Comparison of cortical network effects of high-definition and conventional tDCS during visuomotor processing

Abstract

tDCS is a non-invasive brain stimulation approach in which low level currents are applied across the scalp to influence underlying brain function [1Weller S. Nitsche M.A. Plewnia C. Enhancing cognitive control training with transcranial direct current stimulation: a systematic parameter study.Brain Stimul. 2020; 13: 1358-1369Abstract Full Text Full Text PDF PubMed Scopus (15) Google Scholar, 2Polanía R. Nitsche M.A. Ruff C.C. Studying and modifying brain function with non-invasive brain stimulation.Nat Neurosci. 2018; 21: 174-187Crossref PubMed Scopus (316) Google Scholar, 3Bikson M. Brunoni A.R. Charvet L.E. Clark V.P. Cohen L.G. Deng Z.-D. et al.Rigor and reproducibility in research with transcranial electrical stimulation: an NIMH-sponsored workshop.Brain Stimul. 2018; 11: 465-480Abstract Full Text Full Text PDF PubMed Scopus (101) Google Scholar]. The Serial Reaction Time Task (SRTT) is commonly used to investigate neural mechanisms underlying motor-learning [[4]Buch E.R. Santarnecchi E. Antal A. Born J. Celnik P.A. Classen J. et al.Effects of tDCS on motor learning and memory formation: a consensus and critical position paper.Clin Neurophysiol : Off. J. Int. Federat. Clin. Neurophysiol. 2017; 128: 589-603Crossref PubMed Scopus (161) Google Scholar]. In the SRTT, subjects make a series of button presses based upon visual location cues. When the sequence is random, mean RT remains relatively stable over time. However, when the sequence is repetitive and fixed, individuals show a progressive motor learning reflected in a reduction in reaction time (RT) across trials even if they are not told of the sequence in advance. The rate of motor learning may be modulated by tDCS applied to the visuomotor learning circuit consisting of dorsal-stream visual cortex and motor/premotor regions of frontal cortex [[5]Hardwick R.M. Rottschy C. Miall R.C. Eickhoff S.B. A quantitative meta-analysis and review of motor learning in the human brain.Neuroimage. 2013; 67: 283-297Crossref PubMed Scopus (371) Google Scholar,[6]Savic B. Meier B. How transcranial direct current stimulation can modulate implicit motor sequence learning and consolidation: a brief review.Front Hum Neurosci. 2016; 10: 26Crossref PubMed Scopus (28) Google Scholar]. We recently [[7]Sehatpour P. Donde C. Hoptman M.J. Kreither J. Adair D. Dias E. et al.Network-level mechanisms underlying effects of transcranial direct current stimulation (tDCS) on visuomotor learning.Neuroimage. 2020; 117311Crossref PubMed Scopus (6) Google Scholar] demonstrated that RT distributions during the fixed version of the SRTT are bimodal, with intermixed fast, “proactive” and slow, “reactive” trials, and that tDCS functions primarily by altering the ratio between trials reflecting enhanced motor learning. Further, we have demonstrated that the different trial types are associated with differential connectivity patterns within the visuomotor network. Finally, we demonstrated that the shift in connectivity pattern explained the shift in RT distribution. Specifically, cathodal stimulation of dorsal-stream visual cortex, with cathode at POz and anode at Cz according to the 10–20 EEG system, brought about a change in connectivity between motor and visual cortices accompanied by improved task performance. Traditional tDCS uses relatively large (3 × 3 cm) pads placed over specific scalp regions, which leads to relatively coarse targeting of the electrical field within underlying brain regions. More recently, high-definition (HD-tDCS) approaches have been developed to better focus the energy to key underlying brain regions [[8]Dmochowski J.P. Datta A. Bikson M. Su Y. Parra L.C. Optimized multi-electrode stimulation increases focality and intensity at target.J Neural Eng. 2011; 8046011Crossref PubMed Scopus (337) Google Scholar]. Here we investigate the relative effectiveness of HD-vs. conventional tDCS [[9]Kuo H.-I. Bikson M. Datta A. Minhas P. Paulus W. Kuo M.-F. et al.Comparing cortical plasticity induced by conventional and high-definition 4 × 1 ring tDCS: a neurophysiological study.Brain Stimul. 2013; 6: 644-648Abstract Full Text Full Text PDF PubMed Scopus (368) Google Scholar] in improving motor learning when applied to the visual node of the visuomotor network, along with the relative effects on underlying brain connectivity patterns. We predicted increased efficacy of HD-vs. conventional tDCS, reflecting its greater focality within target regions. This study involved 10 healthy participants (3 females, 7 males), mean age 41.7 ± 9.6. All subjects provided written informed consent, and the procedures were approved by the Nathan Kline Institute Review Board. All participants reported normal vision. All were right-handed. Cathodal (2 mA) or sham tDCS over visual cortex was administered using the Soterix Medical HD-tDCS 4 × 1 stimulator while subjects performed repeat trial blocks (“runs”) of the SRTT using a previously described paradigm. Simultaneous EEG was recorded using an AC-coupled BrainVision recording system. Participants received four tDCS conditions on separate days in random order: HD-active, HD-sham, conventional-active, and conventional-sham (see Figs. A, B). Separate analyses were performed for random and fixed conditions using 2 × 2 mixed-model regression with factors of run-number, active/sham and HD/conventional stimulation as described previously [[7]Sehatpour P. Donde C. Hoptman M.J. Kreither J. Adair D. Dias E. et al.Network-level mechanisms underlying effects of transcranial direct current stimulation (tDCS) on visuomotor learning.Neuroimage. 