A structural network fingerprint of mild traumatic brain injury: a multi-study synthesis of T1-weighted MRI abnormalities

Background Mild traumatic brain injury (mTBI) often results in persistent cognitive and somatic deficits despite unremarkable routine neuroimaging. Evidence suggests mTBI affects large-scale neural systems rather than isolated regions, yet structural findings remain heterogeneous across studies. Objective This study synthesized T1-weighted MRI data into a unified structural network fingerprint (SNF) of mTBI. Methods We analyzed ten peer-reviewed studies identifying regional abnormalities in adult mTBI via voxel-based, volumetry, grey/white-matter probability mapping or tensor-based morphometry. Thirty-five significant regions of interest (ROIs) were extracted and mapped to a standardized anatomical atlas. ROIs were categorized into canonical networks, and we applied co-alteration graph modeling, principal component analysis (PCA), and hierarchical clustering to evaluate network-level convergence. Results The SNF identified a core triad of vulnerability: the default mode network (DMN), the limbic/memory system, and thalamic–callosal relay structures. Graph modeling revealed robust clustering among DMN–limbic–thalamic regions. Furthermore, PCA and hierarchical clustering demonstrated that structural alterations strictly align with intrinsic network boundaries, rather than appearing as stochastic damage. Conclusion mTBI exhibits a reproducible structural signature characterized by DMN and thalamo-limbic involvement. This SNF framework establishes a basis for clinically interpretable biomarkers and computable decision-support tools in concussion care.