1. The Structural Connectome in Children, Made Easy eEdE-197 ASNR 54 th Annual Meeting, Washington DC, May 23-26, 2016 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

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: 1.Nodes: subdividing the brain into discrete subunits: ̶High resolution T1-WI 2.Edges: structural connection between any pairs of gray mater regions: ̶DWI/DTI Each step entails choices that can influence the final results

6. Nodes 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 Anatomical landmarks (sulci and gyri) Postmortem cyto- and myelo-architectonic segmentations 200- and 400-unit functional parcellations Craddock RC et al, Nat Methods, 2013

7. Atlases of brain areas generated using anatomical parcellation schemes: 1.Lausanne2008 atlas with 66 cortical regions – also known as the Desikan-Killiany atlas 2.Lausanne2008 atlas with 120 cortical regions 3.Lausanne2008 atlas with 250 cortical regions 4.Lausanne2008 atlas with 500 cortical regions Different parcellations of the human brain 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. (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 Estimating fiber orientation 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: 1.Overlaid on high resolution axial T1-WI 2.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. 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 Connectome building: Step by step 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: 1.Segregation (local integration) 2.Global integration 3.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 Network measureDefinition Measures of local connectivity: 1. Clustering coefficientA measure of local segregation or efficiency, measures the density of connections between the node neighbors 2. TransitivityA normalized variant of clustering coefficient not influenced by nodes with low degree 3. ModularityDecomposability of the system into smaller subsystems, e.g community structure Measures of global connectivity: 1. Characteristic path length A measure of network integration, is the average shortest path length between all pairs of nodes in the network. Short path are likely to be most effective for inter-node communication Measures of influence and centrality: 1. DegreeNumber of edges connecting it to the rest of the network 2. HubImportant nodes highly connected to the rest of the network, facilitate global integrative processes 3. Betweenness centralityFraction of all shortest paths in the network that pass through the node Measures of resilience: 1. Degree distribution The distribution of degrees over all nodes in the network. Brain graphs typically have a broad-scale degree distribution, implying that at least a few “hubs” will have high degree 2. Assortativity coefficient Measure of resilience and is the correlation coefficient for the degree of neighboring nodes. Networks with a positive assortativity coefficient are resilient. Networks with a negative assortativity are likely vulnerable Other: 1. Small-worldness The combination of high clustering and short characteristic path length; also defined as the combination of high global and local efficiency of information transfer between nodes of a network

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