Connectomic assessment of injury burden and longitudinal structural network alterations in moderate‐to‐severe traumatic brain injury

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.

Graph‐matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score

“…While this paper is the first to apply graph matching to investigate the similarities of FCs between individuals, previous work has used graph matching to measure similarity between FC and SC and individuals' SCs. One such work in individuals with traumatic brain injury (TBI) used graph matching to capture SC network integrity after injury and in recovery; specifically they showed that individuals with TBI have more heterogeneity in their SCs than healthy controls, and this heterogeneity increases over time and is related to cognitive scores (Osmanlıŏlu et al, 2022 ). Graph matching has also been used in the past to map SC and FC to one another within individuals; that study found highest structure–function alignment in visual and somatomotor networks and lowest alignment in association regions (Osmanlioğlu et al, 2019 ).…”

A graph-matching based metric of functional connectome distance between pairs of individuals varies with their ages, cognitive performances and familial relationships

“…While this paper is the first to apply graph matching to investigate the similarities of FCs between individuals, previous work has used graph matching to measure similarity between FC and SC and individuals' SCs. One such work in individuals with traumatic brain injury (TBI) used graph matching to capture SC network integrity after injury and in recovery; specifically they showed that individuals with TBI have more heterogeneity in their SCs than healthy controls, and this heterogeneity increases over time and is related to cognitive scores 65 . Graph matching has also been used in the past to map SC and FC to one another; that study found highest structure-function alignment in visual and somatomotor networks and lowest alignment in association regions 43 , which reflects our current results showing least inter-subject FC alignment for association and most for sensory-motor networks.…”