These results provide a proof of principle that tDCS, delivered through the scalp, can influence neural activity in the primate brain. However, because the effects of tDCS may vary with behavioral state [], we next analyzed data collected from PFC and ITC while the monkeys performed a challenging behavioral task. We first consider the effects of tDCS on behavior, and then we ask whether these behavioral effects could be mediated by tDCS-induced changes in local or widespread neural activity.

To test for these effects’ specificity, we also generated a second montage, which targeted left ITC. When tDCS was applied according to this montage ( Figure 2 B, yellow), it induced low-frequency oscillations within ITC, but not PFC. Again, these results were markedly different from what was seen during sham stimulation (blue) with the same montage and were not seen in the non-targeted region (PFC). As the field map in Figure S1 shows, regions around the target, particularly those between the anodal and cathodal electrodes, also receive some stimulation. However, this spatial specificity is incompatible with global arousal changes related to the somatosensory sensations evoked by tDCS’s onset.

(B) When tDCS is instead applied to ITC, oscillations become visible in ITC (right), but not PFC (left), instead. As in (A), this is restricted only to active tDCS (yellow), not sham (blue), and only within the targeted brain area (right).

(A) In blocks where tDCS is applied to PFC (yellow traces, left side), large low-frequency oscillations are present in the LFP recorded from PFC. These are not present in PFC during sham stimulation (blue traces, left side), nor are they present in ITC during either condition (yellow and blue traces, right side).

Applying tDCS produces large low-frequency oscillations in the targeted brain area. Traces show unprocessed, wideband signals simultaneously recorded from electrodes in PFC (left) and ITC (right) during tDCS (yellow) and sham stimulation (blue). Stimuli were applied for 5 min; the last 10 s of each block (before ramp-down) is shown here. Blocks were randomly ordered during the experiment, but traces have been grouped by condition here for display.

The design of this experiment is shown in Figures 1 A and 1B . Animals continuously performed the fixation task while two types of electrical stimulation were applied in 5-min blocks. These blocks were randomly ordered and separated by 5- to 10-min interstimulus intervals. In a tDCS block, the current was gradually ramped up from 0 to 2 mA over 3 s, maintained at 2 mA for 5 min, and then ramped down from 2 to 0 mA over 3 s. Less than 1% of human subjects can detect the presence or absence of a 2-mA DC current on the scalp at steady state, but its onset and offset generate noticeable somatosensory percepts []. We therefore compared our tDCS data against the “sham” stimulation protocol in Figure 1 A. As in the active protocol, stimulation was initially ramped up from 0–2 mA over 3 s. However, in sham blocks, the full 2-mA current was only applied for 10 s and then ramped back down to zero over 3 s. At the end of a sham block, the 2-mA current was again applied and ramped back down to 0 mA. This protocol generates the same somatosensory percepts as the active condition but passes far less charge into the brain. Since blinded human subjects are typically unable to report whether they received active or sham stimulation [], this tDCS versus sham comparison is currently the gold standard for human neurostimulation research [].

(C) A naturalistic foraging task [] was used to study the effects of tDCS on associative learning. (left) Animals learned to associate an image (contrast reduced for illustration) with a reward zone, which is shown here by a red and white ring, although the ring was not displayed during the experiments. Animals initially explored the entire image but after repeated experience learned to saccade directly to the reward zone. The overlaid traces are one animal’s eye movements during his initial exposure to this image/location pair (blue) and 25 trials later (yellow). (right) On each trial, we evaluated neural data from the 400 ms following scene onset (scene onset epoch). Animals learned two or three image pairs per session. During each session, tDCS or sham stimulation was applied to PFC, as described in (A) and (B).

(B) (top) In the passive fixation experiment (see text and Figure 2 ), tDCS (yellow) and sham (blue) stimulation were applied in 5-min blocks, separated by a 5-min interstimulus interval. Blocks were randomly ordered and at least 10 blocks were collected each day. (bottom) During the foraging paradigm (see text and C), tDCS and sham stimulation were applied for 5 min before the task began and then continued until the monkey performed 75 (monkey F) or 100 (monkey M) trials of each image. Trials were separated by 500–1,000 ms, while tDCS and sham blocks were separated by 15-min intervals. One to three blocks were performed each day, and the tDCS/sham order was alternated within sessions and across days (e.g., since the session shown here began with a tDCS block, the next session would start with a sham block, followed by a tDCS one).

(A) Data from two stimulation conditions, active tDCS and sham stimulation, are compared in the following experiments. (A) Waveforms for tDCS (yellow box) and sham stimulation (blue) are shown. The peak current in both conditions was 2 mA. Please see Figure S1 for a depiction of the induced electrical field.

