Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque. In this work, we address this issue by comparing 35 structural connectome-building pipelines. We vary diffusion reconstruction models, tractography algorithms and parcellations. Next, we classify structural connectome pairs as either belonging to the same individual or not. Connectome weights and eight topological derivative measures form our feature set. For experiments, we use three test-retest datasets from the Consortium for Reliability and Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare pairwise classification results to a commonly used parametric test-retest measure, Intraclass Correlation Coefficient (ICC).Comment: Accepted for MICCAI 2017, 8 pages, 3 figure

Mathematical Modelling of Cell-Fate Decision in Response to Death Receptor Engagement

Cytokines such as TNF and FASL can trigger death or survival depending on cell lines and cellular conditions. The mechanistic details of how a cell chooses among these cell fates are still unclear. The understanding of these processes is important since they are altered in many diseases, including cancer and AIDS. Using a discrete modelling formalism, we present a mathematical model of cell fate decision recapitulating and integrating the most consistent facts extracted from the literature. This model provides a generic high-level view of the interplays between NFκB pro-survival pathway, RIP1-dependent necrosis, and the apoptosis pathway in response to death receptor-mediated signals. Wild type simulations demonstrate robust segregation of cellular responses to receptor engagement. Model simulations recapitulate documented phenotypes of protein knockdowns and enable the prediction of the effects of novel knockdowns. In silico experiments simulate the outcomes following ligand removal at different stages, and suggest experimental approaches to further validate and specialise the model for particular cell types. We also propose a reduced conceptual model implementing the logic of the decision process. This analysis gives specific predictions regarding cross-talks between the three pathways, as well as the transient role of RIP1 protein in necrosis, and confirms the phenotypes of novel perturbations. Our wild type and mutant simulations provide novel insights to restore apoptosis in defective cells. The model analysis expands our understanding of how cell fate decision is made. Moreover, our current model can be used to assess contradictory or controversial data from the literature. Ultimately, it constitutes a valuable reasoning tool to delineate novel experiments

Modulation of Interleukin-1 Transcriptional Response by the Interaction between VRK2 and the JIP1 Scaffold Protein

Background. Cellular biological responses to specific stimulation are determined by a balance among signaling pathways. Protein interactions are likely to modulate these pathways. Vaccinia-related kinase-2 (VRK2) is a novel human kinase that can modulate different signaling pathways. Principal findings. We report that in vivo, the activity of JIP1-JNK complexes is downregulated by VRK2 in response to interleukin-1β. Also the reduction of endogenous VRK2 with shRNA increases the transcriptional response to IL-1β. The JIP1 scaffold protein assembles three consecutive members of a given MAPK pathway forming signaling complexes and their signal can be modulated by interactions with regulatory proteins that remain to be identified. Knocking-down JIP1 with siRNA resulted in elimination of the AP1 transcriptional response to IL-1β. VRK2, a member of novel Ser-Thr kinase family, is able to stably interact with JIP1, TAK1 and MKK7, but not JNK, and can be isolated forming oligomeric complexes with different proportions of TAK1, MKK7β1 and JNK. JIP1 assembles all these proteins in an oligomeric signalosome. VRK2 binding to the JIP1 signalosome prevents the association of JNK and results in a reduction in its phosphorylation and downregulation of AP1-dependent transcription. Conclusions/Significance. This work suggests that the intracellular level of VRK2 protein can modulate the flow through a signaling pathway and alter the response from a receptor that can be distributed by more than one pathway, and thus contribute to the cellular specificity of the response by forming alternative signaling complexes. Furthermore, the effect might be more general and affect other signaling routes assembled on the JIP1 scaffold protein for which a model is proposed.S.B., M. S-G, and C.R.S. have predoctoral fellowships from Ministerio de Educación y Ciencia, CSIC (Spain) and Fundação para a Ciência e a Tecnologia (Portugal) respectively. This work was funded by grants from Ministerio de Educación y Ciencia (SAF2004-02900, SAF2007-60242 and Consolider CSD-2007-0017), Fundación de Investigación Médica MM and Federación de Cajas de Ahorro de Castilla y León to P.A.L.Peer reviewe

Structure and Dynamics of Biological Systems: Integration of Neutron Scattering with Computer Simulation

