News | Magnetic Resonance Imaging (MRI) | March 21, 2017

MRI Scans May Help Predict Which Children Will Recover Faster From Brain Injury

Diffusion-weighted MRI reveals large percentage of group with slow transfer time post-injury, which significantly impacted test scores

diffusion weighted MRI, children, traumatic brain injury, TBI recovery, Neurology journal study

March 21, 2017 — A new imaging biomarker may help predict which children will take longer to recover from a traumatic brain injury (TBI), according to a preliminary study published online in Neurology.

“Traumatic brain injury is a leading cause of disability in children, but it’s very difficult to predict long-term outcome and which kids might need more aggressive treatment,” said study author Emily L. Dennis, Ph.D., of the University of Southern California in Los Angeles. “While the severity of the injury certainly plays a role in this, there’s still a lot of uncertainty — you frequently have two patients with similar injuries who have different recoveries.”

The study involved 21 children ages eight to 18 that were in a pediatric intensive care unit at one of four hospitals in Los Angeles County with a moderate to severe traumatic brain injury. Causes of the injuries included falls from skateboards, scooters and bikes, motor vehicle-pedestrian accidents and motor vehicle accidents with children as passengers. The children were compared to 20 children of the same age who had not had a brain injury.

All of the participants were given special magnetic resonance imaging (MRI) scans, called diffusion-weighted MRI, about two to five months after the injury and again about a year later. They also took tests of thinking and memory skills. The kids also had electroencephalograms (EEGs) while they were completing a computerized pattern-matching task to look at how quickly information is transferred from one hemisphere of the brain to the other across the corpus callosum, which is a collection of white matter that connects the two halves of the brain. Previous studies have shown that both children and adults have slow transfer times right after a traumatic brain injury.

The study found that a few months after injury, half of the children with TBI had slow transfer time, while the other half were in the normal range and did not differ from the healthy kids.

The TBI-slow transfer time group also had disruptions to the white matter that got worse in the year between the first and second scans, while scans of the TBI-normal transfer time group showed no significant differences from the scans of the healthy kids. “The TBI-slow transfer time group showed progressive decline during this period, while the other group showed signs of recovery,” Dennis said.

In the tests of thinking and memory skills, the kids in the TBI-slow transfer time group had significantly worse scores than the healthy kids, while those in the TBI-normal transfer time group had scores between the two groups.

“The finding in this study that there is degeneration of white matter in about half of the children with moderate to severe TBI during the first 16 months after an injury should stimulate attempts to understand why this is happening so that treatments may be developed to lessen this progressive decline in white matter,” said Dennis. She noted that the study was small, and the results need to be confirmed with larger studies.

“This study is an important step forward to identifying a functional biomarker that may predict the trajectory of TBI recovery,” said Bradley L. Schlaggar, M.D., Ph.D., of Washington University School of Medicine in St. Louis, Mo., and a member of the American Academy of Neurology, who wrote an editorial accompanying the study. “Success in confirming these results would be transformative for the field. We need tools that will allow us to make individual predictions so we can make the best decisions about treatment and how to educate and counsel our patients and their families.”

The study was supported by the National Institutes of Health, UCLA Brain Injury Research Center, UCLA Steve Tisch BrainSPORT Program, Easton Foundation and UCLA Staglin IMHRO Center for Cognitive Neuroscience.

For more information: www.neurology.org

Related Content

Stereotactic Radiosurgery Effective for Pediatric Arteriovenous Malformation Patients
News | Radiation Therapy | April 19, 2019
Ching-Jen Chen, M.D., of the neurosurgery department at the University of Virginia (UVA) Health System, was the winner...
Video Plus Brochure Helps Patients Make Lung Cancer Scan Decision

Image courtesy of the American Thoracic Society

News | Lung Cancer | April 19, 2019
A short video describing the potential benefits and risks of low-dose computed tomography (CT) screening for lung...
Artificial Intelligence Performs As Well As Experienced Radiologists in Detecting Prostate Cancer
News | Artificial Intelligence | April 18, 2019
University of California Los Angeles (UCLA) researchers have developed a new artificial intelligence (AI) system to...
Surgically Guided Brachytherapy Improves Outcomes for Intracranial Neoplasms
News | Brachytherapy Systems | April 18, 2019
Peter Nakaji, M.D., FAANS, general practice neurosurgeon at Barrow Neurological Institute, presented new research on...
Check-Cap Initiates U.S. Pilot Study of C-Scan for Colorectal Cancer Screening
News | Colonoscopy Systems | April 15, 2019
Check-Cap Ltd. has initiated its U.S. pilot study of the C-Scan system for prevention of colorectal cancer through...
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used “Deep Learning,“ a form of machine learning, which is a type of artificial intelligence. Graphic courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Artificial Intelligence | April 12, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.
Gamma Knife radiosurgery has become the preferred radiation therapy option for patients with brain tumors at facilities like the Northwestern Medicine Cancer Center, pictured here

Gamma Knife radiosurgery has become the preferred radiation therapy option for patients with brain tumors at facilities like the Northwestern Medicine Cancer Center, pictured here. The technology is favored largely for its ability to precisely target tumors while sparing healthy tissue.

Feature | Radiation Oncology | April 11, 2019 | By Jeff Zagoudis
Brain tumors are some of the most complicated forms of cancer to treat due to their extremely sensitive location.
Deep Lens Closes Series A Financing for Digital AI Pathology Platform
News | Digital Pathology | April 09, 2019
Digital pathology company Deep Lens Inc. announced the closing of a $14 million Series A financing that will further...
Uterine Fibroid Embolization Safer and as Effective as Surgical Treatment
News | Interventional Radiology | April 05, 2019
Uterine fibroid embolization (UFE) effectively treats uterine fibroids with fewer post-procedure complications compared...
Videos | RSNA | April 03, 2019
ITN Editor Dave Fornell takes a tour of some of the most interesting new medical imaging technologies displa