2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Fixel-based analysis of the preterm brain: Disentangling bundle-specific white matter microstructural and macrostructural changes in relation to clinical risk factors

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Diffusion MRI (dMRI) studies using the tensor model have identified abnormal white matter development associated with perinatal risk factors in preterm infants studied at term equivalent age (TEA). However, this model is an oversimplification of the underlying neuroanatomy. Fixel-based analysis (FBA) is a novel quantitative framework, which identifies microstructural and macrostructural changes in individual fibre populations within voxels containing crossing fibres. The aim of this study was to apply FBA to investigate the relationship between fixel-based measures of apparent fibre density (FD), fibre bundle cross-section (FC), and fibre density and cross-section (FDC) and perinatal risk factors in preterm infants at TEA. We studied 50 infants (28 male) born at 24.0–32.9 (median 30.4) weeks gestational age (GA) and imaged at 38.6–47.1 (median 42.1) weeks postmenstrual age (PMA). dMRI data were acquired in non-collinear directions with b-value 2500 s/mm 2 on a 3 Tesla system sited on the neonatal intensive care unit. FBA was performed to assess the relationship between FD, FC, FDC and PMA at scan, GA at birth, days on mechanical ventilation, days on total parenteral nutrition (TPN), birthweight z-score, and sex. FBA reveals fibre population-specific alterations in FD, FC and FDC associated with clinical risk factors. FD was positively correlated with GA at birth and was negatively correlated with number of days requiring ventilation. FC was positively correlated with GA at birth, birthweight z-scores and was higher in males. FC was negatively correlated with number of days on ventilation and days on TPN. FDC was positively correlated with GA at birth and birthweight z-scores, negatively correlated with days on ventilation and days on TPN and higher in males. We demonstrate that these relationships are fibre-specific even within regions of crossing fibres. These results show that aberrant white matter development involves both microstructural changes and macrostructural alterations.

          Graphical abstract

          Highlights

          • Fixel-based analysis of effects of risk factors on preterm brain development

          • Differentiates micro- and macrostructural changes in individual fibre bundles

          • Fibre density and cross-section decrease with low gestation and longer ventilation.

          • A number of clinical risk factors are related to lower fibre cross-section.

          Related collections

          Most cited references54

          • Record: found
          • Abstract: found
          • Article: not found

          Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.

          Diffusion-weighted (DW) MR images contain information about the orientation of brain white matter fibres that potentially can be used to study human brain connectivity in vivo using tractography techniques. Currently, the diffusion tensor model is widely used to extract fibre directions from DW-MRI data, but fails in regions containing multiple fibre orientations. The spherical deconvolution technique has recently been proposed to address this limitation. It provides an estimate of the fibre orientation distribution (FOD) by assuming the DW signal measured from any fibre bundle is adequately described by a single response function. However, the deconvolution is ill-conditioned and susceptible to noise contamination. This tends to introduce artefactual negative regions in the FOD, which are clearly physically impossible. In this study, the introduction of a constraint on such negative regions is proposed to improve the conditioning of the spherical deconvolution. This approach is shown to provide FOD estimates that are robust to noise whilst preserving angular resolution. The approach also permits the use of super-resolution, whereby more FOD parameters are estimated than were actually measured, improving the angular resolution of the results. The method provides much better defined fibre orientation estimates, and allows orientations to be resolved that are separated by smaller angles than previously possible. This should allow tractography algorithms to be designed that are able to track reliably through crossing fibre regions.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Cognitive and behavioral outcomes of school-aged children who were born preterm: a meta-analysis.

