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- Brain’s language center has multiple roles 2012-10-16
- A century and a half ago, French physician Pierre Paul Broca found that patients with damage to part of the brain’s frontal lobe were unable to speak more than a few words. Later dubbed Broca’s area, this region is believed to be critical for speech production and some aspects of language comprehension.
However, in recent years neuroscientists have observed activity in Broca’s area when people perform cognitive tasks that have nothing to do with language, such as solving math problems or holding information in working memory. Those findings have stimulated debate over whether Broca’s area is specific to language or plays a more general role in cognition.
A new study from MIT may help resolve this longstanding question. The researchers, led by Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience, found that Broca’s area actually consists of two distinct subunits. One of these focuses selectively on language processing, while the other is part of a brainwide network that appears to act as a central processing unit for general cognitive functions.
“I think we’ve shown pretty convincingly that there are two distinct bits that we should not be treating as a single region, and perhaps we shouldn’t even be talking about ‘Broca’s area’ because it’s not a functional unit,” says Evelina Fedorenko, a research scientist in Kanwisher’s lab and lead author of the new study, which recently appeared in the journal Current Biology.
Kanwisher and Fedorenko are members of MIT’s Department of Brain and Cognitive Sciences and the McGovern Institute for Brain Research. John Duncan, a professor of neuroscience at the Cognition and Brain Sciences Unit of the Medical Research Council in the United Kingdom, is also an author of the paper.
A general role
Broca’s area is located in the left inferior frontal cortex, above and behind the left eye. For this study, the researchers set out to pinpoint the functions of distinct sections of Broca’s area by scanning subjects with functional magnetic resonance imaging (fMRI) as they performed a variety of cognitive tasks.
To locate language-selective areas, the researchers asked subjects to read either meaningful sentences or sequences of nonwords. A subset of Broca’s area lit up much more when the subjects processed meaningful sentences than when they had to interpret nonwords.
The researchers then measured brain activity as the subjects performed easy and difficult versions of general cognitive tasks, such as doing a math problem or holding a set of locations in memory. Parts of Broca’s area lit up during the more demanding versions of those tasks. Critically, however, these regions were spatially distinct from the regions involved in the language task.
These data allowed the researchers to map, for each subject, two distinct regions of Broca’s area — one selectively involved in language, the other involved in responding to many demanding cognitive tasks. The general region surrounds the language region, but the exact shapes and locations of the borders between the two vary from person to person.
The general-function region of Broca’s area appears to be part of a larger network sometimes called the multiple demand network, which is active when the brain is tackling a challenging task that requires a great deal of focus. This network is distributed across frontal and parietal lobes in both hemispheres of the brain, and all of its components appear to communicate with one another. The language-selective section of Broca’s area also appears to be part of a larger network devoted to language processing, spread throughout the brain’s left hemisphere.
Mapping functions
The findings provide evidence that Broca’s area should not be considered to have uniform functionality, says Peter Hagoort, a professor of cognitive neuroscience at Radboud University Nijmegen in the Netherlands. Hagoort, who was not involved in this study, adds that more work is needed to determine whether the language-selective areas might also be involved in any other aspects of cognitive function. “For instance, the language-selective region might play a role in the perception of music, which was not tested in the current study,” he says.
The researchers are now trying to determine how the components of the language network and the multiple demand network communicate internally, and how the two networks communicate with each other. They also hope to further investigate the functions of the two components of Broca’s area.
“In future studies, we should examine those subregions separately and try to characterize them in terms of their contribution to various language processes and other cognitive processes,” Fedorenko says.
The team is also working with scientists at Massachusetts General Hospital to study patients with a form of neurodegeneration that gradually causes loss of the ability to speak and understand language. This disorder, known as primary progressive aphasia, appears to selectively target the language-selective network, including the language component of Broca’s area.
The research was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the Ellison Medical Foundation and the U.K. Medical Research Council.
- Scientists image brain structures that deteriorate in Parkinson’s 2012-11-26
- A new imaging technique developed at MIT offers the first glimpse of the degeneration of two brain structures affected by Parkinson’s disease.