Since virtually nothing is known about the effects of tDCS on the primate brain, we first sought to determine whether it affects neural activity. Two non-human primates were each implanted with a pair of 96-channel “Utah” arrays: one in the right PFC and the other in the left inferotemporal cortex (ITC). Animals were trained to sit calmly in a darkened testing chamber while fixating a small target displayed against a gray background; liquid rewards were dispensed every 1–3 s while the animal maintained fixation. This task allowed us to control the animals’ behavioral and oculomotor state across stimulation conditions.

The metallic implants and skull defects required for neurophysiological recording may distort the electric field generated by tDCS [], and individual differences in neuroanatomy may also cause electrodes placed on similar scalp locations to generate different electric fields within the brain []. To minimize these potential confounds, we first created a detailed finite element model of each monkey’s head. The model was then solved to find scalp locations that maximized the electric fields within a targeted brain location []. We considered both traditional bipolar electrode montages and montages containing up to 8 stimulating electrodes. Since the vast majority of human experiments use no more than 2 mA of stimulating current, we also limited our montages to 2 mA total current. We generated electrode montages that maximized field strength in right lateral prefrontal cortex (PFC), an area with extensive neuroanatomical connections [], associated with memory formation and recall [], and the focus of many human tDCS studies. The finite-element models indicate that these montages generated fields of 0.68 V/m in PFC of one monkey and 0.42 V/m in the other. These values are comparable to those estimated for healthy human subjects (0.4–1.0 V/m from []) and measured in human epilepsy patients (0.4 V/m from []). Further details of the modeling procedure, including the models generated for the animals used in this study, have already been published []; Figure S1 shows the field strength for one animal.

Effects of tDCS on Performance in an Associative Learning Task

29 Chukoskie L.

Snider J.

Mozer M.C.

Krauzlis R.J.

Sejnowski T.J. Learning where to look for a hidden target. Two non-human primates were trained to perform an oculomotor foraging task [] depicted in Figure 1 C. This task requires learning arbitrary associations between natural images and response zones (RZs), small (2° radius) regions within each image. On each trial, animals were shown a single image and allowed to freely view it. When they fixated within the RZ for 100 ms, they received a juice reward, and the trial ended. Presentations of 2–3 images, each with its own RZ, were randomly interleaved. Each image’s RZ was randomly chosen at the start of the block, and, except for a small jitter (0–4°), its location was fixed across presentations of the same image.

Along with the foraging task, animals also performed a simple visually guided saccade-to-target task. On these trials, animals were shown a gray screen containing only a small, high-contrast saccade target. They received a small liquid reward for saccading to the target and maintaining their gaze on it for 750–1,250 ms. This is essentially the same paradigm used in the passive fixation experiment (above), except that the target location was randomly chosen from one of 9 (or 25) locations on a 3 × 3 (or 5 × 5) grid spanning the monitor. Since these trials were intended to monitor the animals’ motivation and ensure that the eye tracker remained calibrated, they were interleaved so at least one saccade-to-target trial followed every foraging trial.

To study the effects of tDCS, each block was paired with either active tDCS or sham stimulation; conditions were counterbalanced within and across days to avoid order effects ( Figure 1 B). At the beginning of each block, the stimulation was applied for 5 min while the animals sat quietly. We then began the behavioral paradigm depicted in Figure 1 C. Both the behavioral paradigm and stimulation ended after every image was shown 75 (monkey F) or 100 (monkey M) times. Since the trials were self-paced, blocks varied in length. The median stimulation length was 35 min and never exceeded 1 hr.

20 Márquez-Ruiz J.

Leal-Campanario R.

Sánchez-Campusano R.

Molaee-Ardekani B.

Wendling F.

Miranda P.C.

Ruffini G.

Gruart A.

Delgado-García J.M. Transcranial direct-current stimulation modulates synaptic mechanisms involved in associative learning in behaving rabbits. 36 Nitsche M.A.

