Structural Brain Connectivity Correlates with Outcome in Mild Traumatic Brain Injury

Area of Science:

• Neuroscience
• Medical Imaging
• Network Science

Background:

• Mild traumatic brain injury (mTBI) is a leading cause of long-term disability.
• Understanding the neurobiological underpinnings of mTBI recovery is crucial for developing effective treatments.
• Brain connectivity networks are increasingly recognized as important in neurological function and dysfunction.

Purpose of the Study:

• To investigate the topology of structural brain connectivity networks after mild traumatic brain injury (mTBI).
• To determine the association between brain network properties and clinical outcomes in mTBI patients.
• To explore the potential of network properties as prognostic biomarkers for mTBI.

Main Methods:

• Structural brain connectivity networks were reconstructed using diffusion MRI and constrained spherical deconvolution-based probabilistic streamlines tractography.
• Cortical and subcortical gray matter were parcellated into 84 regions.
• Graph theoretical methods were used to analyze network properties, including global and local measures, in 85 mTBI patients and 30 controls at acute/subacute and chronic stages.

Main Results:

• No significant global differences in network properties were observed between mTBI patients and controls at either time point.
• Increased betweenness centrality in the right pars opercularis was found in mTBI patients during the chronic stage compared to controls.
• Both global (higher normalized global efficiency, degree, strength; lower small-worldness) and local network properties correlated significantly with clinical outcome, with prominent associations in the left putamen and left postcentral gyrus.

Conclusions:

• Global and local structural brain network properties are altered after mTBI and correlate with patient outcomes.
• These network properties show promise as early prognostic biomarkers for mild traumatic brain injury.
• The findings highlight the utility of network analysis in understanding mTBI pathophysiology and predicting recovery.