2020; 117311Crossref PubMed Scopus (6) Google Scholar]. Mean RT Results: No significant tDCS effects were observed during the “random” sequences as reflected in a non-significant effect of run (F1,140 = 0.76, p = .39). During “fixed” sequences, significant main effects of run-number (F1,781 = 18.3, p < .0001), active/sham stimulation (F1,781 = 18.3, p < .0001), and tDCS type (HD/conventional, F1,781 = 18.8, p < .0001), the 2-way interactions between active/sham X run# (F1,781 = 4.13, p = .043), tDCS type X run# (F1,781 = 12.1, p = .001) and active/sham X type (F1,781 = 5.74, p = .018) as well as the 3-way interaction between factors (F1,781 = 5.85, p = .016) were observed. Across all runs, active stimulation was significantly superior to sham (F1,781 = 27.9, p < .0001) and HD was significantly superior to conventional (F1,781 = 7.29, p = .007) (Fig. 1C, D). Single-trial RT Results: Across all random conditions, data fit best to a 1-Gaussian distribution (R2 = 0.999) with mean RT across conditions of 2.676 ±.001 log-ms (474.2 ms) (Fig. 1E). By contrast, in the fixed condition, a 2-Gaussian solution was statistically superior (F3,23 = 220.9, p < .0001, R2 = 0.989), with mean RT of the fast and slow trials of 2.305 ±.009 and 2.635 ±.006 log-ms (201.8 and 431.5 ms), respectively. When analyses were conducted across conditions, the ratio of fast to slow trials was significantly higher (F1,50 = 40.3, p < .0001) in the HD (62.5 ±.01) than conventional (49.6 ±.02) condition, consistent with mean RT results. Furthermore, when a cut-off value of 2.47 log-ms (295 ms) was used to differentiate fast vs. slow responses, the 3-way run X active/passive X type (HD/conventional) interaction (F1,797 = 13.2, p < .0001) was also significant (Fig. F). Electrophysiological Results: As reported previously [[7]Sehatpour P. Donde C. Hoptman M.J. Kreither J. Adair D. Dias E. et al.Network-level mechanisms underlying effects of transcranial direct current stimulation (tDCS) on visuomotor learning.Neuroimage. 2020; 117311Crossref PubMed Scopus (6) Google Scholar], under sham condition significant coherence is observed across Motor, SMA, Visual cortical regions revealing a functional network engaged in SRTT task performance (Figure, Panel G left). Conventional visual-cathodal tDCS significantly modulated coherence across the visual-motor nodes of this network compared to sham (Figure, Panel G middle). HD-tDCS however not only modulated the coherence across the visual-motor nodes but also across visual-SMA and motor-SMA regions compared to sham. Moreover, when comparing HD-tDCS vs. conventional, HD-tDCS brought about a significantly higher coherence between the motor-SMA and lower coherence between visual-SMA regions (Figure, Panel G right). In summary, tDCS alters mean RT during motor learning primarily by facilitating a shift from slow, “reactive” to fast, “proactive” responses, in which the subject can predict in advance where the stimulus will appear. Here, we show performance improvement with HD-tDCS also follows the same pattern albeit more so compared to conventional configuration. We also show HD-tDCS modulated the coherence across all the cortical nodes engaged in SRTT with greater effectiveness compared to conventional tDCS. This is note-worthy considering the current flow distribution of HD-tDCS shows minimal direct current spread to non-visual nodes of the visuomotor circuit. This suggests the observed significant change in coherence between the motor and SMA regions result from more efficient use of visual information, rather than local modulation of the interaction between these regions. tDCS has been shown to have robust effects on brain plasticity across a range of paradigms. Nevertheless, effects of conventional tDCS may be limited by non-focality of conventional stimulation approaches. HD-tDCS produces greater focality by surrounding a central “active” electrode with multiple “returns”, limiting current spread. Here, we show both superior behavioral and superior neurophysiological effects of HD-vs. conventional tDCS, supporting its more widespread use across learning paradigms. This work was supported by National Institute of Mental Health grants MH49334 and MH109289 to DCJ.

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