Summary: A new study reports using MEG neuroimaging technology in conjunction with machine learning algorithms could help to determine whether a person is suffering from concussion.
New machine learning approach uses brain scans to accurately detect mild traumatic brain injury.
High-resolution brain scans analyzed by machine learning algorithms could determine whether a patient has a concussion, according to a new study published in PLOS Computational Biology.
Currently, doctors diagnose concussions according to patient-reported symptoms, which can be challenging and inefficient. Previous studies have used high-resolution imaging to show that concussions cause changes in communication between different brain areas. However, these studies have typically only looked at average changes across groups of patients.
Vasily Vakorin, now of Simon Fraser University, British Columbia, and colleagues (from the Hospital for Sick Children, Toronto and Defense Research and Development Canada) investigated whether high-resolution imaging could be combined with machine learning algorithms to detect concussions in the brains of individual patients.
The researchers scanned the brains of men with and without concussion using magnetoencephalography (MEG), which records brain activity at fast time scales. MEG imaging showed that patients with concussions had distinctive changes in communication among areas of their brains.
Then, by employing machine learning algorithms, the scientists were able to use individual brain scans to work backwards and predict whether a given patient had a concussion or not. They were able to detect concussions with 88% accuracy. This approach also accurately predicted the severity of symptoms reported by individual patients.
“Changes in communication between brain areas, as detected by magnetoencephalography, allowed us to detect concussion from individual scans, in situations wherein typical clinical imaging tools such as MRI or CT fail,” says study coauthor Sam Doesburg.
Future research could refine understanding of the specific neural changes associated with concussions in order to improve detection, inform treatment, and monitor recovery.
Funding: This work was supported by the Canadian Forces Health Services and funding from Defence Research and Development Canada (DRDC) (contract # W7719-135182/001/TOR) to MJT and EWP. The funders had no role in study.
Competing Interests: The authors have declared that no competing interests exist.
Source: Sam Doesburg – PLOS
Image Source: NeuroscienceNews.com image is credited to Vakorin et al.
Original Research: Full open access research for “Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity” by Vasily A. Vakorin, Sam M. Doesburg, Leodante da Costa, Rakesh Jetly, Elizabeth W. Pang, and Margot J. Taylor in PLOS Computational Biology. December 1 2016 doi:10.1371/journal.pcbi.1004914
Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity
Accurate means to detect mild traumatic brain injury (mTBI) using objective and quantitative measures remain elusive. Conventional imaging typically detects no abnormalities despite post-concussive symptoms. In the present study, we recorded resting state magnetoencephalograms (MEG) from adults with mTBI and controls. Atlas-guided reconstruction of resting state activity was performed for 90 cortical and subcortical regions, and calculation of inter-regional oscillatory phase synchrony at various frequencies was performed. We demonstrate that mTBI is associated with reduced network connectivity in the delta and gamma frequency range (>30 Hz), together with increased connectivity in the slower alpha band (8–12 Hz). A similar temporal pattern was associated with correlations between network connectivity and the length of time between the injury and the MEG scan. Using such resting state MEG network synchrony we were able to detect mTBI with 88% accuracy. Classification confidence was also correlated with clinical symptom severity scores. These results provide the first evidence that imaging of MEG network connectivity, in combination with machine learning, has the potential to accurately detect and determine the severity of mTBI.
“Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity” by Vasily A. Vakorin, Sam M. Doesburg, Leodante da Costa, Rakesh Jetly, Elizabeth W. Pang, and Margot J. Taylor in PLOS Computational Biology. December 1 2016 doi:10.1371/journal.pcbi.1004914