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 Massachusetts Institute of Technology
(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 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.
