Pattern Recognition Technology May Help Predict Future Mental Illness in Teens

Pattern Recognition Technology May Help Predict Future Mental Illness in Teens


Source: NIMH

A technique combining computer-based pattern recognition and brain imaging data accurately distinguished teens at risk for mental disorders from those with low risk and may someday be useful in predicting risk in individuals, according to an NIMH-funded study published February 15, 2012, in the journal PLoS One.

Background

Research on risk for mental disorders generally describes risk factors that apply to groups. To date, no biological measures can accurately predict an individual’s risk of future mental disorders.

Mary Phillips, M.D., of the University of Pittsburgh School of Medicine, and colleagues evaluated the use of computer-based techniques that automatically find patterns in data—these techniques are collectively called machine learning—with functional magnetic resonance imaging (fMRI) data. The researchers obtained fMRI data from 32 teens, half of whom had at least one biological parent diagnosed with bipolar disorder and were therefore at genetic risk for future psychiatric disorders. The other half of teens had no history of mental disorders either personally or in their immediate families.

The teens’ brain activity was assessed as they identified the gender of actors depicting various emotional facial expressions (happy, fearful, or neutral) in a series of photographs. Previous research has linked various mental disorders, especially depression and bipolar disorder, with abnormal patterns of brain activity during this task. Based on this fMRI data, the researchers used machine learning to calculate each participant’s odds for future mental illness social worker ceus

The participants were also assessed clinically and with fMRI at the start of the study, and clinically assessed again about two years later, on average. Long-term follow up is ongoing, with successive face-to-face assessments occurring every other year.

Results

Machine learning combined with fMRI accurately identified most of the healthy teens at genetic risk of future mental disorders vs. healthy teens with low genetic risk. Four of the 16 at-risk teens were misidentified as having low risk.

At the two-year follow up, none of the at-risk teens had developed bipolar disorder, but six were diagnosed with major depression or an anxiety disorder. Among all the at-risk teens identified through machine learning, these six had received the highest odds for belonging to the at-risk group.

Three of the four at-risk teens misidentified as belonging to the low risk group at the start of the study remained healthy at the second assessment. Clinical information for the fourth teen was not available at the time of follow-up.

Significance

Though still a very preliminary study, according to the researchers, machine learning combined with fMRI shows promise for predicting individual risk of developing future mental disorders, especially in at-risk populations.

The ongoing follow-up may also yield further insights into the relationship between depression, anxiety disorders, and bipolar disorder. Many studies have shown that bipolar disorder is often preceded by depression or anxiety disorders, and that these disorders may affect the course of subsequent bipolar disorder.

What’s Next

Larger studies using machine learning and fMRI will help to better define the extent to which pattern recognition techniques can accurately identify people at risk for future mental disorders. Research in this area may also inform early treatment or prevention efforts.

Reference

Mourão-Miranda J, Oliveira L, Ladouceur CD, Marquand A, Brammer M, Birmaher B, Axelson D, Phillips ML. Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents. PLoS One. 2012;7(2):e29482. Epub 2012 Feb 15. PubMed PMID: 22355302; PubMed Central PMCID: PMC3280237.

Related Funding: K01 MH083001-04; R01 MH060952-11

Brain Wiring a No-Brainer?

The brain appears to be wired more like the checkerboard streets of New York City than the curvy lanes of Columbia, Md., suggests a new brain imaging study. The most detailed images, to date, reveal a pervasive 3D grid structure with no diagonals, say scientists funded by the National Institutes of Health.

“Far from being just a tangle of wires, the brain’s connections turn out to be more like ribbon cables -- folding 2D sheets of parallel neuronal fibers that cross paths at right angles, like the warp and weft of a fabric,” explained Van Wedeen, M.D., of Massachusetts General Hospital (MGH), A.A. Martinos Center for Biomedical Imaging and the Harvard Medical School. “This grid structure is continuous and consistent at all scales and across humans and other primate species.”

Wedeen and colleagues report new evidence of the brain’s elegant simplicity March 30, 2012 in the journal Science. The study was funded, in part, by the NIH’s National Institute of Mental Health (NIMH), the Human Connectome Project of the NIH Blueprint for Neuroscience Research, and other NIH components.

“Getting a high resolution wiring diagram of our brains is a landmark in human neuroanatomy,” said NIMH Director Thomas R. Insel, M.D. “This new technology may reveal individual differences in brain connections that could aid diagnosis and treatment of brain disorders.”

