How neuroimaging can be better utilized to yield diagnostic information
about individuals

Date:
March 14, 2023
Source:
Dartmouth College
Summary:
Since the development of functional magnetic resonance imaging
in the 1990s, the reliance on neuroimaging has skyrocketed as
researchers investigate how fMRI data from the brain at rest,
and anatomical brain structure itself, can be used to predict
individual traits, such as depression, cognitive decline, and brain
disorders. But how reliable brain imaging is for detecting traits
has been a subject of wide debate.

Researchers now report that stronger links between brain measures
and traits can be obtained when state-of-the-art pattern recognition
(or 'machine learning') algorithms are utilized, which can garner
high- powered results from moderate sample sizes.


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FULL STORY ========================================================================== Since the development of functional magnetic resonance imaging in the
1990s, the reliance on neuroimaging has skyrocketed as researchers
investigate how fMRI data from the brain at rest, and anatomical brain structure itself, can be used to predict individual traits, such as
depression, cognitive decline, and brain disorders.


========================================================================== Brain imaging has the potential to reveal the neural underpinnings of
many traits, from disorders like depression and chronic widespread pain
to why one person has a better memory than another, and why some people's memories are resilient as they age. But how reliable brain imaging is
for detecting traits has been a subject of wide debate.

Prior research on brain-wide associated studies (termed 'BWAS') has
shown that links between brain function and structure and traits are
so weak that thousands of participants are needed to detect replicable
effects. Research of this scale requires millions of dollars in investment
in each study, limiting which traits and brain disorders can be studied.

However, according to a new commentary published in Nature, stronger links between brain measures and traits can be obtained when state-of-the-art
pattern recognition (or 'machine learning') algorithms are utilized,
which can garner high-powered results from moderate sample sizes.

In their article, researchers from Dartmouth and University Medicine
Essen provide a response to an earlier analysis of brain-wide
association studies led by Scott Marek at Washington University School
of Medicine in St. Louis, Brenden Tervo-Clemmens at Massachusetts General Hospital/Harvard Medical School, and colleagues. The earlier study found
very weak associations across a range of traits in several large brain
imaging studies, concluding that thousands of participants would be
needed to detect these associations.

The new article explains that the very weak effects found in the earlier
paper do not apply to all brain images and all traits, but rather are
limited to specific cases. It outlines how fMRI data from hundreds of participants, as opposed to thousands, can be better leveraged to yield important diagnostic information about individuals.

One key to stronger associations between brain images and traits such
as memory and intelligence is the use of state-of-the-art pattern
recognition algorithms.

"Given that there's virtually no mental function performed entirely by
one area of the brain, we recommend using pattern recognition to develop
models of how multiple brain areas contribute to predicting traits,
rather than testing brain areas individually," says senior author Tor
Wager, the Diana L. Taylor Distinguished Professor of Psychological and
Brain Sciences and director of the Brain Imaging Center at Dartmouth.

"If models of multiple brain areas working together rather than in
isolation are applied, this provides for a much more powerful approach
in neuroimaging studies, yielding predictive effects that are four times
larger than when testing brain areas in isolation," says lead author
Tamas Spisak, head of the Predictive Neuroimaging Lab at the Institute of Diagnostic and Interventional Radiology and Neuroradiology at University Medicine Essen.

However, not all pattern recognition algorithms are equal and finding the algorithms that work best for specific types of brain imaging data is
an active area of research. The earlier paper by Marek, Tervo-Clemmens
et al. also tested whether pattern recognition can be used to predict
traits from brain images, but Spisak and colleagues found that the
algorithm they used is suboptimal.

When the researchers applied a more powerful algorithm, the effects
got even larger and reliable associations could be detected in much
smaller samples.

"When you do the power calculations on how many participants are needed
to detect replicable effects, the number drops to below 500 people,"
Spisak says.

"This opens the field to studies of many traits and clinical conditions
for which obtaining thousands of patients is not possible, including
rare brain disorders," says co-author Ulrike Bingel at University
Medicine Essen, who is the head of the University Centre for Pain
Medicine. "Identifying markers, including those involving the central
nervous system, are urgently needed, as they are critical to improve diagnostics and individually tailored treatment approaches. We
need to move towards a personalized medicine approach grounded in
neuroscience. The potential for multivariate BWAS to move us towards this
goal should not be underestimated." The team explains that the weak associations found in the earlier analysis, particularly through brain
images, were collected while people were simply resting in the scanner,
rather than performing tasks. But fMRI can also capture brain activity
linked to specific moment-by-moment thoughts and experiences.

Wager believes that linking brain patterns to these experiences may be a
key to understanding and predicting differences among individuals. "One
of the challenges associated with using brain imaging to predict traits
is that many traits aren't stable or reliable. If we use brain imaging to
focus on studying mental states and experiences, such as pain, empathy,
and drug craving, the effects can be much larger and more reliable,"
says Wager. "The key is finding the right task to capture the state."
"For example, showing images of drugs to people with substance use
disorders can elicit drug cravings, according to an earlier study
revealing a neuromarker for cravings," says Wager.

"Identifying which approaches to understanding the brain and mind are
most likely to succeed is important, as this affects how stakeholders
view and ultimately fund translational research in neuroimaging," says
Bingel. "Finding the limitations and working together to overcome them
is key to developing new ways of diagnosing and caring for patients with
brain and mental health disorders."
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========================================================================== Story Source: Materials provided by Dartmouth_College. Original written
by Amy Olson. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Brenden Tervo-Clemmens, Scott Marek, Roselyne J. Chauvin, Andrew
N. Van,
Benjamin P. Kay, Timothy O. Laumann, Wesley K. Thompson, Thomas E.

Nichols, B. T. Thomas Yeo, Deanna M. Barch, Beatriz Luna, Damien
A. Fair, Nico U. F. Dosenbach. Reply to: Multivariate BWAS can be
replicable with moderate sample sizes. Nature, 2023; 615 (7951):
E8 DOI: 10.1038/s41586- 023-05746-w ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/03/230314205337.htm

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