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

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

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

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

Interplay between blood-brain barrier disruption and neuroinflammation following severe traumatic brain injury

A severe traumatic brain injury (TBI) holds deleterious consequences for the afflicted, its next-of-kin and society. Still today, prognosis is semi-desolate. One explanation for this might be pathophysiological processes ensuing the primary trauma that are but indirectly targeted for treatment. Among such processes, blood-brain barrier (BBB) disruption and neuroinflammation constitute two astrocyte-dependent mechanisms that interplay in the aftermath of a severe TBI. The overall aim of this thesis was to characterize both BBB disruption and neuroinflammation translationally. In paper I, n = 17 patients with severe TBI were included in a prospective observational longitudinal study. Here, the protein biomarkers S100B and neuron-specific enolase (NSE) were sampled with high temporal resolution from both cerebrospinal fluid (CSF) and blood. We found that BBB disruption occurred among numerous patients and remained throughout the first week following injury. Interestingly, BBB disruption also affected clearance from brain to blood of S100B, but not NSE. This indicates that biomarkers are cleared differently from the injured CNS. We elaborated on this by utilizing a larger cohort size (n = 190 patients), which enabled outcome prediction modelling, in paper II. In this prospective, observational, cross-sectional study, we found that BBB disruption comprised a novel, independent outcome predictor that strongly related to levels of neuroinflammatory proteins in CSF and inflammatory processes within the injured brain. Among pathways assessed, particularly the complement system entailed proteins of future interest. We next assessed the relationship between in situ neuroinflammatory protein expression, BBB disruption, and brain edema in paper III. By utilizing a rodent model of severe TBI, we found that the cytotoxic edema region was associated with an innate neuroinflammatory response, and astrocytic aquaporin-4 retraction from the BBB interface. In fact, the astrocyte itself is an important neuroinflammatory cell, which we showed in paper IV, where we constructed a disease-modelling system of stem cell-derived astrocytes that we exposed to neuroinflammatory substances. Following neuroinflammatory stimulus, astrocytes exhibited an important increase in canonical stress-response pathways. Importantly, following stimulation with clinically relevant neuroinflammatory substances seen in human TBI from paper II, they also acquired a neurotoxic potential, of plausible importance for local cell survival following a severe TBI. Taken together, BBB disruption and neuroinflammation ensue a severe TBI. Neuroinflammation, particularly mediated by the complement system, stands out as a future therapeutic target in order to mitigate exacerbated BBB disruption. Locally in the lesion vicinity, additional neuroinflammatory mechanisms are in part mediated by astrocytes, where these cells seem to have an important role in local cell survival. Onwards, our findings suggest that future efforts should be directed at evaluating if neuroinflammatory modulation of complement inhibition yields improved outcome, while elaborating on the promising experimental data of astrocyte-mediated effects in the lesion vicinity

Technological aids for the rehabilitation of memory and executive functioning in children and adolescents with acquired brain injury (Review).

No abstract available

Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging

Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (n = 83) and healthy controls (HCs) (n = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and improves classification performance. Additionally, a novel data interpretation approach was utilized to identify top-performing features learned by the AEs. Our method delivered a classification accuracy within the range of 79–91.67% for single neuroimaging modalities. However, the performance of classification improved to 95.83%, thereby employing the multimodality model. The models have identified several brain regions located in the default mode network, sensorimotor network, visual cortex, cerebellum, and limbic system as the most discriminative features. We suggest that this approach could be extended to the objective biomarkers predicting mTBI in clinical settings

Deep Learning-Based Multimodality Classification of Chronic Mild Traumatic Brain Injury Using Resting-State Functional MRI and PET Imaging

Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (n = 83) and healthy controls (HCs) (n = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and improves classification performance. Additionally, a novel data interpretation approach was utilized to identify top-performing features learned by the AEs. Our method delivered a classification accuracy within the range of 79–91.67% for single neuroimaging modalities. However, the performance of classification improved to 95.83%, thereby employing the multimodality model. The models have identified several brain regions located in the default mode network, sensorimotor network, visual cortex, cerebellum, and limbic system as the most discriminative features. We suggest that this approach could be extended to the objective biomarkers predicting mTBI in clinical settings

Optical imaging and spectroscopy for the study of the human brain: status report.

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions

Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions

Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions. Keywords: DCS; NIRS; diffuse optics; functional neuroscience; optical imaging; optical spectroscop