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| An innovative machine learning method anticipates neurocognitive changes, similar to predictive text-entry for cell phones, Internet search engines. (Credit: © ktsdesign / Fotolia) |
- At UCLA's Laboratory of Integrative Neuroimaging Technology, researchers use functional MRI brain scans to observe brain signal changes that take place during mental activity.
- They then employ computerized machine learning (ML) methods to study these patterns and identify the cognitive state -- or sometimes the thought process -- of human subjects. The technique is called "brain reading" or "brain decoding."
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| This technique is called "brain reading" or "brain decoding." |
The research, presented last week at the Neural Information
Processing Systems' Machine Learning and Interpretation in Neuroimaging
workshop in Spain, was funded by the National Institute on Drug Abuse,
which is interested in using these method to help people control drug
cravings.
In this study on addiction and cravings, the team classified data
taken from cigarette smokers who were scanned while watching videos
meant to induce nicotine cravings. The aim was to understand in detail
which regions of the brain and which neural networks are responsible for
resisting nicotine addiction specifically, and cravings in general,
said Dr. Ariana Anderson, a postdoctoral fellow in the Integrative
Neuroimaging Technology lab and the study's lead author.
"We are interested in exploring the relationships between structure
and function in the human brain, particularly as related to higher-level
cognition, such as mental imagery," Anderson said. "The lab is engaged
in the active exploration of modern data-analysis approaches, such as
machine learning, with special attention to methods that reveal
systems-level neural organization."
For the study, smokers sometimes watched videos meant to induce
cravings, sometimes watched "neutral" videos and at sometimes watched no
video at all. They were instructed to attempt to fight nicotine
cravings when they arose.
The data from fMRI scans taken of the study participants was then
analyzed. Traditional machine learning methods were augmented by Markov
processes, which use past history to predict future states. By measuring
the brain networks active over time during the scans, the resulting
machine learning algorithms were able to anticipate changes in subjects'
underlying neurocognitive structure, predicting with a high degree of
accuracy (90 percent for some of the models tested) what they were
watching and, as far as cravings were concerned, how they were reacting
to what they viewed.
"We detected whether people were watching and resisting cravings,
indulging in them, or watching videos that were unrelated to smoking or
cravings," said Anderson, who completed her Ph.D. in statistics at UCLA.
"Essentially, we were predicting and detecting what kind of videos
people were watching and whether they were resisting their cravings."
In essence, the algorithm was able to complete or "predict" the
subjects' mental states and thought processes in much the same way that
Internet search engines or texting programs on cell phones anticipate
and complete a sentence or request before the user is finished typing.
And this machine learning method based on Markov processes demonstrated a
large improvement in accuracy over traditional approaches, the
researchers said.
Machine learning methods, in general, create a "decision layer" --
essentially a boundary separating the different classes one needs to
distinguish. For example, values on one side of the boundary might
indicate that a subject believes various test statements and, on the
other, that a subject disbelieves these statements. Researchers have
found they can detect these believe-disbelieve differences with high
accuracy, in effect creating a lie detector. An innovation described in
the new study is a means of making these boundaries interpretable by
neuroscientists, rather than an often obscure boundary created by more
traditional methods, like support vector machine learning.
"In our study, these boundaries are designed to reflect the
contributed activity of a variety of brain sub-systems or networks whose
functions are identifiable -- for example, a visual network, an
emotional-regulation network or a conflict-monitoring network," said
study co-author Mark S. Cohen, a professor of neurology, psychiatry and
biobehavioral sciences at UCLA's Staglin Center for Cognitive
Neuroscience and a researcher at the California NanoSystems Institute at
UCLA.
"By projecting our problem of isolating specific networks associated
with cravings into the domain of neurology, the technique does more than
classify brain states -- it actually helps us to better understand the
way the brain resists cravings," added Cohen, who also directs UCLA's
Neuroengineering Training Program.
Remarkably, by placing this problem into neurological terms, the
decoding process becomes significantly more reliable and accurate, the
researchers said. This is especially significant, they said, because it
is unusual to use prior outcomes and states in order to inform the
machine learning algorithms, and it is particularly challenging in the
brain because so much is unknown about how the brain works.
Machine learning typically involves two steps: a "training phase" in
which the computer evaluates a set of known outcomes -- say, a bunch of
trials in which a subject indicated belief or disbelief -- and a second,
"prediction" phase in which the computer builds a boundary based on
that knowledge.
In future research, the neuroscientists said, they will be using
these machine learning methods in a biofeedback context, showing
subjects real-time brain readouts to let them know when they are
experiencing cravings and how intense those cravings are, in the hopes
of training them to control and suppress those cravings.
But since this clearly changes the process and cognitive state for
the subject, the researchers said, they may face special challenges in
trying to decode a "moving target" and in separating the "training"
phase from the "prediction" phase.
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Story Source:
The above story is reprinted from materials provided by University of California - Los Angeles. The original article was written by Jennifer Marcus.___________________________________________________________________________________
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
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