The structural connectome in traumatic brain injury: A meta-analysis of graph metrics

NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS

卷:99

Review

Imms, Phoebe¹  Clemente, Adam¹  Cook, Mark⁴  D'Souza, Wendyl⁴  Wilson, Peter H.¹  Jones, Derek K.^1,2,3  Caeyenberghs, Karen¹ 

[1] Australian Catholic Univ, Fac Hlth Sci, Mary MacKillop Inst Heatlh Res, 115 Victoria Parade, Melbourne, Vic 3065, Australia

[2] Cardiff Univ, Sch Psychol & Neurosci, Brain Res Imaging Ctr, Maindy Rd, Cardiff CF24 4HQ, S Glam, Wales

[3] Cardiff Univ, Mental Hlth Res Inst, Maindy Rd, Cardiff CF24 4HQ, S Glam, Wales

[4] Univ Melbourne, St Vincents Hosp, Dept Med, 41 Victoria Parade, Melbourne, Vic 3065, Australia

关键词: Traumatic brain injury;    Graph theory;    Graph metrics;    Structural connectomics;    Network analysis;    Diffusion MRI;    Biomarkers;    Meta-analysis;    Systematic search;    Narrative review;   

DOI  :  10.1016/j.neubiorev.2019.01.002

来源: Elsevier

【 摘 要 】

Although recent structural connectivity studies of traumatic brain injury (TBI) have used graph theory to evaluate alterations in global integration and functional segregation, pooled analysis is needed to examine the robust patterns of change in graph metrics across studies. Following a systematic search, 15 studies met the inclusion criteria for review. Of these, ten studies were included in a random-effects meta-analysis of global graph metrics, and subgroup analyses examined the confounding effects of severity and time since injury. The meta-analysis revealed significantly higher values of normalised clustering coefficient (go =o1.445, CI = [0.512, 2.378], po = 60.002) and longer characteristic path length (go = o0.514, CI = [0.190, 0.838], po = o0.002) in TBI patients compared with healthy controls. Our findings suggest that the TBI structural network has shifted away from the balanced small-world network towards a regular lattice. Therefore, these graph metrics may be useful markers of neurocognitive dysfunction in TBI. We conclude that the pattern of change revealed by our analysis should be used to guide hypothesis-driven research into the role of graph metrics as diagnostic and prognostic biomarkers.

【 授权许可】

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