            The cognitive and behavioral outcomes of school-aged children who were born preterm have been reported extensively. Many of these studies have methodological flaws that preclude an accurate estimate of the long-term outcomes of prematurity. To estimate the effect of preterm birth on cognition and behavior in school-aged children. MEDLINE search (1980 to November 2001) for English-language articles, supplemented by a manual search of personal files maintained by 2 of the authors. We included case-control studies reporting cognitive and/or behavioral data of children who were born preterm and who were evaluated after their fifth birthday if the attrition rate was less than 30%. From the 227 reviewed studies, cognitive data from 15 studies and behavioral data from 16 studies were selected. Data on population demographics, study characteristics, and cognitive and behavioral outcomes were extracted from each study, entered in a customized database, and reviewed twice to minimize error. Differences between the mean cognitive scores of cases and controls were pooled. Homogeneity across studies was formally tested using a general variance-based method and graphically using Galbraith plots. Linear meta-analysis regression models were fitted to explore the impact of birth weight and gestational age on cognitive outcomes. Study-specific relative risks (RRs) were calculated for the incidence of attention-deficit/hyperactivity disorder (ADHD) and pooled. Quality assessment of the studies was performed based on a 10-point scale. Publication bias was examined using Begg modified funnel plots and formally tested using the Egger weighted-linear regression method. Among 1556 cases and 1720 controls, controls had significantly higher cognitive scores compared with children who were born preterm (weighted mean difference, 10.9; 95% confidence interval [CI], 9.2-12.5). The mean cognitive scores of preterm-born cases and term-born controls were directly proportional to their birth weight (R(2) = 0.51; P<.001) and gestational age (R(2) = 0.49; P<.001). Age at evaluation had no significant correlation with mean difference in cognitive scores (R(2) = 0.12; P =.20). Preterm-born children showed increases in externalizing and internalizing behaviors in 81% of studies and had more than twice the RR for developing ADHD (pooled RR, 2.64; 95% CI, 1.85-3.78). No differences were noted in cognition and behaviors based on the quality of the study. Children who were born preterm are at risk for reduced cognitive test scores and their immaturity at birth is directly proportional to the mean cognitive scores at school age. Preterm-born children also show an increased incidence of ADHD and other behaviors.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images

              Despite its great potential in studying brain anatomy and structure, diffusion magnetic resonance imaging (dMRI) is marred by artefacts more than any other commonly used MRI technique. In this paper we present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion. Signal loss (dropout) affecting a whole slice, or a large connected region of a slice, is frequently observed in diffusion weighted images, leading to a set of unusable measurements. This is caused by bulk (subject or physiological) motion during the diffusion encoding part of the imaging sequence. We suggest a method to detect slices affected by signal loss and replace them by a non-parametric prediction, in order to minimise their impact on subsequent analysis. The outlier detection and replacement, as well as correction of other dMRI distortions (susceptibility-induced distortions, eddy currents (EC) and subject motion) are performed within a single framework, allowing the use of an integrated approach for distortion correction. Highly realistic simulations have been used to evaluate the method with respect to its ability to detect outliers (types 1 and 2 errors), the impact of outliers on retrospective correction of movement and distortion and the impact on estimation of commonly used diffusion tensor metrics, such as fractional anisotropy (FA) and mean diffusivity (MD). Data from a large imaging project studying older adults (the Whitehall Imaging sub-study) was used to demonstrate the utility of the method when applied to datasets with severe subject movement. The results indicate high sensitivity and specificity for detecting outliers and that their deleterious effects on FA and MD can be almost completely corrected.
                Bookmark

                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                10 April 2019
                2019
                10 April 2019
                : 23
                : 101820
                Affiliations
                [a ]Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King'’s College London, UK
                [b ]Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King'’s College London, UK
                [c ]Department of Computer Science and Centre for Medical Image Computing, University College London, UK
                Author notes
                [* ]Corresponding author at: Centre for the Developing Brain, Department of Perinatal Imaging, School of Biomedical Engineering & Imaging Sciences, Kings College London, 1st Floor South Wing, St Thomas' Hospital, London SE1 7EH, UK. Serena.counsell@ 123456kcl.ac.uk
                Article
                S2213-1582(19)30170-6 101820
                10.1016/j.nicl.2019.101820
                6462822
                30991305
                ab4efc2f-1d7e-464d-bfc6-c14098c85abb
                © 2019 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 8 November 2018
                : 5 April 2019
                : 7 April 2019
                Categories
                Regular Article

                brain,preterm,diffusion mri,fixel-based analysis,dmri, diffusion mri,fba, fixel-based analysis,fc, fibre cross-section,fd, fibre density,fdc, fibre density x fibre cross-section,ga, gestational age,pma, post-menstrual age,tpn, total parenteral nutrition

                Comments

                Comment on this article