About this Research Topic
Bayesian networks are a type of probabilistic graphical models that represent the joint probability distribution over a set of random variables by means of a directed acyclic graph and a list of conditional probabilities. The directed acyclic graph shows the conditional independencies among triplets of variables, allowing for sparse representations of the joint probability distribution. The Bayesian network model can be learnt automatically from data, by means of structure learning algorithms, or alternatively can be given by an expert in the domain to be modelled. Once the model is obtained it constitutes an efficient and effective tool for reasoning. This reasoning is carried out by exact or approximate inference algorithms that propagate the given evidence through the graphical structure.
The discovery of relationships among entities and the inference capabilities of Bayesian networks place them as an appropriate methodology for modelling the underlying uncertainty in neuroscience at three different levels of resolution and with any kind of neuronal characteristics (morphological, electrophysiological, and genetic):
a) Microscopic: spine, synapse, neuron, population of them.
b) Macroscopic: temporal and causal relationships among different brain regions from neuroimaging data (fMRIS, MEG, EEG,...).
c) Clinical: diagnosis, prognosis, and prediction of dementia development in different neurodegenerative diseases: Parkinson, Alzheimer, Huntington,...
This Research Topic aims to receive contributions from researchers from different backgrounds who are either developing new inference and/or learning algorithms for Bayesian networks motivated by neuroscience problems, or applying existing methodologies to new data for the understanding of brain structure, function, and dynamics.
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