[en] Background: Transcranial Random Noise Stimulation (tRNS) has been shown to facilitate visual-attention abilities. Yet, the impact of this type of stimulation alone on brain dynamics remains to be determined.
Aim: We investigated the effect of off-line multi-session tRNS on brain activity and visual-attention behavior. We hypothesized that parietal stimulation would impact brain activity related to attentional processes that could consequently facilitate behavior.
Methods: 28 healthy subjects participated in a 5-day experiment and were assigned to one of four conditions in a between-subject design: two active conditions (tRNS was delivered over hMT+ or over parietal cortex) and two no-stimulation conditions (sham and behavioral only). From day 1 to day 4 subjects received stimulation (depending on their condition) while at rest. On day 5, brain activity was recorded while subjects performed the visual-attention task.
Results: Parietal tRNS significantly modulated brain activity compared to control groups. However, no difference in performance was found.
Conclusion: Our findings demonstrate that off-line multi-session parietal tRNS has a long-lasting impact on brain activity but not on behavior. This study shows that this type of stimulation can be efficient alone to influence brain states and such stimulation efficacy could be advantageous in the rehabilitation context where active patients’ collaboration is not always possible. Furthermore, this study is a starting point to exploit the physical properties of this stimulation method to influence the cortex.
Disciplines :
Neurosciences & behavior
Author, co-author :
Pergher, Valentina; Harvard University > Psychology > Postdoc
Contò, Federica; Italian Institute of Technology - IIT > Postdoc
Paparella, Ilenia ; Université de Liège - ULiège > GIGA CRC In vivo Imaging - Sleep and chronobiology > PhD
Battelli, Lorella; Italian Institute of Technology - IIT & Harvard University > Psychology > PI
Language :
English
Title :
Functional response of attention-related cortical networks to multi-sessions tRNS