Back

Remoteness sensitive theta network dynamics during early autobiographical memory access

Navas, M. C.; Ferrelli, I.; Pedreira, M. E.; Fernandez, R. S.; Bavassi, L.

2025-12-14 animal behavior and cognition
10.64898/2025.12.12.694026 bioRxiv
Show abstract

AO_SCPLOWBSTRACTC_SCPLOWAutobiographical memory (AM) is a core component of human cognition; it defines who we are, it helps us relate to others, and supports decision-making and future planning. The variability embedded in AMs offers a unique opportunity to examine the neural dynamics engaged during the retrieval of memories of different ages. Here, we focus on the early access (or search) phase and test whether theta-band (4-9 Hz) cortical activity carries age-sensitive signatures distinguishing recent (<1 year) from remote (>3 years) AMs. Forty-one participants performed an AM retrieval task while EEG was recorded. Theta power and time-resolved Granger causality (GC) were quantified during the first 1200 ms of the access period. Remote AM access elicited a significant increase in midline fronto-central theta power around 900 ms, consistent with enhanced internally driven, reconstructive processing. GC analyses further revealed that remote AMs exhibited denser, more widespread theta-band connectivity, including a prominent anterior-to-posterior information flow between 600-800 ms, whereas recent AMs showed more localized fronto-central interactions. These findings demonstrate that the earliest phase of AM access is shaped by memory age, with remote memories recruiting broader, long range theta mediated coordination. Our results identify theta oscillations and their directed network interactions as temporally precise markers of AM remoteness, providing new insights into the systems-level mechanisms that support autobiographical retrieval.

Published in Scientific Reports (predicted rank #1) · training set

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.