Brain parcellation based on information theory

Bonmati, Ester; Bardera, Anton; Boada, Imma

doi:10.1016/j.cmpb.2017.07.012

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…Bonmati et al implemented a novel and efficient framework based on information theory that models brain regions as a random walk on the connectome by applying a Markov process in which the different nodes refer to brain regions. They also used an agglomerative information bottleneck technique to cluster functional or anatomical patches by minimizing loss of information through computing mutual information as the similarity metric [24].…”

Section: Related Workmentioning

confidence: 99%

A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning

Kazemivash

2021

Preprint

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ObjectiveBrain parcellation is an essential aspect of computational neuroimaging research and deals with segmenting the brain into (possibly overlapping) sub-regions employed to study brain anatomy or function. In the context of functional parcellation, brain organization which is often measured via temporal metrics such as coherence, is highly dynamic. This dynamic aspect is ignored in most research, which typically applies anatomically based, fixed regions for each individual, and can produce misleading results.MethodsIn this work, we propose a novel spatio-temporal-network (5D) brain parcellation scheme utilizing a deep residual network to predict the probability of each voxel belonging to a brain network at each point in time.ResultsWe trained 53 4D brain networks and evaluate the ability of these networks to capture spatial and temporal dynamics as well as to show sensitivity to individual or group-level variation (in our case with age).ConclusionThe proposed system generates informative spatio-temporal networks that vary not only across individuals but also over time and space.SignificanceThe dynamic 5D nature of the developed approach provides a powerful framework that expands on existing work and has potential to identify novel and typically ignored findings when studying the healthy and disordered brain.

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Section: Related Workmentioning

confidence: 99%

A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning

Kazemivash

2021

Preprint

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“…In this paper, we use a brain network model, where regions correspond to states of a Markov process, to model impulses as random walks on the brain network [ 38 ]. Please note that this model differs from the previous ones [ 24 , 27 , 28 ], where correlations between subsets are used to study the centrality and segregation.…”

Section: Introductionmentioning

confidence: 99%

Novel Brain Complexity Measures Based on Information Theory

Bonmati

Bardera

Feixas

et al. 2018

Entropy

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Brain networks are widely used models to understand the topology and organization of the brain. These networks can be represented by a graph, where nodes correspond to brain regions and edges to structural or functional connections. Several measures have been proposed to describe the topological features of these networks, but unfortunately, it is still unclear which measures give the best representation of the brain. In this paper, we propose a new set of measures based on information theory. Our approach interprets the brain network as a stochastic process where impulses are modeled as a random walk on the graph nodes. This new interpretation provides a solid theoretical framework from which several global and local measures are derived. Global measures provide quantitative values for the whole brain network characterization and include entropy, mutual information, and erasure mutual information. The latter is a new measure based on mutual information and erasure entropy. On the other hand, local measures are based on different decompositions of the global measures and provide different properties of the nodes. Local measures include entropic surprise, mutual surprise, mutual predictability, and erasure surprise. The proposed approach is evaluated using synthetic model networks and structural and functional human networks at different scales. Results demonstrate that the global measures can characterize new properties of the topology of a brain network and, in addition, for a given number of nodes, an optimal number of edges is found for small-world networks. Local measures show different properties of the nodes such as the uncertainty associated to the node, or the uniqueness of the path that the node belongs. Finally, the consistency of the results across healthy subjects demonstrates the robustness of the proposed measures.

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