The Brain Hierarchical Atlas Ibai Diez 1*, Paolo Bonifazi 1,2*, Asier Erramuzpe 1, Inaki Escudero1, Beatriz Mateos1, Miguel Angel Munoz3, Lola Boyano4, Sebastiano Stramaglia5,6, Jesus Cortes1,2,4

INTRODUCTION Most used brain atlases are purely anatomical or structural [1-4], and others are purely functional, like the wellknown Brodmann atlas based on lesion studies or data driven methods [5,6]. Although obtaining suitable brain partitions (or atlases) has been studied intensively (see [7] and references therein), to the best of our knowledge, the first brain partition that accounts for subnetworks that are relevant to both structure and function is the Brain Hierarchical Atlas (BHA), published in [8] and presented here.

RESULTS After maximization of the crossmodularity X, we found that the partition into 20 moduli derived from FN (as portrayed in figure 1) was optimal [8]. The Brain Hierarchical Atlas (BHA), composed by these 20 moduli or 'subnetworks' maximizing X, is illustrated in figure 1. We studied the similarity between the BHA and other existing brain partitions. The results are illustrated in figure 2, from top to bottom: (i) The automated anatomical labeling (AAL) [2]; (ii) The resting state networks (RSNs) [5]; and (iii), The Brodmann atlas (included in the software http://www.mricro.com).

The BHA: (i) represents a distinct brain partition from those previously described in the literature; (ii) incorporate distinct both structural and functional brain regions into a single operative subnetwork/unit; (iii) can overlap and share both anatomical and functional brain regions. In the same way as alterations in resting state networks have been reported in several brain pathologies and diseases, we expect that the use of the BHA, with simultaneous focus on structure and function, might help also in diagnosing disease.

1Biocruces Health Research Institute, Cruces University Hospital, Barakaldo, Spain. 2Ikerbasque: The Basque Foundation for Science, Bilbao, Spain. 3Institute Carlos I for Theoretical and Computational Physics, University of Granada, Granada, Spain. 4University of the Basque Country, Leioa, Spain. 5University of Bari, Bari, Italy. 6BCAM - Basque Center for Applied Mathematics, Bilbao, Spain.

*equal contribution

REFERENCES [1] Lancaster J, Woldorff M, Parsons L, Liotti M, Freitas C, Rainey L, Kochunov P, Nickerson D, Mikiten S, and Fox P (2000), 'Automated talairach atlas labels for functional brain mapping', Human Brain Mapping, vol. 10, pp. 120131 [2] TzourioMazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, and Joliot M (2002), 'Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI singlesubject brain', Neuroimage, vol. 15, pp. 273289.

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METHODS 12 Healthy subjects. Age: 33.5 ± 8.7 years. 6 males, 6 females. *Validated with N=30 subjects from WUMinn Human Connectome Project [9]

Largescale brain networks were obtained after magnetic resonance imaging (MRI). Whilst the T1 was mainly used for preprocessing and co-registration, diffusion tensor imaging provided structural networks (SN) of gray matter areas connected by white matter tracts, and variations in the blood oxygenation level dependent T2* signal provided functional networks (FN). Both FN and SN were obtained at a resolution of 2514 brain regions, making FN and SC to be represented by (2514*2514) matrices for each of the subjects. For details on how we built these networks, see [8]. Applying a standard hierarchical clustering algorithm to FN and SN, we compared at the moduli level FN and SN by exploiting their hierarchical modular organization. This approach provided a hierarchical tree or dendrogram in which nodes were progressively merged together into moduli following a nested hierarchy of "vicinity" (which reflects distance in the correlation for FN and distance in

fiber number for SN). More specifically, we compared FN and SN by employing the template of hierarchical modular organization derived from FN to visualize SC, and vice versa. We searched for the best common partition shared by structure and function by maximizing the 'crossmodularity' index X [8], an index which is large for a given brain partition if the corresponding Newman's modularities of the two matrices FN and SN are large and there is also a large within-module similarity between both divisions (i.e., a large fraction of existing intramodule links are shared by both networks). Thus, a large crossmodularity value indicates that both FN and SN are highly modular and, at the same time, the moduli are internally wired in a similar way.

This work was approved by the Ethics Committee at the Cruces University Hospital (Principal Investigator: Jesus M Cortes).

Region 1 Region 2 Region 3 Region 4 Region 5

Region 6

Region 8

Region 10

Region 12

Region 14

Region 16

Region 7

Region 9

Region 11

Region 13

Region 15

Region 17 Region 18 Region 19 Region 20

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CONCLUSIONS

AAL RSN Brodmann

[3] Eickhoff S, Stephan K, Mohlberg H, Grefkes C, Fink G, Amunts K, and Zilles K (2005), 'A new spm toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data', Neuroimage, vol. 25, pp. 13251335. [4] Desikan R, Segonne F, Fischl B, Quinn B, Dickerson B, Blacker D, Buckner R, Dale A, Maguire R, Hyman B, Albert M, and Killiany R. (2006), 'An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest', Neuroimage, vol. 31, pp. 968980. [5] Beckmann CF, DeLuca M, Devlin JT and, Smith SM (2005), 'Investigations into restingstate connectivity using independent component analysis'. Philos. Trans. R. Soc. Lond. B. Biol. Sci., vol. 360, pp. 10011013 [6] Craddock R, James G, Holtzheimer P, Hu X, and Mayberg H (2012), 'A whole brain fmri atlas generated via spatially constrained spectral clustering', Human Brain Mapping, vol. 33, pp. 19141928. [7] Craddock R, Jbabdi S, Yan C, Vogelstein J, Castellanos F, Martino AD, Kelly C, Heberlein K, Colcombe S, and Milham M (2013), 'Imaging human connectomes at the macroscale', Nature Methods, vol. 10, pp. 524539. [8] Diez I, Bonifazi P, Escudero I, Mateos B, Munoz MA, Stramaglia S, and Cortes JM (2015), 'A novel brain partition highlights the modular skeleton shared by structure and function', Scientific Reports, vol. 5, pp. 10532. The atlas can be downloaded at http://www.nitrc.org/projects/biocr_hcatlas/ [9] Van Essen DC et. al. (2013), 'The WUMinn Human Connectome Project: An overview', Neuroimage, vol. 80, pp. 6279

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