Poster No:
43
Submission Type:
Abstract Submission
Authors:
Dario Müller1, Ute Habel2, Carmen Weidler1
Institutions:
1University Hospital Aachen, Aachen, NRW, 2Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University Hospital, Aachen, North Rhine-Westphalia
First Author:
Co-Author(s):
Ute Habel
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University Hospital
Aachen, North Rhine-Westphalia
Introduction:
High impulsivity significantly contributes to the likelihood of substance use, relapse rates, and risky behavior. Consequently, interventions to improve impulsivity are highly warranted. High-definition transcranial direct current stimulation (HD-tDCS), a non-invasive brain stimulation technique altering cortical excitability, stands as a powerful tool to modulate brain activation associated with impulsivity. However, previous research has produced inconclusive results regarding stimulation effects on impulsivity. Findings suggest that inter-individual differences in the electric field created by HD-tDCS can partially explain variance in behavioral outcomes[1], as well as functional brain connectivity[2]. Due to the heightened sensitivity of HD-tDCS to individual brain architecture[3], these differences may exert more pronounced influences on outcomes compared to conventional tDCS. Furthermore, literature suggests that nicotine interacts with the effects of tDCS because both affect calcium channel signaling[4]. This study investigates the effects of HD-tDCS on response inhibition in smokers and non-smokers, aiming to understand the underlying mechanisms better and delineate factors influencing responsiveness to (HD-)tDCS.
Methods:
In a double-blind, placebo-controlled, between-subjects study employing simultaneous HD-tDCS and functional magnetic resonance imaging (fMRI), we recruited forty-five male chronic tobacco users and forty-five non-smoking male participants. Participants performed the Stop Signal Task before and after receiving either sham tDCS or 20 minutes of 1.5mA anodal HD-tDCS over the right inferior frontal gyrus during resting state fMRI. Carbon monoxide (CO) levels were assessed in chronic tobacco users as a measure of nicotine intake. Using T1 and T2 weighted anatomical images, individual head meshes were created with CHARM[5]. Electric field simulations were computed using SimNIBS[6]. Within the right inferior frontal gyrus, the mean electric field magnitude within a 5mm radius sphere was computed for each participant.
Results:
Behavioral data revealed significant improvements in Stop Signal Reaction Times (SSRTs) following active and sham stimulation in non-smoking participants. Chronic tobacco users showed improved SSRTs following sham but not active stimulation. While CO levels did not influence tDCS effects, lower CO levels were associated with shorter SSRTs. Results also highlighted significant variability in HD-tDCS-induced electric fields. Seed-to-voxel analysis indicated increased resting-state functional connectivity (rsFC) under the anode, particularly to the left prefrontal cortex, in active compared to sham stimulation during initial stimulation periods and post-stimulation. These effects were driven by rsFC fluctuations in the sham group, while the active group remained stable. Additionally, an effect of e-field magnitude was found, with a higher magnitude correlating with increased rsFC during the first part of the stimulation.
Conclusions:
Collectively, our findings suggest that enhanced SSRTs are primarily attributed to a training effect rather than being influenced by HD-tDCS. This contradicts many studies using conventional tDCS setups reporting improved response inhibition following anodal prefrontal tDCS. In chronic tobacco users, anodal HD-tDCS even seems to suppress the training effect. Additionally, considering the high variability of electric fields, our study highlights the importance of taking individual differences into account when assessing the impact of HD-tDCS, urging further investigation in this domain.
Brain Stimulation:
Non-invasive Electrical/tDCS/tACS/tRNS 1
TDCS
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural) 2
Other Methods
Keywords:
Cognition
FUNCTIONAL MRI
MRI
Other - High-Definition Transcranial Direct Current Stimulation; Electric Field Modeling, Nicotine, Impulsivity
1|2Indicates the priority used for review
Provide references using author date format
1 Albizu, A., Fang, R., Indahlastari, A., O’Shea, A., Stolte, S. E., See, K. B., ... & Woods, A. J. (2020). Machine learning and individual variability in electric field characteristics predict tDCS treatment response. Brain stimulation, 13(6), 1753-1764.
2 Abellaneda-Pérez, K., Vaqué-Alcázar, L., Perellón-Alfonso, R., Solé-Padullés, C., Bargalló, N., Salvador, R., ... & Bartrés-Faz, D. (2020). Multifocal tDCS modulates resting-state functional connectivity in older adults depending on induced electric field and baseline connectivity. bioRxiv.
3 Mikkonen M, Laakso I, Tanaka S, Hirata A. Cost of focality in TDCS: Interindividual variability in electric fields. Brain Stimul. 2020;13(1):117-124. doi:10.1016/j.brs.2019.09.017
4 Grundey, J., Barlay, J., Batsikadze, G., Kuo, M. F., Paulus, W., & Nitsche, M. (2018). Nicotine modulates human brain plasticity via calcium‐dependent mechanisms. The Journal of physiology, 596(22), 5429-5441.
5 Puonti, O., Van Leemput, K., Saturnino, G. B., Siebner, H. R., Madsen, K. H., & Thielscher, A. (2020). Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling. Neuroimage, 219, 117044.
6 Thielscher, A., Antunes, A., & Saturnino, G. B. (2015, August). Field modeling for transcranial magnetic stimulation: A useful tool to understand the physiological effects of TMS?. In 2015 37th annual