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Title: Hierarchical Bayesian mixture modeling of resting-state functional brain connectivity: An alternative to thresholding Authors:  Tetiana Gorbach - Umea University (Sweden) [presenting]
Anders Lundquist - USBE, Umea University (Sweden)
Xavier de Luna - Umea University (Sweden)
Lars Nyberg - Umea University (Sweden)
Alireza Salami - Umea University (Sweden)
Abstract: A Bayesian hierarchical mixture model is proposed in order to analyze functional brain connectivity where mixture components represent connected and non-connected brain regions. Such an approach provides a data-informed separation of reliable connections from noise in contrast to arbitrary thresholding of a connectivity matrix. The hierarchical structure of the model allows simultaneous inferences for the entire population and each subject separately. We show that a new connectivity measure, the posterior probability of a given pair of brain regions of a specific subject to be connected given the observed correlation of regions activity, might be superior to correlation. The posterior probability reflects connectivity of a pair of regions relative to the overall connectivity pattern of an individual, which is overlooked in traditional correlation analyses. We also demonstrate that using the posterior probability might diminish the effect of noise on inferences, which is present when a correlation is used as a connectivity measure. Additionally, simulation analyses reveal that the sparsification of the connectivity matrix using the posterior probabilities might outperform the absolute thresholding based on correlations. The applicability of the introduced method is exemplified by a study of functional resting-state brain connectivity in older adults.