Multi-Task Learning for Interpretation of Brain Decoding Models

Kia, Seyed Mostafa; Vega-Pons, Sandro; Olivetti, Emanuele; Avesani, Paolo

doi:10.1007/978-3-319-45174-9_1

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…In qualitative evaluation, to show the superiority of one decoding method over the other (or a univariate map), the corresponding brain maps are compared visually in terms of smoothness, sparseness, and coherency using already known facts (see for example, Varoquaux et al, 2012). In the second approach, important factors in interpretability such as spatio-temporal reproducibility are evaluated to indirectly assess the interpretability of results (see for example, Langs et al, 2011; Rasmussen et al, 2012; Conroy et al, 2013; Kia et al, 2016). Despite partial effectiveness, there is no general consensus regarding the quantification of these intermediate criteria.…”

Section: Introductionmentioning

confidence: 99%

“…Despite partial effectiveness, there is no general consensus regarding the quantification of these intermediate criteria. For example, in the case of spatial reproducibility, different methods such as correlation (Rasmussen et al, 2012; Kia et al, 2016), dice score (Langs et al, 2011), or parameter variability (Conroy et al, 2013; Haufe et al, 2013) are used for quantifying the stability of brain maps, each of which considers different aspects of local or global reproducibility.…”

Section: Introductionmentioning

confidence: 99%

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Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

et al. 2017

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Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

et al. 2017

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“…Therefore, prediction methods that do not give these plausible explanations are much more difficult to use for accumulating neuroscientific knowledge. Generative probabilistic models are generally more immediately interpretable [11,7], and for instance when using linear generative models, the weights point to those brain locations that are activated due to a performed task.…”

Section: Introductionmentioning

confidence: 99%

Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers

Remes,

Mononen,

Kaski

2015

Preprint

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We propose a novel classification model for weak signal data, building upon a recent model for Bayesian multi-view learning, Group Factor Analysis (GFA). Instead of assuming all data to come from a single GFA model, we allow latent clusters, each having a different GFA model and producing a different class distribution. We show that sharing information across the clusters, by sharing factors, increases the classification accuracy considerably; the shared factors essentially form a flexible noise model that explains away the part of data not related to classification. Motivation for the setting comes from single-trial functional brain imaging data, having a very low signal-to-noise ratio and a natural multiview setting, with the different sensors, measurement modalities (EEG, MEG, fMRI) and possible auxiliary information as views. We demonstrate our model on a MEG dataset.

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Multi-Task Learning for Interpretation of Brain Decoding Models

Cited by 2 publications

References 26 publications

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers

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