Neuroscience & Biobehavioral ReviewsNeurosciencePeer-reviewed journal article
Multisensory approach in Mental Imagery: ALE meta-analyses comparing Motor, Visual and Auditory Imagery
Elise E Van Caenegem, Marcos Moreno VerdúiD, Baptiste M Waltzing, Gautier HamolineiD, Siobhan Margaret McAteeriD, Lennart FrahmiD, Robert M. HardwickiD
- Citations
- 17
- FWCI
- 1.64
- Field percentile
- top 19%
Abstract
Mental Imagery is a topic of longstanding and widespread scientific interest. Individual studies have typically focused on a single modality (e.g. Motor, Visual, Auditory) of Mental Imagery. Relatively little work has considered directly comparing and contrasting the brain networks associated with these different modalities of Imagery. The present study integrates data from 439 neuroimaging experiments to identify both modality-specific and shared neural networks involved in Mental Imagery. Comparing the networks involved in Motor, Visual, and Auditory Imagery identified a pattern whereby each form of Imagery preferentially recruited higher level associative brain regions involved in the associated real experience. Results also indicate significant overlap in a left-lateralized network including the pre-supplementary motor area, ventral premotor cortex and inferior parietal lobule. This pattern of results supports the existence of a core network that supports the attentional, spatial, and decision-making demands of Mental Imagery. Together these results offer new insights into the brain networks underlying human imagination. HighlightsO_LIMeta-Analyses of Motor, Visual, and Auditory modalities of Imagery were compared C_LIO_LIEach modality recruited high level areas related to associated real-life experience C_LIO_LIA core network of pre-SMA, PMv and IPL were recruited by all imagery modalities C_LIO_LIResults emphasize attentional, spatial, & executive aspects of Mental Imagery C_LI
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: dimensionality reduction
- approach: statistical model
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