Exploring personalized structural connectomics for moderate to severe traumatic brain injury

被引:6
|
作者
Imms P. [1 ]
Clemente A. [2 ]
Deutscher E. [3 ]
Radwan A.M. [4 ]
Akhlaghi H. [5 ]
Beech P. [6 ]
Wilson P.H. [2 ]
Irimia A. [1 ,7 ,8 ]
Poudel G. [9 ]
Domínguez Duque J.F. [3 ]
Caeyenberghs K. [3 ]
机构
[1] Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA
[2] Healthy Brain and Mind Research Centre, School of Behavioural, Health, and Human Sciences, Faculty of Health Sciences, Australian Catholic University, Fitzroy, VIC
[3] Cognitive Neuroscience Unit, School of Psychology, Faculty of Health, Deakin University, Burwood, VIC
[4] KU Leuven, Department of Imaging and Pathology, Translational MRI, ,, Leuven
[5] Emergency Department, St. Vincent’s Hospital (Melbourne), Faculty of Health, Deakin University, Melbourne, VIC
[6] Department of Radiology and Nuclear Medicine, The Alfred Hospital, Melbourne, VIC
[7] Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA
[8] Department of Quantitative and Computational Biology, Dana and David Dornsife College of Arts and Sciences, University of Southern California, Los Angeles, CA
[9] Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC
基金
美国国家卫生研究院; 英国医学研究理事会;
关键词
Graph theory; Lesion filling; Personalized connectomics; Personalized medicine; Structural connectomics; Traumatic brain injury;
D O I
10.1162/netn_a_00277
中图分类号
学科分类号
摘要
Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated individualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N = 12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscienceguided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome. © 2022 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
引用
收藏
页码:160 / 183
页数:23
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