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The Structural Connectome in Children, Made Easy

eEdE-197 . The Structural Connectome in Children, Made Easy. Avner Meoded, Thierry A.G.M. Huisman, Andrea Poretti.

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The Structural Connectome in Children, Made Easy

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  1. eEdE-197  The Structural Connectome in Children, Made Easy Avner Meoded, Thierry A.G.M. Huisman, Andrea Poretti Section of Pediatric Neuroradiology, Division of Pediatric Radiology, Russell H Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD ASNR 54 th Annual Meeting, Washington DC, May 23-26, 2016

  2. Disclosure • We have nothing to disclose • No relevant financial relations interfering with our presentation

  3. Introduction • The structural connectome is a comprehensive description of the network of elements and connections that form the brain • In the last years, this framework has been used increasingly to investigate the developing brain • This educational exhibit aims to discuss the various steps that are needed to reconstruct the pediatric structural connectome

  4. Recipe for connectome reconstruction • The ingredients: The key components of structural connectome are nodes (cortical regions) and edges (measurements of structural association between nodes) • The matrix: The next step is to generate an association matrix by compiling all pairwise associations between nodes • The metrics: Various measures are used to characterize the topological architecture of the brain's structural connectivity; connectomes commonly are assessed for their local and global efficiency • The whole picture: An overview of various visualization methods of the structural connectome will be provided

  5. How to build the structural connectome? • The two key elements in constructing a graphical model of a brain network: • Nodes: subdividing the brain into discrete subunits: • High resolution T1-WI • Edges: structural connection between any pairs of gray mater regions: • DWI/DTI Each step entails choices that can influence the final results

  6. Nodes Anatomical landmarks (sulci and gyri) Postmortem cyto- and myelo-architectonic segmentations 200- and 400-unit functional parcellations • We lack agreement on how to best define the constituent brain units • Ideally, both brain-function and structural-connectivity information should be used to delineate brain areas Craddock RC et al, Nat Methods, 2013

  7. Different parcellations of the human brain Atlases of brain areas generated using anatomical parcellation schemes: • Lausanne2008 atlas with 66 cortical regions – also known as the Desikan-Killiany atlas • Lausanne2008 atlas with 120 cortical regions • Lausanne2008 atlas with 250 cortical regions • Lausanne2008 atlas with 500 cortical regions Meoded A et al, in preparation

  8. Structural connectivity: Edges • DTI MRI  pre processing • Information necessary to estimate the orientation(s) of fibers passing through each voxel  reconstruct large-scale tracts of white matter  tractography Meoded A et al, in preparation

  9. DTI VS. HARDI • Estimating fiber orientation: • DTI vs. HARDI = Ellipsoid vs. ODF (orientation distribution function) • ODF: Samples a direction distribution function at each step to determine the propagation direction • Allows estimation of a probability density of the most likely location of the tract, and thus its spatial uncertainty

  10. Estimating fiber orientation (A) Axial view of DTI fiber orientation estimates. The zoomed area represents one of the most critical “cross-roads” of the human brain: the region where corpus callosum, corona radiata and superior longitudinal fasciculus fibers intersect. (B) Tensor ellipsoid and (C) ODF better estimate fiber trajectories and allow recovery of nondominant pathways invisible to DTI Meoded A et al, in preparation

  11. Capturing more connections with HARDI • 3D depiction of whole brain tractography obtained with HARDI of a 8 year old healthy subject: • Overlaid on high resolution axial T1-WI • Coronal view of whole brain tractography with hemibrain surface • Improved tractography with HARDI captures more connections and render a detailed representation of the white matter tracts Meoded A et al, in preparation

  12. Connectivity and adjacency matrix: compiling all pairwise associations between nodes Weighted Vs. Unweighted: • Connections between regions vary (i.e., are weighted) according to the strength of their interaction. Thresholding Directed Vs. Unidirected / Afferent Vs. Efferent: • Each anatomical connection emanates from a source region and links to a target; each interaction represents the causal influence of the activity in one region on the activity in another. Meoded A et al, in preparation

  13. Connectome building: Step by step Step 1: Define the network nodes, with anatomical parcellation of high resolution T1-WI Step 2: Estimate a continuous measure of association between nodes with structural connectivity, obtained with MR tractography Step 3: Generate an association matrix by compiling all pairwise associations between nodes The result is the structural connectome: a graphical model of a brain network  Meoded A et al, in preparation

  14. The metrics: topological measures • Topology: layout pattern of interconnection • Topology analysis: • Network metrics  global/regional network organization Information about: • Segregation (local integration) • Global integration • Small worldness, centrality (Hubs)

  15. Topology measures: Degree • The degree of a node = Number of edges emanating from that node • High-degree nodes are likely to play an important role in the system’s dynamics

  16. Topology measures: Cluster, Path length and Efficiency Cluster coef./ local efficiency Path length $ $ $ $ $ $ Cost It has been suggested that the spatial layout of neurons or brain regions is economically arranged to minimize axonal volume; Thus, conservation of wiring costs is likely to be an important selection pressure on the evolution of brain networks Bullmore E and Sporns O, Nat Rev Neurosci, 2012

  17. Small worldness: Six degrees of separation • The “small-world” property combines high levels of local clustering among nodes of a network (to form families or cliques) and short paths that globally link all nodes of the network • Small-world organization is intermediate between that of random networks, the short overall path length of which is associated with a low level of local clustering, and that of regular networks or lattices, the high-level of clustering of which is accompanied by a long path length

  18. Hubs and Modules • Hubs are nodes with high degree, or high centrality  crucial to efficient communication • Each module contains several densely interconnected nodes, and there are relatively few connections between nodes in different modules. • Provincial hubs are connected mainly to nodes in their own modules • Connector hubs are connected to nodes in other modules Bullmore E and Sporns O, Nat Rev Neurosci, 2009

  19. Rich club • Within-module connections tend to be shorter than between-module  improve the local efficiency • Rich club = densely interconnected hubs  global efficient information flow • Modularity confers a degree of resilience against dynamic perturbations and small variations in structural connectivity Bullmore E and Sporns O, Nat Rev Neurosci, 2009

  20. Summary of network measures

  21. The whole picture: connectome visualization • The structure of the human brain is easily perceived by looking at two or three-dimensional views • However, the increase interest and popularity in the human connenctome, has established a new neuroimaging dimension: The imaging of networks

  22. Networks in anatomical space: different modules are depicted with different colors, this type of visualization facilitate anatomical interpretation, but less optimal for dense networks Meoded A et al, in preparation

  23. Network visualization with nodes as spheres with different colors and sizes, according to modularity partitioning and hub, respectively Meoded A et al, in preparation

  24. Circular modules all cortical regions depicted as rectangles with size based on degree of a module, connected by weighted edges Circular degree all cortical regions depicted as circles with size according to degree value Graph visualization network in non-anatomical space with different communities, node color represent assigned community Meoded A et al, in preparation

  25. Summary/Conclusions • The human connectome is the culmination of more than a century of conceptual and methodological innovation • In this work we outlined the different steps in pediatric connectome reconstruction as an easy to use pipeline

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