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Decoding Spatial Memory Retrieval in Cubical Space Using fMRI Signals

ABSTRACT: The way spatial memory retrieval is represented in the brain remains unclear to date. Previous studies have displayed a hippocampus-centered navigation network using functional magnetic resonance imaging (fMRI) analysis. There have been some studies on the representation of navigation behavior by signal distribution patterns, but only in the hippocampus and adjacent structures. In this study, we aimed to determine (1) the brain regions that represent information in both intensity and distribution patterns during spatial memory retrieval and (2) whether the patterns of neural responses represent spatial memory retrieval behavior performance. Both univariate analysis [general linear model (GLM)] and multivariate pattern analysis (MVPA) were employed to reveal the spatial distributions of brain responses elicited by spatial memory retrieval. Correlation analyses were performed to detect the correspondences between brain responses and behavior performance. We found that spatial memory retrieval occurred in widespread brain regions, including the bilateral hippocampi, bilateral superior frontal gyrus, bilateral superior parietal lobules, bilateral occipital lobes, and cerebellum. The amplitude of activation in the left hippocampus showed a significant negative correlation (r = −0.46, p = 0.039) with the number of task completions. Additionally, within-subject classification accuracies based on the blood oxygenation level-dependent (BOLD) signal patterns of the right middle temporal gyrus (rMTG) rostral areas in the Brainnetome Atlas showed a significant positive correlation (r = 0.78, p < 0.0001) with retrieval accuracy. In summary, our findings have implications for understanding the separation between navigational and non-navigational states and emphasizing the utility of MVPA in the whole brain.

SUBMITTER: Guo J

PROVIDER: S-EPMC8168467 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:FMRI studies of autobiographical memory (AM) retrieval typically ask subjects to retrieve memories silently to avoid speech-related motion artifacts. Recently, some fMRI studies have started to use overt (spoken) retrieval to probe moment-to-moment retrieved content. However, the extent to which the overt retrieval method alters fMRI activations during retrieval is unknown. Here we examined this question by eliciting unrehearsed AMs during fMRI scanning either overtly or silently, in the same subjects, in different runs. Differences between retrieval modality (silent vs. narrated) included greater activation for silent retrieval in the anterior hippocampus, left angular gyrus, PCC, and superior PFC, and greater activation for narrated retrieval in speech production regions, posterior hippocampus, and the DLPFC. To probe temporal dynamics, we divided each retrieval period into an initial search phase and a later elaboration phase. The activations during the search and elaboration phases were broadly similar regardless of modality, and these activations were in line with previous fMRI studies of AM temporal dynamics employing silent retrieval. For both retrieval modalities, search activated the hippocampus, mPFC, ACC, and PCC, and elaboration activated the left DLPFC and middle temporal gyri. To examine content-specific reactivation during retrieval, the timecourse of narrated memory content was transcribed and modeled. We observed dynamic activation associated with object content in the lateral occipital complex, and activation associated with scene content in the retrosplenial cortex. The current findings show that both silent and narrated AMs activate a broadly similar memory network, with some key differences, and add to current knowledge regarding the content-specific dynamics of AM retrieval. However, these observed differences between retrieval modality suggest that studies using overt retrieval should carefully consider this method's possible effects on cognitive and neural processing.

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Decoding Spatial Memory Retrieval in Cubical Space Using fMRI Signals

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