Back

Temporal Interference Stimulation Enhances Neural Regeneration

Peressotti, S.; Garcia Garrido, M.; Dzialecka, P.; Law, R. M. H.; Portillo Lara, R.; Geary, B.; Faillace, E.; Wojewska, M.; Otero-Jimenez, M.; Genta, M.; Tan, L.; Duff, K.; Alegre-Abarrategui, J.; Green, R. A.; Grossman, N.

2025-08-22 bioengineering
10.1101/2025.08.18.670811 bioRxiv
Show abstract

Neural regeneration therapies aim to treat neurodegeneration by promoting the proliferation and maturation of exogenous or endogenous neural progenitor cells (NPCs). However, their efficacy has been limited. Deep brain stimulation (DBS) via implanted electrodes has been shown to promote neurogenesis. However, its invasiveness precludes deployment in research and widespread clinical use. Temporal interference (TI) has emerged as a strategy for non-invasive, high-precision DBS using multiple kHz-range electric fields, with a frequency difference within the range of neural activity. Here, we validate the potential of TI stimulation for neural regeneration augmentation. We demonstrate that TI stimulation with a theta-band frequency difference enhances the maturation of embryonic neural progenitor cells in vitro. We then demonstrate that theta-band TI stimulation targeting the hippocampus enhances endogenous hippocampal neurogenesis in an in vivo mouse model of Alzheimers disease. By uncovering frequency-specific control of stem cell fate, we propose a clinically relevant regeneration strategy which avoids pharmacological or genetic manipulation. Our results demonstrate focal, non-invasive augmentation of deep-brain neural regeneration. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/670811v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@984165org.highwire.dtl.DTLVardef@1ed8bbdorg.highwire.dtl.DTLVardef@715057org.highwire.dtl.DTLVardef@151882f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Published in Advanced Science (predicted rank #2) · training set

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.