Exploring personalized structural connectomics for moderate to severe traumatic brain injury

• Published in: Network neuroscience (Cambridge, Mass.) Vol. 7; no. 1; pp. 160 - 183
• Main Authors: Imms, Phoebe, Clemente, Adam, Deutscher, Evelyn, Radwan, Ahmed M., Akhlaghi, Hamed, Beech, Paul, Wilson, Peter H., Irimia, Andrei, Poudel, Govinda, Domínguez Duque, Juan F., Caeyenberghs, Karen
• Format: Journal Article
• Language: English
• Published: One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA MIT Press 01-01-2023; MIT Press Journals, The; The MIT Press
• Subjects: Brain damage; Customization; Damage assessment; Graph theory; Head injuries; Heterogeneity; Image segmentation; Injury analysis; Lesion filling; Lesions; Magnetic resonance imaging; Network analysis; Neuroimaging; Patients; Personalized connectomics; Personalized medicine; Rehabilitation; Structural connectomics; Traumatic brain injury; Graph theory; Traumatic brain injury; Lesion filling; Structural connectomics; Personalized connectomics; Personalized medicine
• ISSN: 2472-1751; 2472-1751
• DOI: 10.1162/netn_a_00277

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 (
= 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.
Single-subject profiling captures heterogeneity of the structural connectome to characterize brain network alterations in patients with traumatic brain injury (TBI). We profile individual patients based on their unique brain graphs in comparison to healthy controls, to provide insights into brain network disruption otherwise obscured by group-level approaches. Our implementation extends current methods by analyzing TBI structural profiles when automatic sub/cortical segmentation or parcellation of MRIs fail in the presence of lesions. Our approach highlights the translational potential for single-subject network analyses in the study of brain injury. Personalized connectome profiling provides a novel user-friendly framework for leveraging graph metrics to benefit the individual patient, by characterizing network-level brain alterations with potential prognostic relevance.