A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series

• Verfasst von: Demanuele, Charmaine [VerfasserIn]
• Verfasst von: Kirsch, Peter [VerfasserIn]
• Verfasst von: Meyer-Lindenberg, Andreas [VerfasserIn]
• Titel: A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series
• Verf.angabe: Charmaine Demanuele, Florian Bähner, Michael M. Plichta, Peter Kirsch, Heike Tost, Andreas Meyer-Lindenberg and Daniel Durstewitz
• E-Jahr: 2015
• Jahr: 07 October 2015
• Umfang: 14 S.
• Fussnoten: Gesehen am 25.01.2018
• Titel Quelle: Enthalten in: Frontiers in human neuroscience
• Ort Quelle: Lausanne : Frontiers Research Foundation, 2008
• Jahr Quelle: 2015
• Band/Heft Quelle: 9(2015), Artikel-ID 537, Seite 1-14
• ISSN Quelle: 1662-5161
• DOI: doi:10.3389/fnhum.2015.00537
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Classifiers
• Sach-SW: Decision Making
• Sach-SW: discriminant analysis
• Sach-SW: Hidden Markov Models
• Sach-SW: machine learning
• Sach-SW: multivariate pattern analysis
• Sach-SW: Prefrontal Cortex
• Sach-SW: working memory
• K10plus-PPN: 1567692036

Abstract

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from fMRI blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.