A structural network fingerprint of mild traumatic brain injury: a multi-study synthesis of T1-weighted MRI abnormalities.
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All Authors
Mavroudis, I.
Petridis, F.
Ciobica, AS.
Cojocariu, RO.
Kazis, D.
Kamar, AAM.
Ionescu, C.
Gheban, D.
Morosan, C.
Gurzu, B.
LTHT Author
Mavroudis, Ioannis
LTHT Department
Neurosciences
Neurology
Neurology
Non Medic
Publication Date
2026
Item Type
Journal Article
Systematic Review
Language
Subject
MAGNETIC RESONANCE IMAGING , BRAIN , MODELS, STATISTICAL , BRAIN INJURIES, TRAUMATIC , NERVE NET
Subject Headings
Abstract
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.
Journal
Frontiers in Human Neuroscience