Back to Search Start Over

Exploring personalized structural connectomics for moderate to severe traumatic brain injury.

Authors :
Imms P
Clemente A
Deutscher E
Radwan AM
Akhlaghi H
Beech P
Wilson PH
Irimia A
Poudel G
Domínguez Duque JF
Caeyenberghs K
Source :
Network neuroscience (Cambridge, Mass.) [Netw Neurosci] 2023 Jan 01; Vol. 7 (1), pp. 160-183. Date of Electronic Publication: 2023 Jan 01 (Print Publication: 2023).
Publication Year :
2023

Abstract

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 neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome.<br /> (© 2022 Massachusetts Institute of Technology.)

Details

Language :
English
ISSN :
2472-1751
Volume :
7
Issue :
1
Database :
MEDLINE
Journal :
Network neuroscience (Cambridge, Mass.)
Publication Type :
Academic Journal
Accession number :
37334004
Full Text :
https://doi.org/10.1162/netn_a_00277