Interoperable atlases of the human brain

Abstract : The last two decades have seen an unprecedented development of human brain mapping approaches at various spatial and temporal scales. Together, these have provided a large fundus of information on many different as-pects of the human brain including micro-and macrostructural segregation, regional specialization of function, connectivity, and temporal dynamics. Atlases are central in order to integrate such diverse information in a topo-graphically meaningful way. It is noteworthy, that the brain mapping field has been developed along several major lines such as structure vs. function, postmortem vs. in vivo, individual features of the brain vs. population-based aspects, or slow vs. fast dynamics. In order to understand human brain organization, however, it seems inevitable that these different lines are integrated and combined into a multimodal human brain model. To this aim, we held a workshop to determine the constraints of a multi-modal human brain model that are needed to enable (i) an integration of different spatial and temporal scales and data modalities into a common reference system, and (ii) efficient data exchange and analysis. As detailed in this report, to arrive at fully interoperable atlases of the human brain will still require much work at the frontiers of data acquisition, analysis, and represen-tation. Among them, the latter may provide the most challenging task, in particular when it comes to representing features of vastly different scales of space, time and abstraction. The potential benefits of such endeavor, however, clearly outweigh the problems, as only such kind of multi-modal human brain atlas may provide a starting point from which the complex relationships between structure, function, and connectivity may be explored.
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Article dans une revue
NeuroImage, Elsevier, 2014, 99 (1), pp.8. 〈10.1016/j.neuroimage.2014.06.010〉
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K Amunts, M.J. Hawrylycz, David C. Van Essen, Jd Van Horn., N Harel, et al.. Interoperable atlases of the human brain. NeuroImage, Elsevier, 2014, 99 (1), pp.8. 〈10.1016/j.neuroimage.2014.06.010〉. 〈hal-01094749〉



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