Dynamic brain activity during associative learning examined with MEG/fMRI co-processing
Sangeeta NairiD, Jane B. AllendorferiD, Yingying WangiD, Jerzy P. SzaflarskiiD
Abstract
BackgroundDue to limitations of individual neuroimaging methods we examine spatial and temporal contributions to self-generation using multimodality imaging with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) within the Bayesian framework Multiple Sparse Priors (MSP). New Method24 healthy participants performed an fMRI and MEG paired-associate learning task. FMRI data were processed within Group ICA fMRI Toolbox. Independent components (ICs) were temporally sorted by task time series (|r|>0.30 threshold identified task-related ICs). Task-positive ("generate") ICs were retained as spatial priors for MEG analyses. MEG data were processed by averaging trials to increase the signal-to-noise ratio within subjects and with an event-related theta power approach. MEG source reconstructions were constrained within the task-positive ICs for both analytical approaches. ResultsFor fMRI, five networks were identified as task-related. Four ICs underlying active generation spanned bilateral parietal, orbitofrontal, medial frontal and superior temporal regions, and occipital lobe. FMRI-constrained MEG source reconstructions yielded early visual cortex activity followed by left inferior frontal gyrus (IFG) and orbito-frontal cortex (OFC) recruitment to coalesce in the left inferior temporal lobe. For the event-related theta approach, reconstructions showed a progression of activity from bilateral temporal areas to left OFC and middle temporal gyrus, followed by right IFG. Comparison with Existing MethodsMSP analyses informed by fMRI produced more focused regional activity than reconstructions without priors suggesting increased attention and maintenance when selecting relevant semantic information during active generation. ConclusionsConstraining MEG source reconstruction to fMRI priors during active generation implicates interconnected fronto-temporal and fronto-parietal networks across time.
02 /Specification & Classification
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