Connectome analysis of the human brain structural and functional architecture provides a unique opportunity to understand the organization of brain networks [1]. However, such analyses require an appropriate definition of functional or structural nodes to efficiently represent cortical regions [2-3]. In this work, we investigate a novel large scale parcellation-based connectome, merging together information coming from resting state fMRI (rs-fMRI) data and diffusion tensor imaging (DTI) measurements.Forty right-handed subjects (males/females=23/17, age = 35±10 years (average ± SD)) were enrolled in the study. rs-fMRI data (TR/TE =3000/30 msec, FoV=131.8x131.8 mm$$$^{2}$$$, 50 slices, voxel =2x2x3 mm$$$^{3}$$$, flip=90$$$^{0}$$$, 180 volumes) DTI measurements (TR/TE =10000/70 msec, FoV=224x224 mm$$$^{2}$$$, 70 slices, voxel = 1.56x1.56x2 mm$$$^{3}$$$ resolution, 128 diffusion directions, b-max=1000 sec/mm$$$^{2}$$$) and T1-weighted images (TR/TE=11/5 msec, FoV=224x224 mm$$$^{2}$$$, voxel size=0.54x0.54x0.9 mm$$$^{3}$$$) were recorded at 3T (Philips Achieva, AE Eindhoven The Netherlands) using a 32 channel head coil. T1-weighted images were acquired for anatomical reference and rs-fMRI data were used to establish functional nodes of the network. Functional nodes corresponded to functional parcels of the grey matter obtained by a ICA with hierarchical agglomerative clustering framework. The method and the proposed connectome reconstruction pipeline are detailed in Figure 1. We investigated three parcellation resolution (i.e., number of clusters $$$k$$$ = 100, 200, 500) for the following analysis. Deterministic tractography in diffusion space was calculated (including around 1.2 million streamlines) after motion and eddy current distortion corrections. Post-processing included short fiber elimination (length 20-500 mm). For each subject $$$i = 1,...,N$$$ and each parcellation clustering $$$k = 100, 200, 500$$$ a $$$k\times k$$$ connectivity matrix $$$M$$$ was extracted from the fiber counts across the whole brain as the number of streamline fiber tracts connecting each pair of nodes. Network metrics were computed using Brain Connectivity Toolbox [4]. Given the mathematical representation of a network as a graph $$$\mathit{G} = (\mathit{V},\mathit{E})$$$, composed of the set of nodes $$$\mathit{V}$$$ and set of links $$$\mathit{E}$$$, we considered as $$$\mathit{V}$$$ both the AAL 116 distinct segmented brain structures [5] and the 100, 200 and 500 functional parcels (Figure 2). The group-averaged $$$\bar{M}$$$ was calculated as the average adjacency matrices from all the subjects. Each adjacency matrix was obtained by thresholding each matrix in order to assure at least 75% entries from $$$M$$$.In order to summarize and compare network’s features different graph metrics were calculated: global efficiency, density, mean cluster coefficient, mean betweenness, and smallworldness. Figure 3 shows the topological metrics obtained by the unweighted individual matrices. The plot reveals that by increasing the resolution no significant differences can be found in global efficiency, density, characteristic path length and mean clustering of structural networks, while the centrality properties tend to resemble those typical of functional networks [6].Networks’ topological metrics here used can be divided in two different characterizations of the brain organization: integration (global efficiency, density and characteristic path length), segregation (mean clustering coefficient) and centrality (betweeness and small worldness). The main finding of this work is the description of human connectivity properties at different levels of spatial resolution. The functional parcellation-based organization of cortical and subcortical regions showed that by increasing the resolution no significant differences can be found in integration and segregation properties of structural networks, while the centrality properties tend to resemble those typical of functional networks. Our multi-resolution parcellation seems to lead to a more functionally consistent description of the connectome and to be a possible tool to detect alterations in neurological and psychiatric patients.