News | Neuro Imaging | October 18, 2017

MRI May Predict Neurological Outcomes for Cardiac Arrest Survivors

Patients showing greater connection between neural networks had greater potential for positive outcomes; findings could help guide future treatment

MRI May Predict Neurological Outcomes for Cardiac Arrest Survivors

October 18, 2017 — Magnetic resonance imaging (MRI)-based measurements of the functional connections in the brain can help predict long-term recovery in patients who suffer neurological disability after cardiac arrest, according to a clinical study appearing online in the journal Radiology.

Cardiac arrest, or abrupt loss in heart function, is a common and often deadly occurrence that affects hundreds of thousands of people every year in the United States alone, according to the American Heart Association. Many patients who survive end up with severe neurological disabilities, as the temporary loss of oxygenated blood flow to the brain can result in widespread neuronal cell death.

“Current methods to predict future levels of function for these survivors have limited accuracy,” said study lead author Robert D. Stevens, M.D., from Johns Hopkins University School of Medicine in Baltimore. “We need better methods to help clinicians understand the magnitude of these injuries and make more accurate predictions on recovery, thereby enabling more informed decision-making.”

For the study, Stevens and colleagues used advanced MRI neuro imaging techniques like diffusion tensor imaging and resting-state functional MRI (fMRI) to focus on the brain’s large-scale functional integration. This “network of networks,” or connectome, represents the ensemble of different neuronal populations in the brain that work together to perform tasks.

The researchers assessed the brain’s functional connectivity in 46 patients who were in a coma following cardiac arrest. The imaging, performed within two weeks of cardiac arrest, included studies of brain structure and function. Functional imaging focused on four well-characterized networks in the brain, including the default mode network, which is active when a person is not engaged in a specific task, and the salience network, a collection of brain regions that select which stimuli are deserving of our attention.

One year after the patients’ cardiac arrests, the researchers assessed the patients with the Cerebral Performance Category Scale, a commonly used measure of neurological function following cardiac arrest. Eleven patients had favorable outcomes. Functional connectivity was stronger in those who achieved higher levels of independence at one year compared with those who were heavily dependent. The changes in functional connectivity between networks predicted outcomes with greater accuracy than any of the MRI structural measures tested.

“This is game-changing information about what happens in the brains of people who suffer cardiac arrest,” Stevens said. “We realize that network architectures can be selectively disrupted in this setting.”

A key predictor of outcomes was the interaction between the brain’s default mode and salience networks. These two networks are normally anti-correlated, meaning that as the default mode network becomes more active, activity is reduced in the salience network, and vice versa. When researchers compared the brain imaging results of patients who had favorable outcomes with those who did not, they noticed a stark difference.

“Anti-correlation was preserved in patients who recovered and abolished in those who did not,” Stevens said. “Relative preservation of this anti-correlation was the most robust signal of a favorable outcome.”

The results indicate that connectivity measures could be early markers of long-term recovery potential in patients with cardiac arrest-related brain damage, the researchers said.

While researchers don’t expect connectome analysis with MRI to be the single “magic bullet” solution to predicting outcomes, it could increase the confidence that clinicians have in communicating with patients’ families in the wake of cardiac arrest. Additionally, fMRI could aid in the development of therapeutic interventions for neurologically disabled patients.

“Connectome studies have the potential to change not only outcome prediction, but to guide treatment as well,” Stevens said.

For more information: www.pubs.rsna.org/journal/radiology

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