Supplemental Materials to submitted paper

<p>This file contains the supplemental materials to the paper:</p> <p><em>Esteban O. et al., Surface-driven registration method for the structure-informed segmentation of diffusion MR images. NeuroImage (accepted), 2016. </em><em>doi: http://dx.doi.org/10.1016/j.neuroimage.2016.05.011</em></p><p><em><br></em></p> <p><strong>Abstract:</strong></p> <p>Current methods for processing diffusion MRI (dMRI) to map the connectivity of the human brain require precise delineations of anatomical structures. This requirement has been approached either segmenting the data in native dMRI space or mapping the structural information from T1-weighted (T1w) images. The characteristic features of diffusion data in terms of signal-to-noise ratio, resolution, as well as the geometrical distortions caused by the inhomogeneity of magnetic susceptibility across tissues hinder both solutions. Unifying the two approaches, we propose regseg, a surface-to-volume nonlinear registration method that segments homogeneous regions within multivariate images by mapping a set of nested reference-surfaces. Accurate surfaces are extracted from a T1w image of the subject, using as target image the bivariate volume comprehending the fractional anisotropy (FA) and the apparent diffusion coefficient (ADC) maps derived from the dMRI dataset. We first verify the accuracy of regseg on a general context using digital phantoms. Then we establish an evaluation framework using undistorted dMRI data from the Human Connectome Project (HCP) and known deformations derived from real inhomogeneity fieldmaps. We analyze the performance of regseg computing the misregistration error of the surfaces estimated after being mapped with regseg onto 16 datasets from the HCP. The distribution of errors shows a 95% CI of 0.56–0.66 mm, that is below the dMRI resolution (1.25 mm, isotropic). Finally, we cross-compare the proposed tool against a nonlinear b0-to-T2w registration method, thereby obtaining a significantly lower misregistration error with<br>regseg. Therefore, we demonstrate that regseg allows the accurate mapping of structural information in dMRI space, enabling the application of new structure-informed techniques in the connectome extraction.</p> <p> </p

Whole-brain tractography.

<p>Example tractograms estimated with (A) the standard <i>deterministic streamline</i> and (B) the <i>global tractography</i> approach <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048121#pone.0048121-Reisert1" target="_blank">[26]</a>.</p

Registration between morphological and diffusion images.

<p>The reference space in CMP is the one of the diffusion images. The tissue masks extracted during the segmentation step, then, have to be registered to the diffusion space (A). The quality of the registrations can be inspected by overlaying on the b0 either the T1-weighted volume (B) or the geometric models of the cortex estimated with Freesurfer (C).</p

Data_Sheet_1_Evaluation of tractography-based myelin-weighted connectivity across the lifespan.docx

IntroductionRecent studies showed that the myelin of the brain changes in the life span, and demyelination contributes to the loss of brain plasticity during normal aging. Diffusion-weighted magnetic resonance imaging (dMRI) allows studying brain connectivity in vivo by mapping axons in white matter with tractography algorithms. However, dMRI does not provide insight into myelin; thus, combining tractography with myelin-sensitive maps is necessary to investigate myelin-weighted brain connectivity. Tractometry is designated for this purpose, but it suffers from some serious limitations. Our study assessed the effectiveness of the recently proposed Myelin Streamlines Decomposition (MySD) method in estimating myelin-weighted connectomes and its capacity to detect changes in myelin network architecture during the process of normal aging. This approach opens up new possibilities compared to traditional Tractometry.MethodsIn a group of 85 healthy controls aged between 18 and 68 years, we estimated myelin-weighted connectomes using Tractometry and MySD, and compared their modulation with age by means of three well-known global network metrics.ResultsFollowing the literature, our results show that myelin development continues until brain maturation (40 years old), after which degeneration begins. In particular, mean connectivity strength and efficiency show an increasing trend up to 40 years, after which the process reverses. Both Tractometry and MySD are sensitive to these changes, but MySD turned out to be more accurate.ConclusionAfter regressing the known predictors, MySD results in lower residual error, indicating that MySD provides more accurate estimates of myelin-weighted connectivity than Tractometry.</p

Basic workflow to create a connectome.

<p>Morphological and diffusion MRI images are processed as separate streams. Several possibilities are available in each stage. The final connectome is obtained by registering and merging the data coming from the two streams.</p

Graphical user interface (GUI) of the Connectome Mapper.

<p>The GUI controls the proper execution of the whole processing workflow (A) and helps the user in setting all the parameters required at each step (B). Metadata associated with the files being processed can also be entered, and CMP organises the data accordingly in a hierarchical structure (C) in order to simplify the management of big amount of data.</p

Multi-scale connectomes.

<p>The five multi-scale atlases derived from the Desikan-Killiany's anatomical atlas implemented in Freesurfer, and the corresponding connectivity matrices.</p

Data inspector.

<p>Sometimes a flipping/swapping can be present in the data due to incorrect information stored in the image's header (top). CMP allows to interactively explore the data and easily fix the problem (bottom).</p

Multi-variate connectomes.

<p>Example of weighted connectomes computed using different measures for quantifying the connectivity strength between each pair of regions. In (A) the weight of each edge is proportional to the <i>number of connecting fibres</i> (logarithmic scale), while in (B) the <i>average GFA along the bundles</i> was used instead. The inter-hemispheric connections are highlighted here in white: although they do not differ much in number from the rest of the brain, they clearly manifest a higher GFA as they pass through the <i>Corpus Callosum</i>.</p

Normalized connectivity as a function of the diffusion encoding scheme for the selected association fiber pathways.

<p>The reported values are obtained by averaging the normalized connectivity over the five subjects.</p