Spherical-deconvolution Informed Filtering Of Tractograms Research Articles

Randomized iterative spherical‐deconvolution informed tractogram filtering

Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of-the-art tractography methods are anatomically implausible. To address this problem, tractogram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, Spherical-deconvolution Informed Filtering of Tractograms (SIFT), which uses a global optimization approach to improve the agreement between the remaining streamlines after filtering and the underlying diffusion magnetic resonance imaging data. SIFT is not suitable for judging the compliance of individual streamlines with the acquired data since its results depend on the size and composition of the surrounding tractogram. To tackle this problem, we propose applying SIFT to randomly selected tractogram subsets in order to retrieve multiple assessments for each streamline. This approach makes it possible to identify streamlines with very consistent filtering results, which were used as pseudo-ground truths for training classifiers. The trained classifier is able to distinguish the obtained groups of complying and non-complying streamlines with the acquired data with an accuracy above 80%.

Structural connectivity changes in the cerebral pain matrix in burning mouth syndrome: a multi-shell, multi-tissue-constrained spherical deconvolution model analysis.

Burning mouth syndrome (BMS) is a chronic intraoral pain syndrome. Previous studies have attempted to determine the brain connectivity features in BMS using functional and structural magnetic resonance imaging. However, no study has investigated the structural connectivity using multi-shell, multi-tissue-constrained spherical deconvolution (MSMT-CSD), anatomically constrained tractography (ACT), and spherical deconvolution informed filtering of tractograms (SIFT). Therefore, this study aimed to assess the differences in brain structural connectivity of patients with BMS and healthy controls using probabilistic tractography with these methods, and graph analysis. Fourteen patients with BMS and 11 age- and sex-matched healthy volunteers underwent 3-T magnetic resonance imaging. MSMT-CSD-based probabilistic structural connectivity was computed using the second-order integration over fiber orientation distributions algorithm based on nodes set in 84 anatomical cortical regions with ACT and SIFT. A t-test was performed for comparisons between the BMS and healthy control brain networks. The betweenness centrality was significantly higher in the left insula, right amygdala, and right lateral orbitofrontal cortex and significantly lower in the right inferotemporal cortex in the BMS group than that in healthy controls. However, no significant difference was found in the clustering coefficient, node degree, and small-worldness between the two groups. Graph analysis of brain probabilistic structural connectivity, based on diffusion imaging using an MSMT-CSD model with ACT and SIFT, revealed alterations in the regions comprising the pain matrix and medial pain ascending pathway. These results highlight the emotional-affective profile of BMS, which is a type of chronic pain syndrome.

M160. INVESTIGATING STRUCTURAL CONNECTIVITY CORRELATES OF VERBAL MEMORY DEFICITS AMONG FIRST-EPISODE PSYCHOSIS PATIENTS

BackgroundVerbal memory is one of the most affected cognitive domains in patients with schizophrenia and related psychoses. Several studies have found associations between cognitive abilities and white matter fractional anisotropy (FA) in schizophrenia; however, only a few tractography studies have investigated FA relative to verbal memory in patients with a first episode of psychosis (FEP) compared with healthy controls (HC). Although white matter tractography differences have been well established between chronic patients and HC, the direction of findings from FEP studies has been inconsistent. Thus, the present study aims to examine whole-brain white matter differences and its association with verbal memory in individuals with a FEP relative to HC using tractography.MethodsDiffusion-weighted images were acquired on a 1.5T scanner for patients (n=65) and controls (n=54) at baseline. The Wechsler Memory Scale was used as a measure of verbal memory. Pre-processing was performed on a subject-by-subject basis using MRtrix. Diffusion tractography was generated using a probabilistic anatomically-constrained tractography algorithm, which constrains the reconstruction to specific biological priors. Furthermore, the spherical-deconvolution informed filtering of tractograms (SIFT) tool will be used to ensure the tractogram is biologically meaningful. This results in subject-specific connectomes defining the mean FA between two regions of interest that were defined using the Desikan- Killiany atlas. A linear model was used to test for main effect of group and main effect of verbal memory on white matter tract FA, covarying for age and sex. For both sets of analyses, results were corrected for multiple comparisons using false discovery rate (FDR).ResultsA significant main effect of group on whole-brain average FA was observed, with patients displaying lower average FA compared to healthy controls (Patients=0.291, controls=0.300, p<0.05). Whole-brain white matter tract FA analysis revealed that there are widespread differences between controls and individuals with a FEP. Group most strongly predicted white matter tract FA differences between left caudal anterior cingulate and left lateral orbitofrontal (patients mean FA=0.302, controls mean FA=0.342), left hippocampus and right isthmus cingulate (patient mean FA= 0.217 controls mean FA= 0.318), and finally left lingual and left rostral anterior cingulate (patients mean FA=0.162, controls mean FA= 0.249. However, none survived correction for multiple comparisons. Further, there was no significant association between verbal memory and white matter tract FA in FEP or HC.DiscussionFindings from this study suggest there are some significant differences in whole-brain average FA between individuals experiencing a FEP and healthy controls. However, when analyzing whole-brain tract FA, none of the connections survived corrections for multiple comparisons. These findings might be limited by the scanner resolution included in this study, which may not capture more subtle differences. Nonetheless, these results are consistent with a cross-sectional study comparing healthy individuals to chronic and first-episode patients suggesting that modest differences are present early in the disease and increase as the disease progresses. We suggest that future studies analyze white matter tract using a longitudinal design to identify disease progression.

Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics

Diffusion MRI streamlines tractography has become a major technique for inferring structural networks through reconstruction of brain connectome. However, quantification of structural connectivity based on the number of streamlines interconnecting brain grey matter regions is known to be problematic in a number of aspects, such as the ill-posed nature of streamlines terminations and the non-quantitative nature of streamline counts. This study investigates the effects of state-of-the-art connectome construction methods on the subsequent analyses of structural brain networks using graph theoretical approaches. Our results demonstrate that the characteristics of structural connectivity, including connectome variability, global network metrics, small-world attributes and network hubs, alter significantly following the improvement in biological accuracy of streamlines tractograms provided by anatomically-constrained tractography (ACT) and spherical-deconvolution informed filtering of tractograms (SIFT). Importantly, the commonly-used correction for connection density based on scaling the contribution of each streamline to the connectome by its inverse length is shown to provide incomplete correction, highlighting the necessity for the use of advanced tractogram reconstruction techniques in structural connectomics research.

SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography

Diffusion MRI streamlines tractography allows for the investigation of the brain white matter pathways non-invasively. However a fundamental limitation of this technology is its non-quantitative nature, i.e. the density of reconstructed connections is not reflective of the density of underlying white matter fibres. As a solution to this problem, we have previously published the “spherical-deconvolution informed filtering of tractograms (SIFT)” method, which determines a subset of the streamlines reconstruction such that the streamlines densities throughout the white matter are as close as possible to fibre densities estimated using the spherical deconvolution diffusion model; this permits the use of streamline count as a valid biological marker of connection density. Particular aspects of its performance may have however limited its uptake in the diffusion MRI research community. Here we present an alternative to this method, entitled SIFT2, which provides a more logically direct and computationally efficient solution to the streamlines connectivity quantification problem: by determining an appropriate cross-sectional area multiplier for each streamline rather than removing streamlines altogether, biologically accurate measures of fibre connectivity are obtained whilst making use of the complete streamlines reconstruction.

Quantification of voxel-wise total fibre density: Investigating the problems associated with track-count mapping

A biological parameter that would be valuable to be able to extract from diffusion MRI data is the local white matter axonal density. Track-density imaging (TDI) has been used as if it could provide such a measure; however, this has been the subject of controversy, primarily due to the fact that track-count quantitation is highly sensitive to tracking biases and errors. The spherical-deconvolution informed filtering of tractograms (SIFT) post-processing method was recently introduced to minimise tractography biases, and thus provides a more biologically meaningful measure that could be used in track-count mapping (i.e. TDI following SIFT). The TDI intensity following SIFT ideally corresponds to the orientational average of the fibre orientation distribution (FOD), which corresponds to the total Apparent Fibre Density (AFDtotal) within the AFD framework; in fact, AFDtotal provides a direct measure of local fibre density at native resolution that does not rely on fibre-tracking. In this study, we demonstrate problems associated with quantitative TDI investigations, which can be avoided by using SIFT processing or directly by using AFDtotal maps. We also characterise the intra- and inter-subject reproducibility of TDI maps (with and without SIFT pre-processing) and AFDtotal maps. It is shown that SIFT improves the quantitative characteristics of TDI, but is still vastly inferior to the properties of the AFDtotal parameter itself, because the latter does not require tracking. While standard TDI might be preferable in applications when high anatomical contrast is required, particularly when combined with super-resolution, for voxel-wise quantitation of total tract density (i.e. without tract orientation information) at native resolution, the total AFD maps are preferable to TDI or other related track-count maps. Regardless of the track-count measure, it should be noted that all of these voxel-averaged approaches discard important information that is retained in fibre-specific approaches such as AFD.