Connectomic assessment of injury burden and longitudinal structural network alterations in moderate‐to‐severe traumatic brain injury. Issue 13 (29th April 2022)

• Record Type: Journal Article
• Authors: Osmanlıoğlu, Yusuf; Parker, Drew; Alappatt, Jacob A.; Gugger, James J.; Diaz‐Arrastia, Ramon R.; Whyte, John; Kim, Junghoon J.; Verma, Ragini
• Is Part Of: Human brain mapping. Volume 43:Issue 13(2022)
• Page Start: 3944
• Page End: 3957
• Publication Date: 2022-04-29
• Subjects: connectivity disruption -- connectomes -- diffusion MRI -- injury burden -- traumatic brain injury; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25894
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Traumatic brain injury (TBI) is a major public health problem. Caused by external mechanical forces, a major characteristic of TBI is the shearing of axons across the white matter, which causes structural connectivity disruptions between brain regions. This diffuse injury leads to cognitive deficits, frequently requiring rehabilitation. Heterogeneity is another characteristic of TBI as severity and cognitive sequelae of the disease have a wide variation across patients, posing a big challenge for treatment. Thus, measures assessing network‐wide structural connectivity disruptions in TBI are necessary to quantify injury burden of individuals, which would help in achieving personalized treatment, patient monitoring, and rehabilitation planning. Despite TBI being a disconnectivity syndrome, connectomic assessment of structural disconnectivity has been relatively limited. In this study, we propose a novel connectomic measure that we call network normality score (NNS) to capture the integrity of structural connectivity in TBI patients by leveraging two major characteristics of the disease: diffuseness of axonal injury and heterogeneity of the disease. Over a longitudinal cohort of moderate‐to‐severe TBI patients, we demonstrate that structural network topology of patients is more heterogeneous and significantly different than that of healthy controls at 3 months postinjury, where dissimilarity further increases up to 12 months. We also show that NNS captures injury burden as quantified by posttraumatic amnesia and that alterations in the structural brain network is not related to cognitive recovery. Finally, we compare NNS to major graph theory measures used in TBI literature and demonstrate the superiority of NNS in characterizing the disease. Abstract : In this study, we propose a novel normative graph theoretical measure to assess injury burden in moderate‐to‐severe traumatic brain injury patients, which captures properties of the disease that are not well captured by standard graph theory measures. We demonstrate the utility of our measure by showing its relationship with cognitive measures. We also investigated the longitudinal change in cognitive measures as well as proposed measure and demonstrated an inverse relationship between the two, potentially indicating the network level effects of neural degeneration in TBI patients over time.