For the first time, a new tool developed at the Department of
Energy's (DOE's) Lawrence Berkeley National Laboratory (Berkeley Lab)
allows researchers to interactively explore the hierarchical processes
that happen in the brain when it is resting or performing tasks.
Scientists also hope that the tool can shed some light on how
neurological diseases like Alzheimer's spread throughout the brain.
Did you know that your brain processes information in a hierarchy? As
you are reading this page, the signal coming in through your eyes
enters your brain through the thalamus, which organizes it. That
information then goes on to the primary visual cortex at the back of the
brain, where populations of neurons respond to very specific basic
‘A software, called Brain Modulyzer, combines multiple coordinated views of functional magnetic resonance imaging (fMRI) data to provide context for brain connectivity data.’
For instance, one set of neurons might fire up because the
text on your screen is black and another set might activate because
there are vertical lines. This population will then trigger a secondary
set of neurons that respond to more complex shapes like circles, and so
on until you have a complete picture.
Created in conjunction with computer scientists at University of
California, Davis (UC Davis) and with input from neuroscientists at UC
San Francisco (UCSF), the software, called Brain Modulyzer, combines
multiple coordinated views of functional magnetic resonance imaging
(fMRI) data - like heat maps, node link diagrams and anatomical views - to
provide context for brain connectivity data.
"The tool provides a novel framework of visualization and new
interaction techniques that explore the brain connectivity at various
hierarchical levels. This method allows researchers to explore multipart
observations that have not been looked at before," says Sugeerth
Murugesan, who co-led the development of Brain Modulyzer. He is
currently a graduate student researcher at Berkeley Lab and a PhD
candidate at UC Davis.
"Other tools tend to look at abstract or statistical network
connections but don't do quite a good job at connecting back to the
anatomy of the brain. We made sure that Brain Modulyzer connects to
brain anatomy so that we can simultaneously appreciate the abstract
information in anatomical context," says Jesse Brown, a postdoctoral
researcher at UCSF who advised the Berkeley Lab development team on the
A paper describing Brain Modulyzer was recently published online in the IEEE/ACM Transactions on Computational Biology and Bioinformatics
Brain Modulyzer is now available on github. Murugesan and Berkeley Lab
Computer Scientist Gunther Weber developed the tool together. Weber is
also an adjunct professor in the Department of Computer Science at UC
Davis. UCSF Associate Professor of Neurology William Seeley also advised
the tool's development.
Predicting Spread of Neurodegenerative Diseases
As a neuroscientist at UCSF's Memory and Aging Center, Brown and his
colleagues use neuroimaging to diagnose diseases, like Alzheimer's and
dementia, as well as monitor how the diseases progress over time.
Ultimately, their goal is to build a predictive model of how a disease
will spread in the brain based on where it starts.
"We know that the brain is built like a network, with axons at the
tip of neurons that project to other neurons. That's the main way the
neurons connect with each other, so one way to think about disease
spreading in the brain is that it starts in one place and kind of jumps
over along the network connections," says Brown.
To see how a brain region is connected to other brain regions, Brown
and his colleagues examine the fMRIs of healthy subjects. The set of
connections observed in the fMRIs are visualized as a network. "For us
the connection pattern of the network in healthy subjects is valuable
information, because if we then study a patient with dementia and see
that the disease is starting at point a in that network, we can expect
that it will soon spread through the network connections to points b and
c," Brown adds.
Before Brain Modulyzer, researchers could only explore these neural
networks by creating static images of the brain regions they were
studying and superimposing those pictures on an anatomical diagram of
the entire brain. On the same screen, they'd also look at fMRI data that
had been reduced to a static network diagram.
"The problem with this analysis process is that it's all static. If I
wanted to explore another region of the brain, which would be a
different pattern, I'd have to input a whole different set of data and
create another set of static images," says Brown.
But with Brain Modulyzer, all he has to do is input a matrix that
describes the connection strengths between all of the brain regions that
he is interested in studying and the tool will automatically detect the
networks. Each network is colored differently in the anatomical view
and the information visualized abstractly in a number of graph and
"Modulyzer is such a helpful tool for discovery because it bubbles
up really important information about functional brain properties,
including information that we knew was there before, but it also
connects to brain regions that we didn't realize existed before in the
dataset. Every time I use it, I find something surprising in the data,"
says Brown. "It is also incredibly valuable for researchers who don't
know these methods as well. It will allow them to be a lot more
efficient in detecting connections between brain regions that are
important for cognition."
History and Next Steps
The idea for Brain Modulyzer initiated when Berkeley Lab's Weber and
Seeley met at the "Computational Challenges for Precision Medicine" in
November 2012. This workshop brought together investigators from
Berkeley and UCSF to focus on computational challenges posed by
precision medicine. Their initial discussions led to a collaboration
with Oblong Industries - a company that builds computer interfaces - to
translate laboratory data collected at UCSF into 3D visualizations of
brain structures and activity. The results of this collaboration were
presented at the Precision Medicine Summit in May 2013.
"At the Aging and Memory Center at UCSF, our expertise is in
neuroscience, neurological diseases and dementia. We are really
fortunate to be in touch with Berkeley Lab scientists whose expertise in
visualization, maps and working with big data exploration helped us
build such amazing tools," says Brown. "The precision medicine
collaboration was such a fruitful collaboration for everyone that we
decided to stay in touch."
After the Precision Medicine Summit, the team discussed
possibilities for further collaboration, which led to a Laboratory
Directed Research and Development (LDRD) project at Berkeley Lab called
"Graph-based Analysis and Visualization of Multimodal Multi-resolution
Large-scale Neuropathology Data." Part of the funding for Brain
Modulyzer development came from this LDRD, as well as grants to Seeley
from the Tau Consortium and National Institutes of Health.
Soon, the team hopes to present their Brain Modulyzer paper to the
neuroscience community for feedback. "We want to make sure that this
tool is useful to the community, so we will keep iterating on it," says
Brown. "We have plenty of ideas to improve on what we have, and we think
that Modulyzer will keep getting better over time."