So our researchers should come up with objective testing to determine if this loss of cognition via stroke is correct.
https://medicalxpress.com/news/2018-06-scientists-intelligence-brain-scans.html
If you've ever lied about your IQ to seem
more intelligent, it's time to fess up. Scientists can now tell how
smart you are just by looking at a scan of your brain.
Actually, to be
more precise, the scientists themselves aren't looking at your brain
scan; a machine-learning algorithm they've developed is.
In a new study, researchers from Caltech, Cedars-Sinai Medical
Center, and the University of Salerno show that their new computing tool
can predict a person's
intelligence from
functional magnetic resonance
imaging (fMRI) scans of their resting state brain activity. Functional
MRI develops a map of brain activity by detecting changes in blood flow
to specific brain regions. In other words, an individual's intelligence
can be gleaned from patterns of activity in their brain when they're not
doing or thinking anything in particular—no math problems, no
vocabulary quizzes, no puzzles.
"We found if we just have people lie in the scanner and do nothing
while we measure the pattern of activity in their brain, we can use the
data to predict their intelligence," says Ralph Adolphs (Ph.D. '92),
Bren Professor of Psychology, Neuroscience, and Biology, and director
and Allen V. C. Davis and Lenabelle Davis Leadership Chair of the
Caltech Brain Imaging Center.
To train their algorithm on the complex patterns of activity in the
human brain, Adolphs and his team used data collected by the Human
Connectome Project (HCP), a scientific endeavor funded by the National
Institutes of Health (NIH) that seeks to improve understanding of the
many connections in the
human brain. Adolphs and his colleagues downloaded the
brain scans
and intelligence scores from almost 900 individuals who had
participated in the HCP, fed these into their algorithm, and set it to
work.
After processing the data, the team's algorithm was able to predict
intelligence at statistically significant levels across these 900
subjects, says Julien Dubois (Ph.D. '13), a postdoctoral fellow at
Cedars-Sinai Medical Center. But there is a lot of room for improvement,
he adds. The scans are coarse and noisy measures of what is actually
happening in the brain, and a lot of potentially useful information is
still being discarded.
"The information that we derive from the brain measurements can be
used to account for about 20 percent of the variance in intelligence we
observed in our subjects," Dubois says. "We are doing very well, but we
are still quite far from being able to match the results of hour-long
intelligence tests, like the Wechsler Adult Intelligence Scale,"
Dubois also points out a sort of philosophical conundrum inherent in
the work. "Since the algorithm is trained on intelligence scores to
begin with, how do we know that the
intelligence scores
are correct?" The researchers addressed this issue by extracting a more
precise estimate of intelligence across 10 different cognitive tasks
that the subjects had taken, not only from an IQ test.
In predicting intelligence from brain scans, the algorithm is doing
something that humans cannot, because even an experienced neuroscientist
cannot look at a brain scan and tell how intelligent a person is.
"If trained properly, these algorithms can answer questions as
complex as the one we are trying to answer here. They are very powerful,
but if you actually ask, 'How do they learn? How do they do these
things?' These are difficult questions to answer," says co-author Paola
Galdi, previously a Ph.D. student at the University of Salerno and now a
postdoctoral fellow at the University of Edinburgh.
The study was conducted as part of an ongoing quest to build a
diagnostic tool that can tell a great deal about a person's mind from
their brain scans. Adolphs and his colleagues say that they would like
to one day see MRIs work as well for diagnosing conditions like autism,
schizophrenia, and anxiety as they currently do for finding tumors,
aneurisms, or liver disease.
"Functional MRI has not yet delivered on its promise as a diagnostic
tool. We, and many others, are actively working to change this," says
Dubois. "The availability of large data sets that can be mined by
scientists around the world is making this possible."
Intelligence was chosen as one of the first test beds for the
technology because research has shown that it's very stable over time.
That is, a person's IQ score will not vary much over a period of weeks,
months, or years.
The researchers also conducted a parallel study, using the same test
population and approach, that attempted to predict personality traits
from fMRI brain scans. An individual's personality, Adolphs says, is at
least as stable as intelligence over a long period of time. The
personality test they used divides personality into five scales:
- Openness to experience: Preference for new experiences and ideas vs. preference for routine and predictability
- Conscientiousness: Self-discipline and thoughtfulness vs. spontaneity and flexibility
- Extraversion: Sociability and talkativeness vs. shyness and reservation
- Agreeableness: Friendliness and helpfulness vs. antagonism and argumentativeness
- Neuroticism: Confidence and predisposition to positive emotions vs. nervousness and predisposition to negative emotions
However, it has turned out to be much more difficult to predict
personality using the method the team used for predicting intelligence.
But this is not surprising, says Dubois.
"The personality scores in the database are just from short,
self-report questionnaires," he says. "That's not going to be a very
accurate measure of personality to begin with, so it is no wonder we
cannot predict it well from the MRI data."
Adolphs and Dubois say they are now teaming up with colleagues from
different fields, including Caltech philosophy professor Frederick
Eberhardt, to follow up on their findings.
Papers describing the two studies, titled "Resting-state functional
brain connectivity best predicts the personality dimension of openness
to experience," and "A distributed
brain
network predicts general intelligence from resting-state human
neuroimaging data," are available online through bioRχiv; their
publication in, respectively,
Personality Neuroscience and
Philosophical Transactions of the Royal Society, is pending.
More information:
Julien Dubois et al.
Resting-state functional brain connectivity best predicts the
personality dimension of openness to experience,
(2017).
DOI: 10.1101/215129
Julien C Dubois et al. A distributed brain network predicts general intelligence from resting-state human neuroimaging data,
(2018).
DOI: 10.1101/257865
Journal reference:
Philosophical Transactions of the Royal Society
Provided by:
California Institute of Technology
