On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data
Gabriel Girard,
Roberto Caminiti,
Alexandra Battaglia-Mayer,
Etienne St-Onge,
Karen S. Ambrosen,
Simon F. Eskildsen,
Kristine Krug,
Tim B. Dyrby,
Maxime Descoteaux,
Jean-Philippe Thiran,
Giorgio M. Innocenti
Affiliations
Gabriel Girard
Corresponding author at: Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland.; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Center for BioMedical Imaging, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Roberto Caminiti
Neuroscience and Behavior Laboratory, Istituto Italiano di Tecnologia, Rome, Italy
Alexandra Battaglia-Mayer
Department of Physiology and Pharmacology, University of Rome SAPIENZA, Rome, Italy
Etienne St-Onge
Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada
Karen S. Ambrosen
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
Simon F. Eskildsen
Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Kristine Krug
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom; Institute of Biology, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany; Leibniz-Insitute for Neurobiology, Magdeburg, Germany
Tim B. Dyrby
Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
Maxime Descoteaux
Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada
Jean-Philippe Thiran
Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Center for BioMedical Imaging, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Giorgio M. Innocenti
Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Brain and Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from “bottleneck” white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.
