A Statistically Representative Atlas for Mapping Neuronal Circuits in the Drosophila Adult Brain

Published: 23 March 2018The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fninf.2018.00013/full#supplementary-material Supplementary Figure 1. 3D renderings of the 14 regions used for quantitative evaluation of atlas performances in segmentation and registration tasks. The 14 regions shown here were extracted from the atlas of Ito et al. (2014) that has been registered onto the group-wise inter-sex atlas (available from http://fruitfly.tefor.net). Supplementary Figure 2. Selected lines from the Janelia Farm collection showing an overlap value with the search pattern ranking among the first 50 for at least three of the five PDF profiles. (Left) GAL4-driven GFP profile registered on the standard brain. (Right) overlap between the first PDF profile and the GAL4-driven GFP profile. Numbers refer to Janelia Farm lines with associated gene names. Scale bar: 20 μm. Supplementary Table 1. Results of the 3D space query for each of the five PDF profiles. Overlap values are indicated for each Janelia Farm line and the corresponding gene name (FlyBase nomenclature) is indicated for the overlap values ranking among the first 50 for at least three of the five PDF profiles (blue). Bold names correspond to the three lines shown in Figure 10. Supplementary Movie 1. Animated rendering of the group-wise inter-sex atlas. Successively: nc82 template image (2D sections then 3D volume rendering, opaque then transparent); label image (3D surface rendering of anatomical regions, defined following Ito et al. 2014); six registered patterns of GAL4-GFP expression (3D surface rendering of intensity-thresholded pattern images); same patterns (left half of the brain) with the anatomical regions (right half of the brain).Imaging the expression patterns of reporter constructs is a powerful tool to dissect the neuronal circuits of perception and behavior in the adult brain of Drosophila, one of the major models for studying brain functions. To date, several Drosophila brain templates and digital atlases have been built to automatically analyze and compare collections of expression pattern images. However, there has been no systematic comparison of performances between alternative atlasing strategies and registration algorithms. Here, we objectively evaluated the performance of different strategies for building adult Drosophila brain templates and atlases. In addition, we used state-of-the-art registration algorithms to generate a new group-wise inter-sex atlas. Our results highlight the benefit of statistical atlases over individual ones and show that the newly proposed inter-sex atlas outperformed existing solutions for automated registration and annotation of expression patterns. Over 3,000 images from the Janelia Farm FlyLight collection were registered using the proposed strategy. These registered expression patterns can be searched and compared with a new version of the BrainBaseWeb system and BrainGazer software. We illustrate the validity of our methodology and brain atlas with registration-based predictions of expression patterns in a subset of clock neurons. The described registration framework should benefit to brain studies in Drosophila and other insect species.IA-C, TM, NM, FS, and AJ were funded by the Tefor Infrastructure under the Investments for the Future program of the French National Research Agency (Grant #ANR-11-INBS-0014). FR was supported by INSERM. Work at Institut des Neurosciences Paris-Saclay was supported by ANR Infrastructure Tefor and by ANR ClockEye(#ANR-14-CE13-0034-01). JI was supported by the Spanish Ministry of Economy and Competitiveness (TEC2014-51882-P), the European Union's Horizon 2020 research and innovation programme (Marie Sklodowska-Curie grant 654911, project THALAMODEL), and the European Research Council (ERC Starting Grant no. 677697 BUNGEE-TOOLS). VRVis (KB, FS) is funded by BMVIT, BMWFW, Styria, SFG and Vienna Business Agency in the scope of COMET - Competence Centers for Excellent Technologies (854174) which is managed by FFG. The Institut Jean-Pierre Bourgin benefits from the support of the LabEx Saclay Plant Sciences-SPS (#ANR-10-LABX-0040-SPS)

