    Brain timecourse video of subject's fMRI image during experiment

Provided by University of Western Ontario

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Scientists Afflict Computers With 'Schizophrenia' to Better Understand the Human Brain

Scientists Afflict Computers With 'Schizophrenia' to Better Understand the Human Brain

  5:30:00 PM    Science Lover

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Computer networks that can't forget fast enough can 
show symptoms of a kind of virtual schizophrenia, 
giving researchers further clues to the inner workings 
of schizophrenic brains, researchers have found. 
(Credit: © Nikolai Sorokin / Fotolia)

Computer networks that can't forget fast enough can show symptoms of a kind of virtual schizophrenia, giving researchers further clues to the inner workings of schizophrenic brains, researchers at The University of Texas at Austin and Yale University have found.

The researchers used a virtual computer model, or "neural network," to simulate the excessive release of dopamine in the brain. They found that the network recalled memories in a distinctly schizophrenic-like fashion.

Their results were published in April in Biological Psychiatry.

"The hypothesis is that dopamine encodes the importance-the salience-of experience," says Uli Grasemann, a graduate student in the Department of Computer Science at The University of Texas at Austin. "When there's too much dopamine, it leads to exaggerated salience, and the brain ends up learning from things that it shouldn't be learning from."

The results bolster a hypothesis known in schizophrenia circles as the hyperlearning hypothesis, which posits that people suffering from schizophrenia have brains that lose the ability to forget or ignore as much as they normally would. Without forgetting, they lose the ability to extract what's meaningful out of the immensity of stimuli the brain encounters. They start making connections that aren't real, or drowning in a sea of so many connections they lose the ability to stitch together any kind of coherent story.

The neural network used by Grasemann and his adviser, Professor Risto Miikkulainen, is called DISCERN. Designed by Miikkulainen, DISCERN is able to learn natural language. In this study it was used to simulate what happens to language as the result of eight different types of neurological dysfunction. The results of the simulations were compared by Ralph Hoffman, professor of psychiatry at the Yale School of Medicine, to what he saw when studying human schizophrenics.

In order to model the process, Grasemann and Miikkulainen began by teaching a series of simple stories to DISCERN. The stories were assimilated into DISCERN's memory in much the way the human brain stores information-not as distinct units, but as statistical relationships of words, sentences, scripts and stories.

"With neural networks, you basically train them by showing them examples, over and over and over again," says Grasemann. "Every time you show it an example, you say, if this is the input, then this should be your output, and if this is the input, then that should be your output. You do it again and again thousands of times, and every time it adjusts a little bit more towards doing what you want. In the end, if you do it enough, the network has learned."

In order to model hyperlearning, Grasemann and Miikkulainen ran the system through its paces again, but with one key parameter altered. They simulated an excessive release of dopamine by increasing the system's learning rate-essentially telling it to stop forgetting so much.

"It's an important mechanism to be able to ignore things," says Grasemann. "What we found is that if you crank up the learning rate in DISCERN high enough, it produces language abnormalities that suggest schizophrenia."

After being re-trained with the elevated learning rate, DISCERN began putting itself at the center of fantastical, delusional stories that incorporated elements from other stories it had been told to recall. In one answer, for instance, DISCERN claimed responsibility for a terrorist bombing.

In another instance, DISCERN began showing evidence of "derailment"-replying to requests for a specific memory with a jumble of dissociated sentences, abrupt digressions and constant leaps from the first- to the third-person and back again.

"Information processing in neural networks tends to be like information processing in the human brain in many ways," says Grasemann. "So the hope was that it would also break down in similar ways. And it did."

The parallel between their modified neural network and human schizophrenia isn't absolute proof the hyperlearning hypothesis is correct, says Grasemann. It is, however, support for the hypothesis, and also evidence of how useful neural networks can be in understanding the human brain.

"We have so much more control over neural networks than we could ever have over human subjects," he says. "The hope is that this kind of modeling will help clinical research."

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Scientists erase bad memories from brain

Scientists erase bad memories from brain

  2:01:00 AM    Science Lover

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It may soon be possible to erase bad memories from the human brain.

Canadian scientists at the University of Toronto and the local Hospital for Sick Children have found a link between a given memory and specific neurons - the cells in the brain that transmit information - that store it.

The human brain has over 100 billion neurons, but memories are stored in only small number of them. Scientists have been trying to identify these precise neurons that encode a given memory.

Now in their experimental study on mice (which has 100 million neurons), the Toronto research team has succeeded in identifying precise neurons that carry a particular memory.

Unlike in the past when scientists had deleted an entire brain region in mice to try and erase a memory in the hopes of finding out about how memories are normally stored, the Toronto team has succeeded in removing only the small portion of neurons that stored a specific memory.

"Though previous studies have provided important evidence suggesting that specific neurons are involved in a memory, we believe this (study) paper is the first to establish causal links," a university statement quoted study leader and physiology professor Sheena Josselyn as saying.

In their previous experiments, the same research team had found evidence that in mice, fear memories are stored in specific neurons within a brain structure known as the lateral amygdala (LA) that have a high amount of a specific protein (CREB).

This means that CREB levels helps dictate which neurons are involved in storing a memory.

Now in their latest study, the research team destroyed only these LA neurons with high levels of CREB and found that mice no longer remembered the fearful event. The research team also showed that random removal of any LA neurons does not erase the fear memory. You have to remove only specific set of neurons that store a memory.

"Our experiences, both good and bad, teach us things," said study leader Josselyn.

"If we did not remember that the last time we touched a hot stove we got burned, we would be more likely to do it again. So in this sense, even memories of bad or frightening experiences are useful.

"However, there are some cases in which fearful memories become maladaptive, such as with post-traumatic stress disorder or severe phobia. Selectively erasing these intrusive memories may improve the lives of afflicted individuals," she said.

"Our studies suggest that one strategy would be to target interventions to that small subset of neurons actually involved in storing a memory, rather than the entire brain. It sounds like a futuristic film, but our results in mice do provide proof-of-principle that this may one day be possible in humans," said co-researcher Paul Frankland.

The study is published in the March 13 issue of the journal Science.

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