2026-04-13T13:45:42Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/155122024-06-18T12:17:23Zcom_2072_452955com_2072_2054col_2072_453069

Brain parcellation based on information theory Bonmatí Coll, Ester Bardera i Reig, Antoni Boada, Imma Ministerio de Economía y Competitividad (Espanya) Informació, Teoria de la Information theory Medicina -- Informàtica Medicine -- Data processing Imatgeria mèdica Imaging systems in medicine Cervell -- Imatges per ressonància magnètica Brain -- Magnetic resonance imaging In computational neuroimaging, brain parcellation methods subdivide the brain into individual regions that can be used to build a network to study its structure and function. Using anatomical or functional connectivity, hierarchical clustering methods aim to offer a meaningful parcellation of the brain at each level of granularity. However, some of these methods have been only applied to small regions and strongly depend on the similarity measure used to merge regions. The aim of this work is to present a robust whole-brain hierarchical parcellation that preserves the global structure of the network. Methods Brain regions are modeled as a random walk on the connectome. From this model, a Markov process is derived, where the different nodes represent brain regions and in which the structure can be quantified. Functional or anatomical brain regions are clustered by using an agglomerative information bottleneck method that minimizes the overall loss of information of the structure by using mutual information as a similarity measure. Results The method is tested with synthetic models, structural and functional human connectomes and is compared with the classic k-means. Results show that the parcellated networks preserve the main properties and are consistent across subjects. Conclusion This work provides a new framework to study the human connectome using functional or anatomical connectivity at different levels This work was supported by the Catalan Government (Grant No. 2014-SGR-1232) and by the Spanish Government (Grant No. TIN2016-75866-C3-3-R) info:eu-repo/date/embargoEnd/2026-01-01 2017-11-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion peer-reviewed http://hdl.handle.net/10256/15512 http://hdl.handle.net/10256/15512 eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cmpb.2017.07.012 info:eu-repo/semantics/altIdentifier/issn/0169-2607 info:eu-repo/semantics/altIdentifier/eissn/1872-7565 MINECO/PE 2016-2019/TIN2016- 75866-C3-3-R Tots els drets reservats info:eu-repo/semantics/embargoedAccess application/pdf Elsevier © Computer Methods and Programs in Biomedicine, 2017, vol. 151, p. 203-212 Articles publicats (D-IMA)