Dependencies between stimuli and spatially independent fMRI sources: Towards brain correlates of natural stimuli

Ylipaavalniemi, Jarkko; Savia, Eerika; Malinen, Sanna; Hari, Riitta; Vigário, Ricardo; Kaski, Samuel

doi:10.1016/j.neuroimage.2009.03.056

Cited by 29 publications

(26 citation statements)

References 19 publications

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“…Preprocessing of the fMRI data included realignment, normalization with skull stripping, and smoothing. For additional details on the measurements and applied preprocessing, see [18]. After preprocessing, the dimensionality was reduced to 40 by spatial independent component analysis (ICA) that identified spatial brain activation patterns related to various aspects of the stimuli.…”

Section: Fmri Datamentioning

confidence: 99%

Focused Multi-task Learning Using Gaussian Processes

Leen

Peltonen

Kaski

2011

Machine Learning and Knowledge Discovery in Databases

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Abstract. Given a learning task for a data set, learning it together with related tasks (data sets) can improve performance. Gaussian process models have been applied to such multi-task learning scenarios, based on joint priors for functions underlying the tasks. In previous Gaussian process approaches, all tasks have been assumed to be of equal importance, whereas in transfer learning the goal is asymmetric: to enhance performance on a target task given all other tasks. In both settings, transfer learning and joint modelling, negative transfer is a key problem: performance may actually decrease if the tasks are not related closely enough. In this paper, we propose a Gaussian process model for the asymmetric setting, which learns to "explain away" non-related variation in the additional tasks, in order to focus on improving performance on the target task. In experiments, our model improves performance compared to single-task learning, symmetric multi-task learning using hierarchical Dirichlet processes, and transfer learning based on predictive structure learning.

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Section: Fmri Datamentioning

confidence: 99%

Focused Multi-task Learning Using Gaussian Processes

Leen

Peltonen

Kaski

2011

Machine Learning and Knowledge Discovery in Databases

Self Cite

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“…Preprocessing of the fMRI data included realignment, normalisation with skull stripping, and smoothing. For additional details on the measurements and applied preprocessing, see Ylipaavalniemi et al (2009). After preprocessing, the dimensionality was reduced to 40 by spatial independent component analysis (ICA) that identified spatial brain activation patterns related to various aspects of the stimuli.…”

Section: Fmri Datamentioning

confidence: 99%

Focused multi-task learning in a Gaussian process framework

2012

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Multi-task learning, learning of a set of tasks together, can improve performance in the individual learning tasks. Gaussian process models have been applied to learning a set of tasks on different data sets, by constructing joint priors for functions underlying the tasks. In these previous Gaussian process models, the setting has been symmetric in the sense that all the tasks have been assumed to be equally important, whereas in settings such as transfer learning the goal is asymmetric, to enhance performance in a target task given the other tasks. We propose a focused Gaussian process model which introduces an "explaining away" model for each of the additional tasks to model their non-related variation, in order to focus the transfer to the task-of-interest. This focusing helps reduce the key problem of negative transfer, which may cause performance to even decrease if the tasks are not related closely enough. In experiments, our model improves performance compared to single-task learning, symmetric multi-task learning using hierarchical Dirichlet processes, transfer learning based on predictive structure learning, and symmetric multi-task learning with Gaussian processes.

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“…We have earlier proposed a two-step approach [5,7], where ICA was first used to identify spatially independent brain patterns. As the second step, temporal dependencies between stimuli and the brain patterns were detected using CCA or some CCA-variant, like DeCA.…”

Section: Application To Fmri Analysismentioning

confidence: 99%

“…This framework has been studied in two earlier papers, [5] and [7], one of which used non-parametric version of DeCA [2] and the other CCA for the dependency exploration step. We applied the new DeCA-variant to the data from study [5] where the fMRI measurements originally come from [8].…”

Section: Application To Fmri Analysismentioning

confidence: 99%