The technique, which combines several types of magnetic resonance imaging (MRI), could allow doctors to better monitor patients’ progression and track the effectiveness of potential new treatments, says Suzanne Corkin, MIT professor emerita of neuroscience and leader of the research team. The first author of the paper is David Ziegler, who received his PhD in brain and cognitive sciences from MIT in 2011.
The study, appearing in the Nov. 26 online edition of the Archives of Neurology, is also the first to provide clinical evidence for the theory that Parkinson’s neurodegeneration begins deep in the brain and advances upward.
“This progression has never been shown in living people, and that’s what was special about this study. With our new imaging methods, we can see these structures more clearly than anyone had seen them before,” Corkin says.
Parkinson’s disease currently affects 1 to 2 percent of people over 65, totaling five million people worldwide. The disease gradually destroys the brain cells that control movement, leaving most patients wheelchair-bound and completely dependent on caregivers. “A major obstacle to research on the causes and progression of this disease has been a lack of effective brain imaging methods for the areas affected by the disease,” Ziegler says.
In 2004, Heiko Braak, an anatomist at Johann Wolfgang Goethe University in Frankfurt, Germany, classified Parkinson’s disease into six stages, based on the appearances of the affected brain structures. He proposed that during the earliest stages, a structure deep inside the brain, known as the substantia nigra, begins to degenerate. This structure is critical for movement and also plays important roles in reward and addiction.
Later, Braak proposed, degeneration spreads outward to a brain region known as the basal forebrain. This area, located behind the eyes, includes several structures that produce acetylcholine, a neurotransmitter important for learning and memory.
Neuropathologists (scientists who study the brains of deceased patients) had found evidence for this sequence of events, but it had never been observed in living patients because the substantia nigra, deep within the brain, is so difficult to image with conventional MRI.
To overcome that, the MIT team used four types of MRI scans, each of which uses slightly different magnetic fields, generating different images. By combining these scans, the researchers created composite images of each patient’s brain that clearly show the substantia nigra and basal forebrain. “Our new MRI methods provide an unparalleled view of these two structures, allowing us to calculate the precise volumes of each structure,” Ziegler says.
After scanning normal brains, the researchers studied 29 early-stage Parkinson’s patients. They found significant loss of volume in the substantia nigra early on, followed by loss of basal forebrain volume later in the disease, as predicted by Braak.
The findings appear to correlate with the appearance of symptoms in Parkinson’s patients, says Joel Perlmutter, a professor of neurology at the Washington University School of Medicine. “This suggests that two different systems of the brain — one dopaminergic and associated with motor control, and one cholinergic and associated with cognitive function — have different timing,” Perlmutter says.
In future studies, this MRI technique could be used to follow patients over time and measure whether degeneration of the two areas is correlated or if they deteriorate independently of one another, Corkin says.
This approach could also give doctors a new way to monitor how their patients are responding to treatment, she says. (Most patients are treated with dopamine, which helps to overcome the loss of dopamine-producing neurons in the substantia nigra.) Researchers could also use the new imaging tools to determine the effects of potential new treatments.
- “Exhibitionists” and “Voyeurs” do it better: a Shared Environment for Flexible Coordination with Tacit Messages 2007-04-10
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Tummolini, L., Castelfranchi C., Ricci, A. Viroli, M., Omicini, A. (2005); in H. van Parunak, D. Weyns (Eds.) Lecture Notes in Artificial Intelligence, LNAI 3374, Sringer-Verlag Heidelberg, pp. 215-231.
- Mapping neurological disease 2012-09-05
- Disorders such as schizophrenia can originate in certain regions of the brain and then spread out to affect connected areas. Identifying these regions of the brain, and how they affect the other areas they communicate with, would allow drug companies to develop better treatments and could ultimately help doctors make a diagnosis. But interpreting the vast amounts of data produced by brain scans to identify these connecting regions has so far proved impossible.
Now, researchers in the Computer Science and Artificial Intelligence Laboratory at MIT have developed an algorithm that can analyze information from medical images to identify diseased areas of the brain and their connections with other regions.
The MIT researchers will present the work next month at the International Conference on Medical Image Computing and Computer Assisted Intervention in Nice, France.
