bioRxivNeurosciencePreprint
Shared and distinct default mode systems in mental time travel and affective experience over time
Yuan Zhang, Wenyi Dong, Kun FuiD, Menghan ZhouiD, Yanan Qing, Raymond C. K. ChaniD, Keith Maurice KendrickiD, Dezhong YaoiD, Shuxia YaoiD, Benjamin BeckeriD
Abstract
Overarching conceptualizations propose a critical role of the default mode network (DMN) in self-referential mental time travel, particularly in autobiographical memory retrieval and episodic future thinking, and internal (intrinsic) emotion generation and regulation. However, these conceptualizations have not been directly evaluated. Against this background, the present fMRI study aimed to identify both shared and distinct neural systems underlying autobiographical episodic processing across different temporal contexts - specifically, episodic memory retrieval (EMR) and episodic future thinking (EFT) - and to examine how these systems interact with affective experiences, including valence and arousal. Our findings demonstrated the central role of the DMN - encompassing the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and medial temporal lobe (MTL) - in both EMR and EFT. Importantly, we identified a functional dissociation along both valence and temporal dimensions: the ventromedial prefrontal cortex (vmPFC) was more strongly associated with positive experiences and simulations, whereas the dorsomedial prefrontal cortex (dmPFC) was consistently engaged during the processing of negative affect across past and future contexts. Moreover, representational similarity and parametric analyses indicated that the hippocampus supports differential processing of valence and arousal across temporal domains. Together, these findings provide empirical evidence for the involvement of cortical midline core DMN systems in autobiographical processing across time and suggest overlapping and distinct systems for the integration of emotional experiences across mental time travel.
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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