Knowledge gained from the study helped shape design specifications for the most powerful brain scanner of its kind, which was installed at MGH’s Martinos Center last fall. The new Connectom diffusion magnetic resonance imaging (MRI) scanner can visualize the networks of crisscrossing fibers – by which different parts of the brain communicate with each other – in 10-fold higher detail than conventional scanners, said Wedeen.

“This one-of-a-kind instrument is bringing into sharper focus an astonishingly simple architecture that makes sense in light of how the brain grows,” he explained. “The wiring of the mature brain appears to mirror three primal pathways established in embryonic development.”

As the brain gets wired up in early development, its connections form along perpendicular pathways, running horizontally, vertically and transversely. This grid structure appears to guide connectivity like lane markers on a highway, which would limit options for growing nerve fibers to change direction during development. If they can turn in just four directions: left, right, up or down, this may enforce a more efficient, orderly way for the fibers to find their proper connections – and for the structure to adapt through evolution, suggest the researchers.

Obtaining detailed images of these pathways in human brain has long eluded researchers, in part, because the human cortex, or outer mantle, develops many folds, nooks and crannies that obscure the structure of its connections. Although studies using chemical tracers in neural tracts of animal brains yielded hints of a grid structure, such invasive techniques could not be used in humans.

Wedeen’s team is part of a Human Connectome Project Harvard/MGH-UCLA consortium that is optimizing MRI technology to more accurately to image the pathways. In diffusion imaging, the scanner detects movement of water inside the fibers to reveal their locations. A high resolution technique called diffusion spectrum imaging (DSI) makes it possible to see the different orientations of multiple fibers that cross at a single location – the key to seeing the grid structure ceus for social workers

In the current study, researchers performed DSI scans on postmortem brains of four types of monkeys – rhesus, owl, marmoset and galago – and in living humans. They saw the same 2D sheet structure containing parallel fibers crossing paths everywhere in all of the brains – even in local path neighborhoods. The grid structure of cortex pathways was continuous with those of lower brain structures, including memory and emotion centers. The more complex human and rhesus brains showed more differentiation between pathways than simpler species.

Among immediate implications, the findings suggest a simplifying framework for understanding the brain’s structure, pathways and connectivity.

The technology used in the current study was able to see only about 25 percent of the grid structure in human brain. It was only apparent in large central circuitry, not in outlying areas where the folding obscures it. But lessons learned were incorporated into the design of the newly installed Connectom scanner, which can see 75 percent of it, according to Wedeen.

Much as a telescope with a larger mirror or lens provides a clearer image, the new scanner markedly boosts resolving power by magnifying magnetic fields with magnetically stronger copper coils, called gradients. Gradients make it possible to vary the magnetic field and get a precise fix on locations in the brain. The Connectom scanner’s gradients are seven times stronger than those of conventional scanners. Scans that would have previously taken hours – and, thus would have been impractical with living human subjects – can now be performed in minutes.

“Before, we had just driving directions. Now, we have a map showing how all the highways and byways are interconnected,” said Wedeen. “Brain wiring is not like the wiring in your basement, where it just needs to connect the right endpoints. Rather, the grid is the language of the brain and wiring and re-wiring work by modifying it.”


Detail from DSI scan shows fabric-like 3D grid structure of connections in monkey brain.

Source: Van Wedeen, M.D., Martinos Center and Dept. of Radiology, Massachusetts General Hospital and Harvard University Medical School


Curvature in this DSI image of a whole human brain turns out to be folding of 2D sheets of parallel neuronal fibers that cross paths at right angles. This picture came from the new Connectom scanner.
Source: Van Wedeen, M.D., Martinos Center and Dept. of Radiology, Massachusetts General Hospital and Harvard University Medical School

Reference

Wedeen VJ, Rosene DL, Ruopeng W, Guangping D, Mortazavi F, Hagmann P, Kass JH, Tseng W-YI. The Geometric Structure of the Brain Fiber Pathways: A Continuous Orthogonal Grid. March 30, 2012 Science.

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The mission of the NIMH is to transform the understanding and treatment of mental illnesses through basic and clinical research, paving the way for prevention, recovery and cure. For more information, visit the NIMH website.

The NIH Blueprint for Neuroscience Research is a cooperative effort among the NIH Office of the Director and the 15 NIH Institutes and Centers that support research on the nervous system. By pooling resources and expertise, the Blueprint supports transformative neuroscience research, and the development of new tools, training opportunities, and other resources to assist neuroscientists.

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