No abstract

Mild traumatic brain injury (mTBI) leads to long-term cognitive sequelae in a significant portion of patients. Disruption of normal neural communication across functional brain networks may explain the deficits in memory and attention observed after mTBI. In this study, we used magnetoencephalography (MEG) to examine functional connectivity during a resting state in a group of mTBI subjects (n = 9) compared with age-matched control subjects (n = 15). We adopted a data-driven, exploratory analysis in source space using phase locking value across different frequency bands. We observed a significant reduction in functional connectivity in band-specific networks in mTBI compared with control subjects. These networks spanned multiple cortical regions involved in the default mode network (DMN). The DMN is thought to subserve memory and attention during periods when an individual is not engaged in a specific task, and its disruption may lead to cognitive deficits after mTBI. We further applied graph theoretical analysis on the functional connectivity matrices. Our data suggest reduced local efficiency in different brain regions in mTBI patients. In conclusion, MEG can be a potential tool to investigate and detect network alterations in patients with mTBI. The value of MEG to reveal potential neurophysiological biomarkers for mTBI patients warrants further exploration.

We have successfully developed a real-time, continuous physiologic data acquisition system that can capture, store, and archive data from pediatric intensive care unit patients for subsequent time series analysis of dynamic changes in physiologic state. The physiologic signal database generated from this system is available for analysis of dynamic changes caused by critical illness and injury.