Findings Highlight Need for Diversity in
Neurological and Psychiatric Research
NEW
YORK, May 14, 2024 /PRNewswire/ -- Artificial
intelligence (AI) computer programs that process MRI results
show differences in how the brains of men and women are organized
at a cellular level, a new study shows. These variations were
spotted in white matter, tissue primarily located in the human
brain's innermost layer, which fosters communication between
regions.
Men and women are known to experience multiple sclerosis, autism
spectrum disorder, migraines, and other brain issues at different
rates and with varying symptoms. A detailed understanding of how
biological sex impacts the brain is therefore viewed as a way to
improve diagnostic tools and treatments. However, while brain size,
shape, and weight have been explored, researchers have only a
partial picture of the brain's layout at the cellular level.
Led by researchers at NYU Langone Health, the new study used an
AI technique called machine learning to analyze thousands of MRI
brain scans from 471 men and 560 women. Results revealed that the
computer programs could accurately distinguish between biological
male and female brains by spotting patterns in structure and
complexity that were invisible to the human eye. The findings were
validated by three different AI models designed to identify
biological sex using their relative strengths in either zeroing in
on small portions of white matter or analyzing relationships across
larger regions of the brain.
"Our findings provide a clearer picture of how a living, human
brain is structured, which may in turn offer new insight into how
many psychiatric and neurological disorders develop and why they
can present differently in men and women," said study senior author
and neuroradiologist Yvonne Lui,
MD.
Lui, a professor and vice chair for research in the Department
of Radiology at NYU Grossman School of Medicine, notes that
previous studies of brain microstructure have largely relied on
animal models and human tissue samples. In addition, the validity
of some of these past findings has been called into question for
relying on statistical analyses of "hand-drawn" regions of
interest, meaning researchers needed to make many subjective
decisions about the shape, size, and location of the regions they
choose. Such choices can potentially skew the results, says
Lui.
The new study results, publishing online May 14 in the journal Scientific Reports,
avoided that problem by using machine learning to analyze
entire groups of images without asking the computer to inspect any
specific spot, which helped to remove human biases, the authors
say.
For the research, the team started by feeding AI programs
existing data examples of brain scans from healthy men and women
and also telling the machine programs the biological sex of each
brain scan. Since these models were designed to use complex
statistical and mathematical methods to get "smarter" over time as
they accumulated more data, they eventually "learned" to
distinguish biological sex on their own. Importantly, the programs
were restricted from using overall brain size and shape to make
their determinations, says Lui.
According to the results, all of the models correctly identified
the sex of subject scans between 92% and 98% of the time. Several
features in particular helped the machines make their
determinations, including how easily and in what direction water
could move through brain tissue.
"These results highlight the importance of diversity when
studying diseases that arise in the human brain," said study
co-lead author Junbo Chen, MS, a
doctoral candidate at NYU Tandon School of Engineering.
"If, as has been historically the case, men are used as a
standard model for various disorders, researchers may miss out on
critical insight," added study co-lead author Vara Lakshmi
Bayanagari, MS, a graduate research assistant at NYU Tandon School
of Engineering.
Bayanagari cautions that while the AI tools could report
differences in brain-cell organization, they could not reveal which
sex was more likely to have which features. She adds that the study
classified sex based on genetic information and only included MRIs
from cis-gendered men and women.
According to the authors, the team next plans to explore the
development of sex-related brain structure differences over time to
better understand environmental, hormonal, and social factors that
could play a role in these changes.
Funding for the study was provided by the National Institutes of
Health grants R01NS119767, R01NS131458, and P41EB017183, as well as
by the United States Department of Defense grant W81XWH2010699.
In addition to Lui, Chen, and Bayanagari, other NYU Langone
Health and NYU researchers involved in
the study were Sohae Chung, PhD, and Yao
Wang, PhD.
Media Inquiries:
Shira Polan
Phone: 212-404-4279
shira.polan@nyulangone.org
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SOURCE NYU Grossman School of Medicine and NYU Langone
Health