Publication

Brain Decoding Of Fmri Connectivity Graphs Using Decision Tree Ensembles

Jonas Richiardi
2010
Conference Papers

Abstract

Functional connectivity analysis of fMRI data can reveal synchronized activity between anatomically distinct brain regions. Here, we exploit the characteristic connectivity graphs of task and resting epochs to perform classification between these conditions. Our approach is based on ensembles of decision trees, which combine powerful discriminative ability with interpretability of results. This makes it possible to extract discriminative graphs that represent a subset of the connections that distinguish best between the experimental conditions. Our experimental results also show that the method can be applied for group-level brain decoding.

Official source

https://infoscience.epfl.ch/entities/publication/15f728a0-f348-44ad-9b5e-fd6a0491af66

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Jonas Richiardi
2010
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/15f728a0-f348-44ad-9b5e-fd6a0491af66

About this result

Related concepts (32)

Decision tree learning

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels.

Brain-reading

Brain-reading or thought identification uses the responses of multiple voxels in the brain evoked by stimulus then detected by fMRI in order to decode the original stimulus. Advances in research have made this possible by using human neuroimaging to decode a person's conscious experience based on non-invasive measurements of an individual's brain activity. Brain reading studies differ in the type of decoding (i.e. classification, identification and reconstruction) employed, the target (i.e.

Ensemble learning

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.

Related publications (20)

Brain Decoding Of Fmri Connectivity Graphs Using Decision Tree Ensembles

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