Back

NDNF+ interneurons have a privileged role in regulating cortical excitability

Richardson, A.; Mercier, M. S.; Shimoda, Y.; Graham, R. T.; Leite, M.; Garcia, A.; Muller, M.; Bond, S. A.; Grodzdanova, M.; Wu, Q.; Kullmann, D. M.; Magloire, V.

2025-11-28 neuroscience
10.1101/2025.11.27.690945 bioRxiv
Show abstract

Cortical processing depends critically on the precise modulation of excitatory activity by a diverse population of GABAergic interneurons. Among these, neuron-derived neurotrophic factor-expressing (NDNF+) interneurons have been proposed to be "master regulators" of cortical microcircuits. While their role in coordinating physiological cortical activity is beginning to be understood, their contribution to restraining hyperexcitability during epileptiform activity remains unexplored. To address this, we employed calcium imaging, optogenetics, and chemogenetics in an NDNF-Cre mouse line to investigate how NDNF+ interneurons influence cortical hyperactivity and spontaneous seizures. Our results demonstrate that NDNF+ interneurons are actively recruited during interictal spikes and focal seizures, although more slowly than parvalbumin-positive interneurons. Optogenetic hyperpolarization of NDNF+ interneurons exacerbates epileptic discharges, whereas their depolarization suppresses focal seizures, even when the optogenetic stimulus is delayed by several seconds from seizure onset. This effect is largely mediated by GABAB receptor signalling. Additionally, chemogenetic depolarization of NDNF+ interneurons has robust antiepileptic effects both ex vivo and in vivo. Collectively, these findings establish NDNF+ neurons as key regulators of cortical excitability and identify them as promising cellular targets for the development of anti-epileptic therapies.

Matching journals

The top 4 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.