Changes in event-related potential functional networks predict traumatic brain injury in piglets

Abstract

Background: Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. None of the current diagnostic tools, such as quantitative cognitive and balance tests, have been validated to identify mild traumatic brain injury in infants, adults and animals. In this preliminary study, we report a novel, quantitative tool that has the potential to quickly and reliably diagnose traumatic brain injury and which can track the state of the brain during recovery across multiple ages and species.

Methods: Using 32 scalp electrodes, we recorded involuntary auditory event-related potentials from 22 awake four-week-old piglets one day before and one, four, and seven days after two different injury types (diffuse and focal) or sham. From these recordings, we generated event-related potential functional networks and assessed whether the patterns of the observed changes in these networks could distinguish brain-injured piglets from non-injured.

Findings: Piglet brains exhibited significant changes after injury, as evaluated by five network metrics. The injury prediction algorithm developed from our analysis of the changes in the event-related potentials functional networks ultimately produced a tool with 82% predictive accuracy.

Interpretation: This novel approach is the first application of auditory event-related potential functional networks to the prediction of traumatic brain injury. The resulting tool is a robust, objective and predictive method that offers promise for detecting mild traumatic brain injury, in particular because collecting event-related potentials data is noninvasive and inexpensive.

Keywords: Auditory; Event-related; Functional; Networks; Pediatric; Traumatic brain injury.

Figure 1

Schematic describing auditory ERP acquisition and functional network construction methods.

Figure 2

Median N40 latencies for channel 15 (left auditory cortex) and P60 amplitudes for channel 19 (parietal lobe midline) in Sham and SAG groups from the Standard paradigm.

Figure 3

Changes in nodal strength with network density for all injury groups and study dates compared to Sham from Standard paradigm. Mean values and standard error bars are shown. Horizontal bars indicate significant difference of injury groups from sham at single density level using two-tailed permutation test with false detection rate correction for multiple comparisons, p<0.05.

Figure 4

Changes in global efficiency with network density for all injury groups and study dates compared to Sham from Standard paradigm. Mean values and standard error bars are shown. Horizontal bars indicate signficant difference of injury groups from sham at density level using two-tailed permutation test with false detection rate correction for multiple comparisons, p<0.05.

Figure 5

Mean and standard error bar charts of injury probabilty scores (InjScores) for all paradigms from xDawn/xDawnCov machine learning models. Black horizontal lines represent significant Wilcoxon ranksum test comparison with Sham/Pre-injury, p<0.05. The red, dotted line represents the threshold of InjScore determined from ROC analysis.