Functional Neural Architecture Supporting Highly Superior Autobiographical Memory

Background: The neural mechanisms of highly superior autobiographical memory (HSAM) are poorly understood. To shed light on the functional magnetic resonance imaging (fMRI)-informed neurobiology of this condition, in this study we characterize for the first time the neurofunctional architecture of a 20-year-old individual (B.B.) with HSAM and no concurrent neurological/psychiatric or other clinical conditions. Materials and Methods: Relying on t-test inferential models comparing a single observation with a control group, we processed B.B.'s resting-state fMRI signal and compared it with the neurofunctional architecture of 16 young adults with normal autobiographical memory. Specifically, we analyzed large-scale brain networks, region-to-region functional connectivity, and connectivity indices informed by graph theory. Results: B.B. showed higher expression of large-scale and region-to-region connectivity, larger segregation of the pallidum and enhanced centrality of the temporal pole, orbitofrontal cortex and cerebellar lobule IX. Conclusion: These findings indicate that HSAM is associated with increased expression of neural pathways that support memory encoding, retrieval, and elaboration, but also with reduced expression of patterns typically involved in information control and metacognition, the use of which would be minimized thanks to automatic and accurate memory processing The findings that highly superior autobiographical memory (HSAM) is supported by increased expression of large-scale brain networks, upregulated memory-centered region-to-region pathways, and downregulated expression of pathways that normally support memory operations that are underutilized in HSAM greatly contribute to clarifying HSAM-specific mechanisms. They also inform computational models of retrograde and anterograde memory processing, and impact on the application of artificial intelligence methods for the modeling of memory or for solving memory-related classification problems in clinical populations. They might also be of impact on research in forensic science when relying on the assessment of content and accuracy of autobiographical retrieval.