Browsing by Author "Mononen, Tommi"

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    Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models
    (2016-06-01) Vehtari, Aki; Mononen, Tommi; Tolvanen, Ville; Sivula, Tuomas; Winther, Ole; Department of Computer Science; Professorship Vehtari Aki; Helsinki Institute for Information Technology (HIIT); Centre of Excellence in Computational Inference, COIN; Technical University of Denmark
    The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integration over the latent values is approximated using the Laplace method or expectation propagation (EP). We study the properties of several Bayesian leave-one-out (LOO) cross-validation approximations that in most cases can be computed with a small additional cost after forming the posterior approximation given the full data. Our main objective is to assess the accuracy of the approximative LOO cross-validation estimators. That is, for each method (Laplace and EP) we compare the approximate fast computation with the exact brute force LOO computation. Secondarily, we evaluate the accuracy of the Laplace and EP approximations themselves against a ground truth established through extensive Markov chain Monte Carlo simulation. Our empirical results show that the approach based upon a Gaussian approximation to the LOO marginal distribution (the so-called cavity distribution) gives the most accurate and reliable results among the fast methods.
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    The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks: A multimodal magnetoencephalography-functional magnetic resonance imaging study
    (FRONTIERS MEDIA SA, 2022-11-03) Mononen, Tommi; Kujala, Jan; Liljeström, Mia; Leppäaho, Eemeli; Kaski, Samuel; Salmelin, Riitta; Department of Computer Science; Department of Neuroscience and Biomedical Engineering; Probabilistic Machine Learning; Helsinki Institute for Information Technology (HIIT); Professorship Kaski Samuel; Computer Science Professors; Computer Science - Artificial Intelligence and Machine Learning (AIML); Finnish Center for Artificial Intelligence, FCAI
    Different neuroimaging methods can yield different views of task-dependent neural engagement. Studies examining the relationship between electromagnetic and hemodynamic measures have revealed correlated patterns across brain regions but the role of the applied stimulation or experimental tasks in these correlation patterns is still poorly understood. Here, we evaluated the across-tasks variability of MEG-fMRI relationship using data recorded during three distinct naming tasks (naming objects and actions from action images, and objects from object images), from the same set of participants. Our results demonstrate that the MEG-fMRI correlation pattern varies according to the performed task, and that this variability shows distinct spectral profiles across brain regions. Notably, analysis of the MEG data alone did not reveal modulations across the examined tasks in the time-frequency windows emerging from the MEG-fMRI correlation analysis. Our results suggest that the electromagnetic-hemodynamic correlation could serve as a more sensitive proxy for task-dependent neural engagement in cognitive tasks than isolated within-modality measures.