The combination of molecular dynamics simulation and neutron scattering techniques has emerged as a highly synergistic approach to elucidate the atomistic details of the structure, dynamics and functions of biological systems. Simulation models can be tested by calculating neutron scattering structure factors and comparing the results directly with experiments. If the scattering profiles agree the simulations can be used to provide a detailed decomposition and interpretation of the experiments, and if not, the models can be rationally adjusted. Comparison with neutron experiment can be made at the level of the scattering functions or, less directly, of structural and dynamical quantities derived from them. Here, we examine the combination of simulation and experiment in the interpretation of SANS and inelastic scattering experiments on the structure and dynamics of proteins and other biopolymers

Measuring macroscopic brain connections in vivo

Decades of detailed anatomical tracer studies in non-human animals point to a rich and complex organization of long-range white matter connections in the brain. State-of-the art in vivo imaging techniques are striving to achieve a similar level of detail in humans, but multiple technical factors can limit their sensitivity and fidelity. In this review, we mostly focus on magnetic resonance imaging of the brain. We highlight some of the key challenges in analyzing and interpreting in vivo connectomics data, particularly in relation to what is known from classical neuroanatomy in laboratory animals. We further illustrate that, despite the challenges, in vivo imaging methods can be very powerful and provide information on connections that is not available by any other means

White matter fiber tractography: why we need to move beyond DTI

Object. Diffusion-based MRI tractography is an imaging tool increasingly used in neurosurgical procedures to generate 3D maps of white matter pathways as an aid to identifying safe margins of resection. The majority of white matter fiber tractography software packages currently available to clinicians rely on a fundamentally flawed framework to generate fiber orientations from diffusion-weighted data, namely diffusion tensor imaging (DTI). This work provides the first extensive and systematic exploration of the practical limitations of DTI-based tractography and investigates whether the higher-order tractography model constrained spherical deconvolution provides a reasonable solution to these problems within a clinically feasible timeframe.Methods. Comparison of tractography methodologies in visualizing the corticospinal tracts was made using the diffusion-weighted data sets from 45 healthy controls and 10 patients undergoing presurgical imaging assessment. Tensor-based and constrained spherical deconvolution based tractography methodologies were applied to both patients and controls.Results. Diffusion tensor imaging based tractography methods (using both deterministic and probabilistic tractography algorithms) substantially underestimated the extent of tracks connecting to the sensorimotor cortex in all participants in the control group. In contrast, the constrained spherical deconvolution tractography method consistently produced the biologically expected fan-shaped configuration of tracks. In the clinical cases, in which tractography was performed to visualize the corticospinal pathways in patients with concomitant risk of neurological deficit following neurosurgical resection, the constrained spherical deconvolution based and tensor-based tractography methodologies indicated very different apparent safe margins of resection; the constrained spherical deconvolution based method identified corticospinal tracts extending to the entire sensorimotor cortex, while the tensor-based method only identified a narrow subset of tracts extending medially to the vertex.Conclusions. This comprehensive study shows that the most widely used clinical tractography method (diffusion tensor imaging based tractography) results in systematically unreliable and clinically misleading information. The higher-order tractography model, using the same diffusion-weighted data, clearly demonstrates fiber tracts more accurately, providing improved estimates of safety margins that may be useful in neurosurgical procedures. We therefore need to move beyond the diffusion tensor framework if we are to begin to provide neurosurgeons with biologically reliable tractography information.</p

Nonlinear Alignment of Whole Tractograms with the Linear Assignment Problem

After registration of the imaging data of two brains, homologous anatomical structures are expected to overlap better than before registration. Diffusion magnetic resonance imaging (dMRI) techniques and tractography techniques provide a representation of the anatomical connections in the white matter, as hundreds of thousands of streamlines, forming the tractogram. The literature on methods for aligning tractograms is in active development and provides methods that operate either from voxel information, e.g. fractional anisotropy, orientation distribution function, T1-weighted MRI, or directly from streamline information. In this work, we align streamlines using the linear assignment problem (LAP) and propose a method to reduce the high computational cost of aligning whole brain tractograms. As further contribution, we present a comparison among some of the freely-available linear and nonlinear tractogram alignment methods, where we show that our LAP-based method outperforms all others. In discussing the results, we show that a main limitation of all streamline-based nonlinear registration methods is the computational cost and that addressing such problem may lead to further improvement in the quality of registration