PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical Imaging. v2: wadded funders acknowledgements to preprin

Ab Initio Identification of Novel Regulatory Elements in the Genome of Trypanosoma brucei by Bayesian Inference on Sequence Segmentation

Background: The rapid increase in the availability of genome information has created considerable demand for both comparative and ab initio predictive bioinformatic analyses. The biology laid bare in the genomes of many organisms is often novel, presenting new challenges for bioinformatic interrogation. A paradigm for this is the collected genomes of the kinetoplastid parasites, a group which includes Trypanosoma brucei the causative agent of human African trypanosomiasis. These genomes, though outwardly simple in organisation and gene content, have historically challenged many theories for gene expression regulation in eukaryotes. Methodology/Principle Findings: Here we utilise a Bayesian approach to identify local changes in nucleotide composition in the genome of T. brucei. We show that there are several elements which are found at the starts and ends of multicopy gene arrays and that there are compositional elements that are common to all intergenic regions. We also show that there is a composition-inversion element that occurs at the position of the trans-splice site. Conclusions/Significance: The nature of the elements discovered reinforces the hypothesis that context dependant RN

Laminar and Dorsoventral Molecular Organization of the Medial Entorhinal Cortex Revealed by Large-scale Anatomical Analysis of Gene Expression

Neural circuits in the medial entorhinal cortex (MEC) encode an animal's position and orientation in space. Within the MEC spatial representations, including grid and directional firing fields, have a laminar and dorsoventral organization that corresponds to a similar topography of neuronal connectivity and cellular properties. Yet, in part due to the challenges of integrating anatomical data at the resolution of cortical layers and borders, we know little about the molecular components underlying this organization. To address this we develop a new computational pipeline for high-throughput analysis and comparison of in situ hybridization (ISH) images at laminar resolution. We apply this pipeline to ISH data for over 16,000 genes in the Allen Brain Atlas and validate our analysis with RNA sequencing of MEC tissue from adult mice. We find that differential gene expression delineates the borders of the MEC with neighboring brain structures and reveals its laminar and dorsoventral organization. We propose a new molecular basis for distinguishing the deep layers of the MEC and show that their similarity to corresponding layers of neocortex is greater than that of superficial layers. Our analysis identifies ion channel-, cell adhesion- and synapse-related genes as candidates for functional differentiation of MEC layers and for encoding of spatial information at different scales along the dorsoventral axis of the MEC. We also reveal laminar organization of genes related to disease pathology and suggest that a high metabolic demand predisposes layer II to neurodegenerative pathology. In principle, our computational pipeline can be applied to high-throughput analysis of many forms of neuroanatomical data. Our results support the hypothesis that differences in gene expression contribute to functional specialization of superficial layers of the MEC and dorsoventral organization of the scale of spatial representations

Dorsoventral organization of gene expression in MEC.

<p>(A) Boxes overlaid on MEC region of the central reference image indicate the regions from which pixel intensity was measured and a mean calculated. Pixel intensity was also measured from the adjacent lateral image if present in the re-registered ABA data set. Intensities for dorsal and ventral regions were averaged across the 2 planes to give a mean (mINT) in the dorsal and ventral regions. (B) Average images indicate mean expression patterns for genes with higher dorsal (D>V) and higher ventral (V>D) expression that have images in the central plane. Genes were classified based on the following criteria: All: mINT^MEC ≥ 2, D>V: log₂(V/D) mINT ≤ -0.2630, V>D: log₂(V/D) mINT ≥ 0.2630. (C) Identification of dorsoventrally patterned genes using Cuffdiff 2 differential expression analysis [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004032#pcbi.1004032.ref058" target="_blank">58</a>] of RNA-Seq data. Scatterplot shows log₂(Ventral FPKM/Dorsal FPKM) as a function of absolute FPKM. Significant genes (FDR < 0.05) are indicated in red. Only genes with mean FPKM ≥ 1 across samples and that had a sufficient number of reads for analysis were included. (D) Scatterplots show log₂(V/D) mINT for ABA images of genes with layer-specific expression as a function of log₂(V/D) FPKM for corresponding RNA-Seq data points. Slopes (<i>m</i>) were obtained using a linear regression analysis. Black outlines indicate genes with FDR < 0.05 using Cuffdiff 2 analysis. (E) The ratio of ventral to dorsal expression is significantly higher for deep-layer specific genes than superficial layer specific genes (1-way ANOVA <i>F</i> = 7.47, <i>p</i> = 0.0008. Post-hoc Tukey’s HSD LII vs. Deep <i>p</i> = 0.0016, LIII vs. Deep: <i>p</i> = 0.010).</p

Laminar and intralaminar organization of MEC defined by differential gene expression.

<p>(A) Composite image shows high-intensity pixels for 6 exemplar genes with layer-specific expression. Pixels outside of the MEC and parasubicular region are made semitransparent. Images for the genes <i>Dcc</i> and <i>Wfs1</i> were re-registered for a second time after manual pre-processing to improve the quality of registration for this figure. (B) Schematic showing a refined genomic atlas of the layers and borders of MEC. The region between layer III and V is the lamina dissecans (LD).</p

Differential gene expression defines the borders of MEC with neighboring regions.

