Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Abstract

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.

Diffusion-Derived Magnetic Resonance Imaging Measures of Longitudinal Microstructural Remodeling Induced by Marrow Stromal Cell Therapy after Traumatic Brain Injury

“…14,20,26,56,57 In agreement with these findings, our dynamic image data showed that FA in particular brain areas spontaneously increased after TBI, which was accompanied with an improvement of functional recovery as measured by mNSS. In addition, similarly to some sites in the brain that are particularly vulnerable to diffuse axonal injury following TBI, 27,58 there seem to be brain regions that are more prone to structural remodeling. For the animal model with severe TBI in the current study, an apparent structural reorganization following the injury is found in the lesion boundary regions, as revealed by both histological and imaging investigations.…”

“…23 Because of its ability to capture the alteration of WM integrity, FA is widely used in experimental study 20,24,25 and clinical research 16,21,26 to quantify the extent of structural pathology or therapeutic benefit following brain insult. Although DTI holds value as an excellent imaging modality for identifying axonal injury as well as neuroplasticity that occur in the injured brain 18,27,28 it has a well-known inherent limitation, stemming from the constraints of the tensor model, which prevent DTI from adequately resolving intravoxel fiber crossing. 29 This significant shortcoming of DTI has motivated the introduction of alternative model-independent methods that directly use diffusion-weighted imaging for accurately characterizing anisotropic diffusion in complex neural tissue and for precisely detecting structural changes in response to injury and/or treatment.…”

Cerebral plasticity and connectopathies: mechanisms of comorbidity of neurological diseases and depression

“…Нарушение функ-циональных связей может быть связано с потерей нейро-нов или непрямыми эффектами в отдаленных областях мозга -диашизом [10]. Не удивительно, что структурный и межрегиональный коннектомный нейроимиджинг про-демонстрировал высокий клинический потенциал и при травматическом повреждении мозга [11,12].…”

“…В последние годы подтверждается предположение о том, что многие психические заболевания связаны с абер-рантыми связями в мозге и в этом случае коннектомика становится перспективной методологией для описания паттерна структурных и функциональных связей в мозге обзоры Журнал неврологии и психиатрии, 11,2016 биолоГичеСкие оСновы коморбиДноСти невролоГичеСких и пСихичеСких болезней человека [13]. По мнению С. Сеунга [1], при церебраль-ной патологии «общее число нейронов и синапсов оста-нется нормальным, однако они будут связаны отнюдь не идеальным образом», неочевидное утверждение, согла-ситься с которым трудно.…”

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

“…In particular, in this PhD, registration is the key stage to automatically delineate anatomical structures that may be injured. The method described in this research work addresses a key need in the management of brain injury, that was identified by (Irimia et al 2012 Fig.III.21 shows the diagram of a typical registration algorithm. The image that remains unchanged is named reference image, the image that will be transformed to be as similar as possible to the reference image is known as source.…”