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

Interactive computation and visualization of structural connectomes in real-time

Structural networks contain high dimensional data that raise huge computational and visualization problems, especially when attempting to characterise them using graph theory. As a result, it can be non-intuitive to grasp the contribution of each edge within a graph, both at a local and global scale. Here, we introduce a new platform that enables tractography-based networks to be explored in a highly interactive real-time fashion. The framework allows one to interactively tune graph-related parameters on the fly, as opposed to conventional visualization softwares that rely on pre-computed connectivity matrices. From a neurosurgical perspective, the method also provides enhanced understanding regarding the potential removal of a specific node or transection of an edge from the network, allowing surgeons and clinicians to discern the value of each node

Role of the hyporheic heterotrophic biofilm on transformation and toxicity of pesticides

The role of heterotrophic biofilm of water–sediment interface in detoxification processes was tested in abiotic and biotic conditions under laboratory conditions. Three toxicants, a herbicide (Diuron), a fungicide (Dimethomorph) and an insecticide (Chlorpyrifos-ethyl) have been tested in water percolating into columns reproducing hyporheic sediment. The detoxification processes were tested by comparing the water quality after 18 days of percolation with and without heterotrophic biofilm. Tested concentrations were 30 mg.Lx1 of Diuron diluted in 0.1% dimethyl sulfoxide (DMSO), 2 mg.Lx1 of Dimethomorph and 0.1 mg.Lx1 of Chlorpyrifos-ethyl. To characterise the detoxification efficiency of the system, we performed genotoxicity bioassays in amphibian larvae and rotifers and measured the respiration and denitrification of sediments. Although the presence of biofilm increased the production of N-(3,4 dichlorophenyl)-N-(methyl)-urea, a metabolite of diuron, the toxicity did not decrease irrespective of the bioassay. In the presence of biofilm, Dimethomorph concentrations decreased compared with abiotic conditions, from 2 mg.Lx1 to 0.4 mg.Lx1 after 18 days of percolation. For both Dimethomorph and Chlorpyrifos-ethyl additions, assessment of detoxification level by the biofilm depended on the test used: detoxification effect was found with amphibian larvae bioassay and no detoxification was observed with the rotifer test. Heterotrophic biofilm exerts a major influence in the biochemical transformation of contaminants such as pesticides, suggesting that the interface between running water and sediment plays a role in self-purification of stream reaches

Mapping Connectivity Damage in the Case of Phineas Gage

White matter (WM) mapping of the human brain using neuroimaging techniques has gained considerable interest in the neuroscience community. Using diffusion weighted (DWI) and magnetic resonance imaging (MRI), WM fiber pathways between brain regions may be systematically assessed to make inferences concerning their role in normal brain function, influence on behavior, as well as concerning the consequences of network-level brain damage. In this paper, we investigate the detailed connectomics in a noted example of severe traumatic brain injury (TBI) which has proved important to and controversial in the history of neuroscience. We model the WM damage in the notable case of Phineas P. Gage, in whom a “tamping iron” was accidentally shot through his skull and brain, resulting in profound behavioral changes. The specific effects of this injury on Mr. Gage's WM connectivity have not previously been considered in detail. Using computed tomography (CT) image data of the Gage skull in conjunction with modern anatomical MRI and diffusion imaging data obtained in contemporary right handed male subjects (aged 25–36), we computationally simulate the passage of the iron through the skull on the basis of reported and observed skull fiducial landmarks and assess the extent of cortical gray matter (GM) and WM damage. Specifically, we find that while considerable damage was, indeed, localized to the left frontal cortex, the impact on measures of network connectedness between directly affected and other brain areas was profound, widespread, and a probable contributor to both the reported acute as well as long-term behavioral changes. Yet, while significantly affecting several likely network hubs, damage to Mr. Gage's WM network may not have been more severe than expected from that of a similarly sized “average” brain lesion. These results provide new insight into the remarkable brain injury experienced by this noteworthy patient

Brain structural and functional asymmetry in human situs inversus totalis

Magnetic resonance imaging was used to investigate brain structural and functional asymmetries in 15 participants with complete visceral reversal (situs inversus totalis, SIT). Language-related brain structural and functional lateralization of SIT participants, including peri-Sylvian gray and white matter asymmetries and hemispheric language dominance, was similar to those of 15 control participants individually matched for sex, age, education, and handedness. In contrast, the SIT cohort showed reversal of the brain (Yakovlevian) torque (occipital petalia and occipital bending) compared to the control group. Secondary findings suggested different asymmetry patterns between SIT participants with (n = 6) or without (n = 9) primary ciliary dyskinesia (PCD, also known as Kartagener syndrome) although the small sample sizes warrant cautious interpretation. In particular, reversed brain torque was mainly due to the subgroup with PCD-unrelated SIT and this group also included 55% left handers, a ratio close to a random allocation of handedness. We conclude that complete visceral reversal has no effect on the lateralization of brain structural and functional asymmetries associated with language, but seems to reverse the typical direction of the brain torque in particular in participants that have SIT unrelated to PCD. The observed differences in asymmetry patterns of SIT groups with and without PCD seem to suggest that symmetry breaking of visceral laterality, brain torque, and language dominance rely on different mechanisms

Effect of perinatal adversity on structural connectivity of the developing brain

Globally, preterm birth (defined as birth at <37 weeks of gestation) affects around 11% of deliveries and it is closely associated with cerebral palsy, cognitive impairments and neuropsychiatric diseases in later life. Magnetic Resonance Imaging (MRI) has utility for measuring different properties of the brain during the lifespan. Specially, diffusion MRI has been used in the neonatal period to quantify the effect of preterm birth on white matter structure, which enables inference about brain development and injury. By combining information from both structural and diffusion MRI, is it possible to calculate structural connectivity of the brain. This involves calculating a model of the brain as a network to extract features of interest. The process starts by defining a series of nodes (anatomical regions) and edges (connections between two anatomical regions). Once the network is created, different types of analysis can be performed to find features of interest, thereby allowing group wise comparisons. The main frameworks/tools designed to construct the brain connectome have been developed and tested in the adult human brain. There are several differences between the adult and the neonatal brain: marked variation in head size and shape, maturational processes leading to changes in signal intensity profiles, relatively lower spatial resolution, and lower contrast between tissue classes in the T1 weighted image. All of these issues make the standard processes to construct the brain connectome very challenging to apply in the neonatal population. Several groups have studied the neonatal structural connectivity proposing several alternatives to overcome these limitations. The aim of this thesis was to optimise the different steps involved in connectome analysis for neonatal data. First, to provide accurate parcellation of the cortex a new atlas was created based on a control population of term infants; this was achieved by propagating the atlas from an adult atlas through intermediate childhood spatio-temporal atlases using image registration. After this the advanced anatomically-constrained tractography framework was adapted for the neonatal population, refined using software tools for skull-stripping, tissue segmentation and parcellation specially designed and tested for the neonatal brain. Finally, the method was used to test the effect of early nutrition, specifically breast milk exposure, on structural connectivity in preterm infants. We found that infants with higher exposure to breastmilk in the weeks after preterm birth had improved structural connectivity of developing networks and greater fractional anisotropy in major white matter fasciculi. These data also show that the benefits are dose dependent with higher exposure correlating with increased white matter connectivity. In conclusion, structural connectivity is a robust method to investigate the developing human brain. We propose an optimised framework for the neonatal brain, designed for our data and using tools developed for the neonatal brain, and apply it to test the effect of breastmilk exposure on preterm infants