<p>(A) Boxes indicate the dorsal (orange) and ventral (red) regions from which mean pixel intensities (mINT) were extracted for identification of genes defining the dorsal border of the MEC. (B) Example cropped ISH images downloaded from the ABA API are shown (left) adjacent to mean expression pattern for genes expressed in MEC but not the wedge-shaped region dorsal to MEC (right). Arrows indicate regional borders. (C) Raw ISH images show the dorsal border of MEC layer II marked out by the gene Nov at different medio-lateral positions (left) and in the coronal plane (right). Values in mm indicate distance from Bregma. (D) Example cropped ISH images downloaded from the ABA API are shown for genes expressed in the wedge-shaped region dorsal to MEC but not the most dorsal MEC region. (E) Raw ISH sagittal (Sag) and coronal (Cor) images from the ABA indicate the continuity of the wedge-shaped region with the more medial parasubicular region for the genes <i>Igfbp6</i> and <i>Kctd16</i>. (F) Boxes highlighting regions used to identify the ventral border of the MEC. See (A). (G-H) Example cropped ISH images downloaded from the ABA API (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004032#sec004" target="_blank">Materials and Methods</a>) are shown adjacent to images of the mean expression pattern for genes expressed in MEC but not the more ventral region (G), or in the ventral region but not MEC (H).</p

Molecular similarity between neocortex and MEC is greater for deep than superficial layers.

<p>(A) Mean expression patterns of layer-specific genes with images in the ABA re-registered data set corresponding to the central reference plane. Scale bar 1mm. (B) Schematic of the central reference image showing the MEC, visual and SS regions overlaid by a color-coded map representing the normalized distance from the inner white matter (0) to the brain surface (1). Pixel intensities were extracted from all locations and binned into 20 groups according to normalized distance. (C) Plots of the distribution of pixel intensities for each MEC layer-specific gene group (see (A)) as a function of distance from the inner white matter border. Error bars represent standard error of the mean. There is a main fixed effect of layer-specific group on neocortical expression (Mixed Model Analysis, <i>F</i> = 22, <i>p</i> < 0.001). Arrows indicate regions of deep and superficial (Sup) neocortex. Laminar boundaries were estimated using individual and mean expression profiles of MEC and SS layer-specific genes (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004032#pcbi.1004032.s005" target="_blank">S5 Fig.</a>). (D) Genes with deep layer-specific expression in MEC show significantly more similar expression patterns in their equivalent neocortical layers than in superficial layers (MANOVA, Overall effect of layer-specific group: <i>F_(4,174)</i> = 3.3, <i>p</i> = 0.012; Between-subjects effects of layer-specific group: Vis <i>F</i> = 6.7, p = 0.002; SS: <i>F</i> = 6.0, p = 0.004. Tukey’s HSD Vis: Deep < LII: <i>p</i> = 0.002; Deep vs. LIII: <i>p</i> = 0.051; SS Deep < LII: <i>p</i> = 0.04; Deep vs. LIII: <i>p</i> = 0.051). (E) Correlation matrix color represents the Pearson’s correlation coefficient (<i><b>r</b></i>) between mean pixel intensity (mINT) in particular layers of MEC, visual and SS cortices for all genes in the re-registered ABA data set.</p

Functionally grouping of genes with laminar organization.

<p>(A) Enriched functional clusters of gene ontology (GO) and KEGG pathway terms. Overrepresented annotations were identified using an overrepresentation analysis in GOElite [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004032#pcbi.1004032.ref102" target="_blank">102</a>]. Colors reflect terms clustered based on kappa similarity [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004032#pcbi.1004032.ref103" target="_blank">103</a>]. Only the most significant annotations (FDR < 0.01) that are sufficiently different from other terms in cluster (kappa score < 0.7) are shown. (B) Colors indicate the log₂ fold enrichment of the term in the layer-specific list, or lists for individual layers, compared with all MEC-expressed genes. Asterisks indicate significance at * α = 0.05, ** α = 0.01, *** α = 0.001. (C) Colors represent the normalized proportion of high-intensity (≥ 2 x mINT^MEC) pixels in each layer for genes in each of the indicated overrepresented groups. Data were normalized by dividing the proportion for each layer by the sum across layers.</p

Large-scale extraction of MEC gene expression data.

<p>(A) Generation of a reference image (Im_ref) for image warping. Images were selected (left), aligned to a template image (red dotted line) by scaling, rotation and translation (Manual Rigid Reg, centre), then registered to each other using non-linear deformation (MIRT Groupwise Reg, right). Im_ref was defined as the median of the resulting images (Median, right). (B) The central reference image (Im_ref^C, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004032#pcbi.1004032.s001" target="_blank">S1A Fig.</a>). (C) Image registration reveals laminar organization of gene expression. Images show the mean pixel intensity (INT) for 1000 ABA expression images before (left) and after (right) registration of the corresponding pre-processed ISH image to Im_ref^C. Colors represent pixel intensity (Colormap adapted from the Matplotlib ‘jet’ colormap). White boxes outline the area corresponding to the MEC, shown at higher magnification. (D) Venn diagrams indicate the number of genes detected as expressed in the MEC using RNA-seq analysis (Ensembl v73) and/or our re-registered ABA data set. (E) 2D histogram indicating the number of genes with a particular FPKM and mINT, represented using a log scale (right). White line is the linear regression fit, which indicates that RNA-seq and ABA expression are positively correlated. Data were averaged across the dorsal and ventral region. Data with zero values are included in the first histogram bin.</p