Multi-modal image registration and atlas formation

Medical images of human anatomy can be produced from a wide range of sensor technologies and imaging techniques resulting in a diverse array of imaging modalities, such as magnetic resonance and computed tomography. The physical properties of the image acquisition process for different modalities elicit different tissue structures. Images from multiple modalities provide complementary information about underlying anatomical structure. Understanding anatomical variability is often important in studying disparate population groups and typically requires robust dense image registration. Traditional image registration methods involve finding a mapping between two scalar images. Such methods do not exploit the complementary information provided by sets of multi-modal images. This dissertation presents a Bayesian framework for generating inter-subject large deformation transformations between two multi-modal image sets of the brain. The estimated transformations are generated using maximal information about the underlying neuroanatomy present in each of the different modalities. This modality independent registration framework is achieved by jointly estimating the posterior probabilities associated with the multi-modal image sets and the high-dimensional registration transformations relating these posteriors. To maximally use the information present in all the modalities for registration, Kullback-Leibler divergence between the estimated posteriors is minimized. This framework is extended to large deformation multi-class posterior atlas estimation. The method generates a representative anatomical template from an arbitrary number of topologically similar multi-modal image sets. The generated atlas is the class posterior that requires the least amount of deformation energy to be transformed into every class posterior (each characterizing a multi-modal image set). This method is computationally practical in that computation times grows linearly with the number of image sets. The multi-class posterior atlas formation method is applied to a database of multi-modal images from ninety-five adult brains as part of a healthy aging study to produce 4D spatiotemporal atlases for the female and male subpopulations. The stability of the atlases is evaluated based on the entropy of their class posteriors. Global volumetric trends and local volumetric change are evaluated. This multi-modal framework has potential applications in many natural multi-modal imaging environments

Automated morphometric analysis and phenotyping of mouse brains from structural µMR images

In light of the utility and increasing ubiquity of mouse models of genetic and neurological disease, I describefully automated pipelines for the investigation of structural microscopic magnetic resonance images of mouse brains – for both high-throughput phenotyping, and monitoring disease. Mouse models offer unparalleled insight into genetic function and brain plasticity, in phenotyping studies; and neurodegenerative disease onset and progression, in therapeutic trials. I developed two cohesive, automatic software tools, for Voxel- and Tensor-Based Morphometry (V/TBM) and the Boundary Shift Integral (BSI), in the mouse brain. V/TBM are advantageous for their ability to highlight morphological differences between groups, without laboriously delineating regions of interest. The BSI is a powerful and sensitive imaging biomarker for the detection of atrophy. The resulting pipelines are described in detail. I show the translation and application of open-source software developed for clinical MRI analysis to mouse brain data: for tissue segmentation into high-quality, subject-specific maps, using contemporary multi-atlas techniques; and for symmetric, inverse-consistent registration. I describe atlases and parameters suitable for the preclinical paradigm, and illustrate and discuss image processing challenges encountered and overcome during development. As proof of principle and to illustrate robustness, I used both pipelines with in and ex vivo mouse brain datasets to identify differences between groups, representing the morphological influence of genes, and subtle, longitudinal changes over time, in particular relation to Down syndrome and Alzheimer’s disease. I also discuss the merits of transitioning preclinical analysis from predominately ex vivo MRI to in vivo, where morphometry is still viable and fewer mice are necessary. This thesis conveys the cross-disciplinary translation of up-to-date image analysis techniques to the preclinical paradigm; the development of novel methods and adaptations to robustly process large cohorts of data; and the sensitive detection of phenotypic differences and neurodegenerative changes in the mouse brai

Distance-based analysis of dynamical systems and time series by optimal transport

The concept of distance is a fundamental notion that forms a basis for the orientation in space. It is related to the scientific measurement process: quantitative measurements result in numerical values, and these can be immediately translated into distances. Vice versa, a set of mutual distances defines an abstract Euclidean space. Each system is thereby represented as a point, whose Euclidean distances approximate the original distances as close as possible. If the original distance measures interesting properties, these can be found back as interesting patterns in this space. This idea is applied to complex systems: The act of breathing, the structure and activity of the brain, and dynamical systems and time series in general. In all these situations, optimal transportation distances are used; these measure how much work is needed to transform one probability distribution into another. The reconstructed Euclidean space then permits to apply multivariate statistical methods. In particular, canonical discriminant analysis makes it possible to distinguish between distinct classes of systems, e.g., between healthy and diseased lungs. This offers new diagnostic perspectives in the assessment of lung and brain diseases, and also offers a new approach to numerical bifurcation analysis and to quantify synchronization in dynamical systems.LEI Universiteit LeidenNWO Computational Life Sciences, grant no. 635.100.006Analyse en stochastie