• autobiographical memory
• fMRI
• MVPA
• retrival

Autobiographical memory (AM) is an explicit memory system responsible for remembering one’s own life. Previous neuroimaging studies have found it to be a complex system relying on multiple cognitive functions and supported by a widespread network centered around the prefrontal cortex (PFC) and the left hippocampus. This work uses multivariate pattern analysis to determine if it is possible to create a model for AM by using some of its elemental components, namely: visual, interpersonal, spatial, temporal, action, object, mental time travel. Using the fMRI data of 14 female subjects (mean age 37±7 years) performing an AM task, consisting in the recognition of autobiographical memories concerning an emotionally charged event, we created an encoding matrix that was used to predict the subject response to testing stimuli. Single-subject analysis showed that our model was able to predict the response to the stimulus with a significant 55% mean accuracy at a specific timepoint (14s). Group level analysis showed that the cortical areas that better predict our encoding matrix across different subjects are the PFC and the left hippocampus, confirming the extreme importance of these structures in the AM network. Mapping all the single component across the cerebral cortex revealed that other areas relevant for the recall of AM are the anterior cingulate cortex, the superior and inferior frontal sulcus and the right insula. Our results show that it is indeed possible to describe AM using its components and confirmed that this system is based on a network spread onto the cortical mantle.