Back

Causal interhemispheric neuromodulation sharpens synaptic and neurobehavioral inhibition in stroke

Castelhano, J.; Duecker, F.; Xavier, C.; Canario, N.; Duarte, I. C.; Afonso, S.; Marques, A.; Lourenco, C.; Carvalho, F.; Lains, J.; Neves, A.; Sargento-Freitas, J.; C Santo, G.; Abrunhosa, A.; Sack, A.; Castelo-Branco, M.

2025-03-11 neurology
10.1101/2025.03.10.25323712 medRxiv
Show abstract

Hemispheric functional asymmetries are a key aspect of human brain organization. Revealing how causal manipulations of hemispheric (im)balances dynamically change pre and postsynaptic GABA regulation, in terms of mean and variance changes, is essential to understand plasticity of brain asymmetries in health and disease. This has direct implications for treatment approaches in stroke relying on interhemispheric inhibition using transcranial magnetic stimulation (TMS). We investigated neurobehavioral and neurochemical effects of cortical stimulation using a unique multimodal brain imaging and stimulation set-up, in both healthy and unilaterally lesioned hemispheres, in stroke. We performed two molecular imaging sessions measuring GABA receptor levels, with sham and real neurostimulation, fMRI and GABA neurospectroscopy, all in the same day. We found that noninvasive inhibitory neuromodulation of interhemispheric interactions causes pre-postsynaptic sharpening of GABA neurotransmitter and receptor levels, providing a mechanism of action for TMS contralateral inhibitory protocols in stroke. This discovered plasticity mechanism based on correlated reduction of pre and postsynaptic variability represents a basic principle underlying long range inhibitory hemispheric interactions and allows for stabilization of synapses and re-balancing of brain activity in disorders of hemispheric imbalance, such as stroke.

Published in Brain Communications (predicted rank #7) · training set

Matching journals

The top 6 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.