Summary: According to researchers, a new neuroimaging system, called fcMRI, can detect subtle differences in how individual brains are wired. The new system may help to distinguish between those at risk of developing neurological and psychiatric disorders, as well as track the progression of diseases.
Source: WUSTL.
There are no laboratory tests to diagnose migraines, depression, bipolar disorder and many other ailments of the brain. Doctors typically gauge such illnesses based on self-reported symptoms and behavior.
Now, a new study shows that a kind of brain scan called functional connectivity MRI (fcMRI) – which shows how brain regions interact – can reliably detect fundamental differences in how individual brains are wired. As such, the technique potentially could be used to distinguish healthy people from people with brain diseases or disorders, and provide insight into variations in cognitive ability and personality traits.
The findings are published April 18 in Neuron.
“This is a step toward realizing the clinical promise of functional connectivity MRI,” said senior author Steven Petersen, PhD, the James S. McDonnell Professor of Cognitive Neuroscience in Neurology and a professor of neurosurgery, of biomedical engineering, of psychological and brain sciences, and of radiology. “Before we can develop diagnostic tests based on fcMRI, we need to know what it is actually measuring. We show here that it’s not measuring what you’re thinking, but how your brain is organized. That opens the door to an entire new field of clinical testing.”
Petersen, postdoctoral researcher and first author Caterina Gratton, PhD, and colleagues analyzed a set of data collected by the Midnight Scan Club, a group of Washington University scientists who took turns undergoing myriad scans in an MRI machine late at night, when the demand for such machines and, consequently, the usage fees tend to be low.
The researchers analyzed data from more than 10 hours of fcMRI scans on each of nine people, collected in 10 separate one-hour sessions for each person. During the scans, each person performed tasks related to vision, memory, reading or motor skills, or rested quietly.
Functional MRI scans generate a dynamic map of the outer surface of the brain, showing changing hot spots of activity over time. To create a functional connectivity map, Gratton divided the brain’s surface into 333 regions and identified areas that became active and inactive in unison. She then constructed brain network maps for each individual, showing patterns of correlation between parts of the brain.
The sheer quantity of data available on each person allowed her to analyze how much an individual’s brain networks changed from day to day and with different mental tasks.
The answer? Not much.
“Brain networks captured by fcMRI are really about the individual,” Gratton said. “Whether someone’s watching a movie or thinking about her breakfast or moving her hands makes only a small difference. You can still identify that individual by her brain networks with a glance.”
The consistency of the fcMRI scans makes them a promising diagnostic tool. Although the technique’s potential to identify brain disorders and diseases was noted years ago, fcMRI-based diagnostic tests have yet to make their way into doctors’ offices. Progress has been stymied by confusion over whether the scans reflect fundamental, stable features of the brain, or if they change with every passing thought.
Further, the researchers found that the technique was powerful enough to distinguish people who were extraordinarily alike. All of the scanned brains belonged to young, healthy scientists and doctors.
“We need more data before we can know what is normal variation in the population at large,” Gratton said. “But the individual differences were really easy to pick up, even in a population that is really very similar. It’s exciting to think that these individual differences may be related to personality, cognitive ability, or psychiatric or neurological disease. Thanks to this work, we know we have a reliable tool to study these possibilities.”
Funding: Study funded by National Institutes of Health, Jacobs Foundation, Child Neurology Foundation, McDonnell Center for Systems Neuroscience, Mallinckrodt Institute of Radiology, Hope Center for Neurological Disorders, American Psychological Association, Dart Neuroscience.
Source: Judy Martin Finch – WUSTL
Publisher: Organized by NeuroscienceNews.com.
Image Source: NeuroscienceNews.com image is credited to Caterina Gratton.
Original Research: Abstract for “Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation” by Caterina Gratton, Timothy O. Laumann, Ashley N. Nielsen, Deanna J. Greene, Evan M. Gordon, Adrian W. Gilmore, Steven M. Nelson, Rebecca S. Coalson, Abraham Z. Snyder, Bradley L. Schlaggar, Nico U.F. Dosenbach, and Steven E. Petersen in Neuron. Published April 18 2018.
doi:10.1016/j.neuron.2018.03.035
[cbtabs][cbtab title=”MLA”]WUSTL “Brain Scans May Help Diagnose Neurological and Psychiatric Disorders.” NeuroscienceNews. NeuroscienceNews, 18 April 2018.
<https://neurosciencenews.com/neurology-neuroimaging-tracking-8833/>.[/cbtab][cbtab title=”APA”]WUSTL (2018, April 18). Brain Scans May Help Diagnose Neurological and Psychiatric Disorders. NeuroscienceNews. Retrieved April 18, 2018 from https://neurosciencenews.com/neurology-neuroimaging-tracking-8833/[/cbtab][cbtab title=”Chicago”]WUSTL “Brain Scans May Help Diagnose Neurological and Psychiatric Disorders.” https://neurosciencenews.com/neurology-neuroimaging-tracking-8833/ (accessed April 18, 2018).[/cbtab][/cbtabs]
Abstract
Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation
Highlights
•Functional networks are dominated by common group and stable individual features
•Task state only modestly influences brain networks, largely varying by individual
•With substantial data, day-to-day variability is minimal
•Variance sources show distinct topography and links to intrinsic and evoked factors
Summary
The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine.
