Parcellating connectivity in spatial maps

PeerJ
Christopher BaldassanoLi Fei-Fei

Abstract

A common goal in biological sciences is to model a complex web of connections using a small number of interacting units. We present a general approach for dividing up elements in a spatial map based on their connectivity properties, allowing for the discovery of local regions underlying large-scale connectivity matrices. Our method is specifically designed to respect spatial layout and identify locally-connected clusters, corresponding to plausible coherent units such as strings of adjacent DNA base pairs, subregions of the brain, animal communities, or geographic ecosystems. Instead of using approximate greedy clustering, our nonparametric Bayesian model infers a precise parcellation using collapsed Gibbs sampling. We utilize an infinite clustering prior that intrinsically incorporates spatial constraints, allowing the model to search directly in the space of spatially-coherent parcellations. After showing results on synthetic datasets, we apply our method to both functional and structural connectivity data from the human brain. We find that our parcellation is substantially more effective than previous approaches at summarizing the brain's connectivity structure using a small number of clusters, produces better generalization...Continue Reading

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Citations

Apr 14, 2016·Cerebral Cortex·Christopher BaldassanoLi Fei-Fei
Jan 26, 2018·Nature Communications·Richard F BetzelDanielle S Bassett
Oct 9, 2019·Topics in Magnetic Resonance Imaging : TMRI·David HuntColin Studholme
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Aug 12, 2019·PeerJ. Computer Science·Reza ArfaParvaneh Shabanzadeh
Jun 5, 2021·NeuroImage·Kristoffer J AlbersMorten Mørup
Sep 1, 2021·BMC Bioinformatics·Leila BaghaarabaniBahram Goliaei

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Software Mentioned

MATLAB
probtrackx2
FSL
KML
bedpostx

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