Bayesian multi-task learning for decoding multi-subject neuroimaging data

Decoding models based on pattern recognition (PR) are becoming increasingly important tools for neuroimaging data analysis. In contrast to alternative (mass-univariate) encoding approaches that use hierarchical models to capture inter-subject variability, inter-subject differences are not typically...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Fla.), 2014-05, Vol.92 (100), p.298-311
Hauptverfasser:	Marquand, Andre F., Brammer, Michael, Williams, Steven C.R., Doyle, Orla M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Abbreviations Adolescent Adult Advantages Algorithms Artificial Intelligence Bayes Theorem Biological and medical sciences Brain Mapping - methods Classification Commonality Data processing Decoding Female Functional magnetic resonance imaging Fundamental and applied biological sciences. Psychology Gaussian Gaussian process Humans Image Interpretation, Computer-Assisted - methods Learning Machine learning Male Medical imaging Memory, Episodic Methods Mixed effects Multi-output learning Multi-task learning Neural networks Pattern recognition Pattern Recognition, Automated - methods Pattern Recognition, Visual - physiology Repeated measures Reproducibility of Results Sensitivity and Specificity Tasks Transfer learning Vertebrates: nervous system and sense organs Visual Cortex - physiology Young Adult
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