summary: Researchers are working on vast datasets to unravel the mysteries of the brain. The research team used new software to analyze the MRIs of 300 infants and found that white matter myelin growth was slow after birth.
This finding provides insight into early childhood brain development and the potential effects of preterm birth. Using neuroinformatics, researchers can now explore datasets built from thousands of individuals and gain deep insights into brain health and development.
Important facts:
- The study used new software to analyze MRIs of 300 infants and showed that white matter myelin develops slowly after birth.
- Abnormal myelin development is associated with various developmental and mental health disorders, such as chronic depression and schizophrenia.
- Although both birth and gestational age at the time of scanning influence myelin development, birth causes a significant slowing.
sauce: University of Washington
A single brain is immeasurably complex. So whether brain researchers are looking at a dataset constructed from 300,000 neurons in 81 mice or from MRIs of 1,200 young people, brain researchers are currently Because we’re dealing with so much information, we also have to come up with new ways to make sense of it. Developing new analytical tools has become as important as using them to understand brain health and development.
A team including researchers from the University of Washington recently compared MRIs of 300 infants using new software and found that postnatal development of myelin, a part of the brain’s so-called white matter, was much slower. . The researchers published their findings on August 7th. Proceedings of the National Academy of Sciences.
Wisconsin News spoke with senior author Ariel Rochem, a research associate professor in the University of Wisconsin Department of Psychology and a data science researcher in the eScience Institute, about the paper and his research approach.
What topics do you research and how?
Ariel Rochem: My group works in neuroinformatics, with a focus on building methods and software to analyze neuroscience data. We specifically focus on her MRI measurements of the human brain. The brain consists of a large network of connections between different areas. Inside our brains, there is a large bundle of connections called white matter, which contains many axons. Axons are long, branching parts of neurons that can communicate with each other over considerable distances.
So we use MRI to find these bundles in all of our study subjects and understand the organization within these bundles. From this, we can understand the differences between people who suffer from a certain disease and those who do not, as well as differences in development and cognitive ability.
How is this approach different from the way brain research has historically been practiced?
AR:For many years, researchers collected data by taking subjects to local hospitals and MRI centers. And people still do this. In fact, his new UW Human Neuroscience Center, which I’m part of, has his one of these scanners. However, more recent approaches require collecting much larger amounts of data.
For example, it would be difficult for anyone here at the University of Wisconsin division to collect data from more than 1,000 individuals. But a few years ago, the National Institutes of Health funded the so-called Human Connectome Project to do just that. So we got a sample of 1,200 healthy adults and collected a fairly large amount of data on each of them. Neuroinformatics involves acquiring these types of datasets and developing tools to study them.
What kind of discoveries have these methods brought about in brain science?
AR: Our recent paper is a good example. Our team used a large, publicly available dataset from the Developing Human Connectome Project, which collects data from newborns during their first few days of life. We were looking at how white matter develops in scans of more than 300 babies.
My co-researcher and first author, Mareike Grothea of Philipps University Marburg, had previously developed software to find white matter bundles in adults and adapted it to apply to infant brains. In this study, we scaled up our approach using cloud computing. We were looking at how myelin, the fatty sheath that insulates axons, grows within white matter.
Other studies have shown that abnormal myelin development is associated with a number of developmental and mental health disorders, from chronic depression to schizophrenia. However, until this study was conducted, it was still unknown how myelin development changes depending on birth.
I had several hypotheses that I wanted to test. One is that it doesn’t matter when exactly you were born. It is important to know how much time has passed between becoming pregnant and taking the test. Another was that it only mattered how long after conception the baby was born, not how long after birth the baby was scanned. And then there was his third hypothesis, that both of these were important. That is how long the baby was gestated in the mother’s womb and the time that has elapsed from birth to the time of the scan.
So we were comparing scans of babies born at different gestational ages, from very early preterm births to babies born weeks after 40 weeks at term. Because we were able to work with this large data set, we were able to actually graph how a baby’s brain changes in the first days and weeks of life.
The data support that both gestational age at birth and gestational age at scan are important, but we found that there is an inflection point shortly after birth. At that moment, the development of these bundles we were considering will be dramatically slowed down. This is a basic fact, but we didn’t know it until now. This was discovered by examining publicly available data.
This has implications for our fundamental understanding of brain development during early childhood and how we can reduce the negative effects of preterm birth. Perhaps, for example, creating a “womb-like” environment after birth could offset this developmental delay and give premature babies more time to develop their brains.
What would you like to investigate using these methods in the future?
AR: We are starting to ask questions about brain connections related to autism spectrum disorders and schizophrenia. We are also currently participating in the ACT study (Adult Thinking Change Study) at California State University.
The effort has been underway for nearly 30 years to track a large, aging population in the Seattle area. A recent round of that study added MRI measurements. We are developing a method to reason about white matter bundles in aging people.
Other co-authors on the paper include David Bloom, a former post-baccalaureate student in the Department of Psychology; John Kruper, PhD student in the University’s Department of Psychology. Adam Ritchie Halford, former postdoctoral fellow in the Department of Psychology at the University of California. Stephanie Zika and Vicente A. Aguilera González of Philipps University Marburg; Jason D. Yetman and Karanit Grill Spector of Stanford University; The study was funded by the National Institute of Mental Health and the National Eye Institute.
About this neurodevelopmental research news
author: Stephen Milne
sauce: University of Washington
contact: Stephen Milne – University of Washington
image: Image credited to Neuroscience News
Original research: Closed access.
“Human white matter myelinates faster in the uterus than outside the uterus” written by Ariel Rochem et al. PNAS
abstract
Human white matter myelinates faster in the uterus than outside the uterus
The formation of myelin, the fatty sheath that insulates nerve fibers, is important for healthy brain function. A fundamental unanswered question is how birth affects myelin growth. To deal with this, a large (n = 300) A cross-sectional sample of newborns from the Developing Human Connectome Project (dHCP).
First, we developed software that automatically identifies 20 white matter bundles in individual newborns, which is suitable for large samples. We then fit a linear model that quantifies how T1w/T2w (myelin-sensitive image contrast) changes over time at each point along the bundle. We found that along the length of all bundles, he T1w/T2w grows faster before birth than immediately after birth.
In addition, another longitudinal sample of preterm infants (N = 34), and were found to have lower T1w/T2w than their full-term peers measured at the same age. Applying a linear model fitted to a cross-sectional sample to a longitudinal sample of preterm infants shows that the delay in her T1w/T2w growth in preterm infants is well explained by the time spent in utero and extrauterine development. got it.
These results suggest that white matter myelinates faster in the uterus than outside the uterus. Reduced postnatal myelin growth rate may explain the reduced myelin content in people born preterm and explain the long-term cognitive, neurological, and developmental effects of preterm birth.
By closely matching the environment of infants born prematurely to the environment they would have experienced in utero, we have shown that delays in myelin growth are reduced and, as a result, developmental outcomes are improved. We hypothesize that there is a possibility that
