Back

Neuronal activity-driven oligodendrogenesis in selected brain regions is required for episodic memories

Barboza, L.; Bessieres, B.; Nazarzoda, O.; Alberini, C.

2021-12-10 neuroscience
10.1101/2021.12.10.472135 bioRxiv
Show abstract

The formation of long-term episodic memories requires the activation of molecular mechanisms in several regions of the medial temporal lobe, including the hippocampus and anterior cingulate cortex (ACC). The extent to which these regions engage distinct mechanisms and cell types to support memory formation is not well understood. Recent studies reported that oligodendrogenesis is essential for learning and long-term memory; however, whether oligodendrocyte lineage cells are required only in selected brain regions is still unclear. Also still unknown are the temporal kinetics of oligodendrocyte lineage cells involvement in memory processes and whether these cells are engaged in response to neuronal activity. Here we show that in rats and mice, episodic learning rapidly increases the oligodendrogenesis and myelin biogenesis transcripts Olig2, Myrf, Mbp, and Plp1 as well as oligodendrocyte precursor cells (OPC) proliferation and differentiation in the ACC, but not in the dorsal hippocampus (dHC). Region-specific knockdown or knockout of Myrf, a regulator of oligodendrocyte differentiation, revealed that cells of the oligodendrocyte lineage are required for memory formation in the ACC but not the dHC. Chemogenetic neuronal silencing in the ACC showed that neuronal activity is critical for learning-induced OPC proliferation. Hence, activity-driven oligodendrocyte lineage cells in the ACC, but not dHC, are critical for the formation of episodic memories. Impact statementOligodendrocyte lineage cells are required in the anterior cingulate cortex but not in the hippocampus for long-term memory formation.

Matching journals

The top 7 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.