Bayesian model selection favors parametric over categorical fMRI subsequent memory models in young and older adults

Subsequent memory paradigms allow to identify neural correlates of successful encoding by separating brain responses as a function of memory performance during later retrieval. In functional magnetic resonance imaging (fMRI), the paradigm typically elicits activations of medial temporal lobe, prefro...

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Bibliographic Details
Published inNeuroImage (Orlando, Fla.) Vol. 230; p. 117820
Main Authors Soch, Joram, Richter, Anni, Schütze, Hartmut, Kizilirmak, Jasmin M., Assmann, Anne, Knopf, Lea, Raschick, Matthias, Schult, Annika, Maass, Anne, Ziegler, Gabriel, Richardson-Klavehn, Alan, Düzel, Emrah, Schott, Björn H.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 15.04.2021
Elsevier Limited
Elsevier
Subjects
Age
Aging
Bayesian analysis
Bayesian model selection
Brain mapping
Brain research
episodic memory
Experiments
Functional magnetic resonance imaging
Magnetic resonance imaging
Mathematical models
Memory
Neuroimaging
Older people
parametric fMRI
subsequent memory effect
Temporal lobe
episodic memory
Bayesian model selection
subsequent memory effect
aging
parametric fMRI
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Summary:Subsequent memory paradigms allow to identify neural correlates of successful encoding by separating brain responses as a function of memory performance during later retrieval. In functional magnetic resonance imaging (fMRI), the paradigm typically elicits activations of medial temporal lobe, prefrontal and parietal cortical structures in young, healthy participants. This categorical approach is, however, limited by insufficient memory performance in older and particularly memory-impaired individuals. A parametric modulation of encoding-related activations with memory confidence could overcome this limitation. Here, we applied cross-validated Bayesian model selection (cvBMS) for first-level fMRI models to a visual subsequent memory paradigm in young (18–35 years) and older (51–80 years) adults. Nested cvBMS revealed that parametric models, especially with non-linear transformations of memory confidence ratings, outperformed categorical models in explaining the fMRI signal variance during encoding. We thereby provide a framework for improving the modeling of encoding-related activations and for applying subsequent memory paradigms to memory-impaired individuals.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2021.117820