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

Constructive connectomics: How neuronal axons get from here to there using gene-expression maps derived from their family trees

During brain development, billions of axons must navigate over multiple spatial scales to reach specific neuronal targets, and so build the processing circuits that generate the intelligent behavior of animals. However, the limited information capacity of the zygotic genome puts a strong constraint on how, and which, axonal routes can be encoded. We propose and validate a mechanism of development that can provide an efficient encoding of this global wiring task. The key principle, confirmed through simulation, is that basic constraints on mitoses of neural stem cells—that mitotic daughters have similar gene expression to their parent and do not stray far from one another—induce a global hierarchical map of nested regions, each marked by the expression profile of its common progenitor population. Thus, a traversal of the lineal hierarchy generates a systematic sequence of expression profiles that traces a staged route, which growth cones can follow to their remote targets. We have analyzed gene expression data of developing and adult mouse brains published by the Allen Institute for Brain Science, and found them consistent with our simulations: gene expression indeed partitions the brain into a global spatial hierarchy of nested contiguous regions that is stable at least from embryonic day 11.5 to postnatal day 56. We use this experimental data to demonstrate that our axonal guidance algorithm is able to robustly extend arbors over long distances to specific targets, and that these connections result in a qualitatively plausible connectome. We conclude that, paradoxically, cell division may be the key to uniting the neurons of the brain

Micro-connectomics: probing the organization of neuronal networks at the cellular scale.

Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.This work was supported by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Nature Publishing Group

Micro-connectomics: probing the organization of neuronal networks at the cellular scale.

Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.This work was supported by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Nature Publishing Group

Digital reconstruction, quantitative morphometric analysis, and membrane properties of bipolar cells in the rat retina.

A basic principle of neuroscience is that structure reflects function. This has led to numerous attempts to characterize the complete morphology of types of neurons throughout the central nervous system. The ability to acquire and analyze complete neuronal morphologies has advanced with continuous technological developments for over 150 years, with progressive refinements and increased understanding of the precise anatomical details of different types of neurons. Bipolar cells of the mammalian retina are short-range projections neurons that link the outer and inner retina. Their dendrites contact and receive input from the terminals of the light-sensing photoreceptors in the outer plexiform layer and their axons descend through the inner nuclear and inner plexiform layers to stratify at different levels of the inner plexiform layer. The stratification level of the axon terminals of different types of bipolar cells in the inner plexiform layer determines their synaptic connectivity and is an important basis for the morphological classification of these cells. Between 10 and 15 different types of cone bipolar cells have been identified in different species and they can be divided into ON-cone bipolar cells (that depolarize to the onset of light) and OFF-cone bipolar cells (that depolarize to the offset of light). Different types of cone bipolar cells are thought to be responsible for coding and transmitting different features of our visual environment and generating parallel channels that uniquely filter and transform the inputs from the photoreceptors. There is a lack of detailed morphological data for bipolar cells, especially for the rat, where biophysical mechanisms have been most extensively studied. The work presented in this thesis provides the groundwork for the future goal of developing morphologically realistic compartmental models for cone and rod bipolar cells. First, the contribution of gap junctions to the membrane properties, specifically input resistance, of bipolar cells was investigated. Gap junctions are ubiquitous within the retina, but it remains to be determined whether the strength of coupling between specific cell types is sufficiently strong for the cells to be functionally coupled via electrical synapses. There are gap junctions between the same class of bipolar cells, and this appears to be a common circuit motif in the vertebrate retina. Surprisingly, our results suggested that the gap junctions between OFF-cone bipolar cells do not support consequential electrical coupling. This provides an important first step both to elucidate the potential roles for these gap junctions, and also for the development of compartmental models for cone bipolar cells. Second, from image stacks acquired from multiphoton excitation microscopy, quantitative morphological reconstructions and detailed morphological analysis were performed with fluorescent dye-filled cone and rod bipolar cells. Compared to previous descriptions, the extent and complexity of branching of the axon terminals was surprisingly high. By precisely quantifying the level of stratification of the axon terminals in the inner plexiform layer, we have generated a reference system for the reliable classification of individual cells in future studies that are focused on correlating physiological and morphological properties. The workflow that we have implemented can be readily extended to the development of morphologically realistic compartmental models for these neurons.Doktorgradsavhandlin

Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors

This thesis presents novel methodological approaches for the automated segmentation of neurons from electron microscopic image volumes using machine learning techniques. New potentials for neural segmentation are revealed by incorporating (high-level) biological prior knowledge. This goes beyond the modeling of neural tissue which has been applied for the purpose of its segmentation, so far. Firstly, the V-Multicut algorithm is introduced which enables the consideration of topological constraints for segmented membranes. In this way, biologically implausible appearances of membranes are corrected. Secondly, this thesis proves that, in addition to local evidence and topological requirements for the detection of neural membranes, the consideration of high-level biological prior knowledge is beneficial. For this task, both the recently proposed Asymmetric Multiway Cut and the introduced Semantic Agglomerative Clustering algorithm are implemented and quantitatively evaluated. To be precise, the spatial separation of dendrites and axons in mammals is exploited to significantly improve the segmentation quality. Additionally, new ways to improve the scalability of the used algorithms are presented. All in all this thesis serves as another step towards fully automated segmentation of neurons and contributes to the field of connectomics