|
|
- Holonomic Control of a robot with an omnidirectional drive 2007-04-17
-
R. Rojas, A. Gloye Förster: Holonomic Control of a robot with an
omnidirectional drive, KI - Künstliche Intelligenz, vol. 20, nr. 2,
BöttcherIT Verlag, 2006.
- 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.
- Studying XCS/BOA learning in Boolean functions 2007-03-13
-
Butz, M.V., & Pelikan, M. (2006). Studying XCS/BOA learning in Boolean functions: Structure encoding and random boolean functions. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006). 1449-1456.
- 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.
- Researchers reverse Fragile X Syndrome symptoms in adult mice 2013-03-19
- Neuroscientists at MIT’s Picower Institute for Learning and Memory report in the March 18 Proceedings of the National Academy of Sciences (PNAS) that they have reversed autism symptoms in adult mice with a single dose of an experimental drug.
The work from the laboratory of Nobel laureate Susumu Tonegawa, the Picower Professor in the Department of Biology and a principal investigator at the Picower Institute, points to potential targets for drugs that may one day improve autism symptoms such as hyperactivity, repetitive behaviors and seizures in humans by modifying molecular mechanisms underlying the disease.
“These findings suggest a possible novel therapeutic target for the treatment of Fragile X Syndrome (FXS) — the most common inherited form of autism and intellectual disability,” said Eric Klann, a professor of neural science at New York University.
Using genetically modified mice that exhibit FXS symptoms, the researchers targeted neurons’ dendritic spines, small protrusions that receive signals from other neurons and are key to effective neuron-to-neuron communication within the brain. The researchers focused on spines in the temporal cortex, a part of the brain implicated in autism in humans.
Humans with FXS and autism, and the mouse model with FXS symptoms, have abnormally high densities of dendritic spines, leading to deficits in learning, cognition and behavior.
Tonegawa is scientific co-founder of Afraxis, a California-based company developing drugs that target p21-activated kinase or PAK, a key regulator of dendritic spines. Calling the inhibitor drug FRAX486, Tonegawa and colleagues demonstrated that inhibiting PAK with a single dose of FRAX496 reduced cellular and behavioral abnormalities in mice that model FXS.
This work was supported by the National Institutes of Health, the RIKEN Brain Science Institute and the Simons Center for the Social Brain at MIT.
- RNN-based Learning of Compact Maps for Efficient Robot Localization 2007-04-17
-
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
-
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
- Complex brain function depends on flexibility 2013-05-19
- Over the past few decades, neuroscientists have made much progress in mapping the brain by deciphering the functions of individual neurons that perform very specific tasks, such as recognizing the location or color of an object.
However, there are many neurons, especially in brain regions that perform sophisticated functions such as thinking and planning, that don’t fit into this pattern. Instead of responding exclusively to one stimulus or task, these neurons react in different ways to a wide variety of things. MIT neuroscientist Earl Miller first noticed these unusual activity patterns about 20 years ago, while recording the electrical activity of neurons in animals that were trained to perform complex tasks.
“We started noticing early on that there are a whole bunch of neurons in the prefrontal cortex that can’t be classified in the traditional way of one message per neuron,” recalls Miller, the Picower Professor of Neuroscience at MIT and a member of MIT’s Picower Institute for Learning and Memory.
In a paper appearing in Nature on May 19, Miller and colleagues at Columbia University report that these neurons are essential for complex cognitive tasks, such as learning new behavior. The Columbia team, led by the study’s senior author, Stefano Fusi, developed a computer model showing that without these neurons, the brain can learn only a handful of behavioral tasks.
“You need a significant proportion of these neurons,” says Fusi, an associate professor of neuroscience at Columbia. “That gives the brain a huge computational advantage.”
Lead author of the paper is Mattia Rigotti, a former grad student in Fusi’s lab.
Multitasking neurons
Miller and other neuroscientists who first identified this neuronal activity observed that while the patterns were difficult to predict, they were not random. “In the same context, the neurons always behave the same way. It’s just that they may convey one message in one task, and a totally different message in another task,” Miller says.
For example, a neuron might distinguish between colors during one task, but issue a motor command under different conditions.
Miller and colleagues proposed that this type of neuronal flexibility is key to cognitive flexibility, including the brain’s ability to learn so many new things on the fly. “You have a bunch of neurons that can be recruited for a whole bunch of different things, and what they do just changes depending on the task demands,” he says.
At first, that theory encountered resistance “because it runs against the traditional idea that you can figure out the clockwork of the brain by figuring out the one thing each neuron does,” Miller says.
For the new Nature study, Fusi and colleagues at Columbia created a computer model to determine more precisely what role these flexible neurons play in cognition, using experimental data gathered by Miller and his former grad student, Melissa Warden. That data came from one of the most complex tasks that Miller has ever trained a monkey to perform: The animals looked at a sequence of two pictures and had to remember the pictures and the order in which they appeared.
During this task, the flexible neurons, known as “mixed selectivity neurons,” exhibited a great deal of nonlinear activity — meaning that their responses to a combination of factors cannot be predicted based on their response to each individual factor (such as one image).
Expanding capacity
Fusi’s computer model revealed that these mixed selectivity neurons are critical to building a brain that can perform many complex tasks. When the computer model includes only neurons that perform one function, the brain can only learn very simple tasks. However, when the flexible neurons are added to the model, “everything becomes so much easier and you can create a neural system that can perform very complex tasks,” Fusi says.
The flexible neurons also greatly expand the brain’s capacity to perform tasks. In the computer model, neural networks without mixed selectivity neurons could learn about 100 tasks before running out of capacity. That capacity greatly expanded to tens of millions of tasks as mixed selectivity neurons were added to the model. When mixed selectivity neurons reached about 30 percent of the total, the network’s capacity became “virtually unlimited,” Miller says — just like a human brain.
Mixed selectivity neurons are especially dominant in the prefrontal cortex, where most thought, learning and planning takes place. This study demonstrates how these mixed selectivity neurons greatly increase the number of tasks that this kind of neural network can perform, says John Duncan, a professor of neuroscience at Cambridge University.
“Especially for higher-order regions, the data that have often been taken as a complicating nuisance may be critical in allowing the system actually to work,” says Duncan, who was not part of the research team.
Miller is now trying to figure out how the brain sorts through all of this activity to create coherent messages. There is some evidence suggesting that these neurons communicate with the correct targets by synchronizing their activity with oscillations of a particular brainwave frequency.
“The idea is that neurons can send different messages to different targets by virtue of which other neurons they are synchronized with,” Miller says. “It provides a way of essentially opening up these special channels of communications so the preferred message gets to the preferred neurons and doesn’t go to neurons that don’t need to hear it.”
The research was funded by the Gatsby Foundation, the Swartz Foundation and the Kavli Foundation.
- Combining Gradient-Based With Evolutionary Online Learning: An Introduction to Learning Classifier Systems 2007-10-30
-
Seventh International Conference on Hybrid Intelligent Systems (HIS 2007), 12-17.
|
|