The algorithm, developed by Polina Golland, an associate professor of computer science, and graduate student Archana Venkataraman, extracts information from two different types of magnetic resonance imaging (MRI) scans. The first, called diffusion MRI, looks at how water diffuses along the white-matter fibers in the brain, providing insight into how closely different areas are connected to one another. The second, known as functional MRI, probes how different parts of the brain activate when they perform particular tasks, and so can reveal when two areas are active at the same time and are therefore connected.
These two scans alone can produce huge amounts of data on the network of connections in the brain, Golland says. “It’s quite hard for a person looking at all of that data to integrate it into a model of what is going on, because we’re not good at processing lots of numbers.”
So the algorithm first compares all the data from the brain scans of healthy people with those of patients with a particular disease, to identify differences in the connections between the two groups that indicate disruptions caused by the disorder.
However, this step alone is not enough, since much of our understanding of what goes on in the brain concerns the individual regions themselves, rather than the connections between them, making it difficult to integrate this information with existing medical knowledge.
So the algorithm then analyzes this network of connections to create a map of the areas of the brain most affected by the disease. “It is based on the assumption that with any disease you get a small subset of regions that are affected, which then affect their neighbors through this connectivity change,” Golland says. “So our methods extract from the data this set of regions that can explain the disruption of connectivity that we see.”
It does this by hypothesizing, based on an overall map of the connections between each of the regions in the brain, what disruptions in signaling it would expect to see if a particular region were affected. In this way, when the algorithm detects any disruption in connectivity in a particular scan, it knows which regions must have been affected by the disease to create such an impact. “It basically finds the subset of regions that best explains the observed changes in connectivity between the normal control scan and the patient scan,” Golland says.
When the team used the algorithm to compare the brain scans of patients with schizophrenia to those of healthy people, they were able to identify three regions of the brain — the right posterior cingulate and the right and left superior temporal gyri — that are most affected by the disease.
In the long term, this could help drug companies develop more effective treatments for the disease that specifically target these regions of the brain, Golland says. In the meantime, by revealing all the different parts of the brain that are affected by a particular disorder, it can help doctors to make sense of how the disease evolves, and why it produces certain symptoms.
Ultimately, the method could also be used to help doctors diagnose patients whose symptoms could represent a number of different disorders, Golland says. By analyzing the patient’s brain scan to pinpoint which regions are affected, it could identify which disorder would create this particular disruption, she says.
In addition to schizophrenia, the researchers, who developed the algorithm alongside Marek Kubicki, associate director of the Psychiatry Neuroimaging Laboratory at Harvard Medical School, are also investigating the possibility of using the method to study Huntington’s disease.
Gregory Brown, associate director of clinical neuroscience at the University of California at San Diego’s Center for Functional MRI, who was not involved in developing the model, plans to use it to study the effects of HIV and drug addiction. “We will use the method to gain a clearer perspective on how HIV infection and methamphetamine dependence disrupts large-scale brain circuitry,” he says.
The method is a critical step away from studying the brain as a collection of localized regions toward a more realistic systems perspective, he says. This should assist the study of disorders such as schizophrenia, neurocognitive impairment and dementia associated with AIDS, and multiple sclerosis, which are best characterized as diseases of brain systems, he says.
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Georgi Petkov, Kiril Kiryazov, Maurice Grinberg, Boicho Kokinov (2007); Proceedings of the Second European Cognitive Science Conference, Greece
- Effect of pure error-based fitness in XCS 2007-10-30
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- A Schema Based Model of the Praying Mantis 2006-12-06
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(2006) From animals to animats 9: Proceedings of the Ninth International Conference on Simulation of Adaptive Behaviour
- RNN-based Learning of Compact Maps for Efficient Robot Localization 2007-04-17
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A. Foerster, A. Graves, J. Schmidhuber: RNN-based Learning of Compact Maps for Efficient Robot Localization. ESANN 2007.
- The Interplay of Analogy-Making with Active Vision and Motor Control in Anticipatory Robots 2007-05-09
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Kiril Kiryazov, Georgi Petkov, Maurice Grinberg, Boicho Kokinov, Christian Balkenius (2006); Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior, LNAI number 4520
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