One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area (“the functional fingerprint”) is closely related to its anatomical connections (“the connectional fingerprint”) and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation and requires clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several non-trivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. Further insight into the hierarchically modular architecture of cortical subunits is given by revealing coarser cortical structures that differentiate between primary as well as premotoric areas and those associated with pre-frontal areas.

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My name was also written as (Andrew) An Bian due to a name change. My ORCID iD is orcid.org/0000-0002-2368-4084. v -Yatao An Bian, Xiong Li, Yuncai Liu, and Ming-Hsuan Yang (2019b). "Parallel Coordinate Descent Newton Method for Efficient L1-Regularized Loss Minimization." In: IEEE Transactions on Neural Networks and Learning Systems, pp. 3233-3245 -Lie *He, An *Bian, and Martin Jaggi (2018). "COLA: Communication-Efficient Decentralized Linear Learning." In: Advances in Neural Information Processing Systems (NeurIPS), pp. 4537-4547 * Authors contributed equally.

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