Paulus W. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. Figure 3 Behavioral Results Show full caption 2 = 0.83), and this did not significantly differ by condition (tDCS: 0.82; sham: 0.85; p = 0.625 via F-P randomization test). Two parameters corresponding to aspects of task performance were extracted. Downward shifts of the curve, governed by the MinRT parameter, suggest that animals perform the task faster once learning is complete, while leftward shifts, indicated by changes to the N 50 parameter, suggest that animals learn more efficiently. (A) As the example in Figure 1 A suggests, animals’ RTs decreased over the course of the session. Since this decrease appeared to be sigmoidal, we fit logistic functions to each sessions’ data. They generally fit the data well (r= 0.83), and this did not significantly differ by condition (tDCS: 0.82; sham: 0.85; p = 0.625 via F-P randomization test). Two parameters corresponding to aspects of task performance were extracted. Downward shifts of the curve, governed by the MinRT parameter, suggest that animals perform the task faster once learning is complete, while leftward shifts, indicated by changes to the Nparameter, suggest that animals learn more efficiently. 50 parameter was significantly smaller in sessions in which tDCS was applied, suggesting that tDCS improves associative learning efficiency. However, we found no difference in the MinRT parameter, indicating that tDCS does not alter the speed with which the monkeys respond after learning. This suggests that tDCS does not increase animals’ arousal levels (see main text and (B) (left) The Nparameter was significantly smaller in sessions in which tDCS was applied, suggesting that tDCS improves associative learning efficiency. However, we found no difference in the MinRT parameter, indicating that tDCS does not alter the speed with which the monkeys respond after learning. This suggests that tDCS does not increase animals’ arousal levels (see main text and Figure S2 ). Error bars represent SEM. We used the response time (RT), defined as the time elapsed between the scene onset and the successful localization of the RZ, to measure performance. Early in a block, animals explored the entire image before finding the RZ ( Figure 1 C, blue). However, after repeated presentations, they learned to saccade directly into the RZ ( Figure 1 C, yellow), indicating that they learnt to associate images with their corresponding RZs. Plotting the RT versus presentation number revealed that this change in RT approximated a sigmoidal trajectory ( Figure 3 A). Accordingly, we fit the RT versus presentation number data for each image/RZ pair to a logistic function (see STAR Methods ). This parameterization separates potential effects of tDCS on associative learning from sensory [] or motor [] effects. Sensorimotor effects (e.g., faster scene recognition) would affect performance before and after learning equally, leading to vertical shifts of the RT curve, while changes in learning efficiency would be reflected in horizontal shifts of the curve.

2 = 0.83), with no significant difference in fit quality across stimulation conditions (tDCS: 0.82; sham: 0.85; p = 0.625 via Fisher-Pittman test). First, we examined the N 50 parameter, which shifts the logistic RT curve horizontally. Smaller values of N 50 indicate that the animals learned more efficiently, or equivalently, fewer trials were required for animals to reach their asymptotic performance. Applying tDCS significantly reduced N 50 , from 20.9 ± 3.6 trials during sham stimulation to 12.0 ± 1.4 trials (M ± SE) during tDCS (p = 0.019; Fisher-Pittman test), as shown in 37 Gamboa O.L.

Antal A.

Moliadze V.

Paulus W. Simply longer is not better: Reversal of theta burst after-effect with prolonged stimulation. These possibilities were examined by fitting data from 75 experimental sessions (38 tDCS, 37 sham) to logistic functions and analyzing the corresponding parameters (see STAR Methods ). The logistic functions generally fit the data very well (r= 0.83), with no significant difference in fit quality across stimulation conditions (tDCS: 0.82; sham: 0.85; p = 0.625 via Fisher-Pittman test). First, we examined the Nparameter, which shifts the logistic RT curve horizontally. Smaller values of Nindicate that the animals learned more efficiently, or equivalently, fewer trials were required for animals to reach their asymptotic performance. Applying tDCS significantly reduced N, from 20.9 ± 3.6 trials during sham stimulation to 12.0 ± 1.4 trials (M ± SE) during tDCS (p = 0.019; Fisher-Pittman test), as shown in Figure 3 B (left). Randomized F-test indicated that there was no main effect (p = 0.18) or interaction (p = 0.42) with monkey identity. These data suggest that tDCS accelerated the monkeys’ ability to learn new associations. This effect could have been underestimated, as prolonged exposure to neurostimulation, even within the same experiment [], can reduce its efficacy.

18 Cosmo C.

Ferreira C.

Miranda J.G.

do Rosário R.S.

Baptista A.F.

Montoya P.

de Sena E.P. Spreading effect of tDCS in individuals with attention-deficit/hyperactivity disorder as shown by functional cortical networks: A randomized, double-blind, sham-controlled trial. 32 Russo R.

Wallace D.

Fitzgerald P.B.

Cooper N.R. Perception of comfort during active and sham transcranial direct current stimulation: A double blind study. 38 Fischer B.

Boch R. Saccadic eye movements after extremely short reaction times in the monkey. One prosaic explanation for these effects is that tDCS may cause non-specific changes in arousal, causing the monkeys to physically perform the task more rapidly. Although this seems unlikely based on the human data mentioned above [], we performed several additional control analyses. First, we examined the MinRT parameter, which controls the vertical position of the RT curve, and represents the time needed to execute a response after learning (time required for recognizing the image, planning and executing saccades, etc.). Figure 3 B (right) shows that the MinRT values were similar for the tDCS and sham conditions (tDCS: M ± SE = 1.43 ± 0.1 s; sham: 1.38 ± 0.1). Neither a Fisher-Pittman randomization test (p = 0.69) nor a randomized F-test with a per-monkey factor (main effect of stimulation p = 0.39; main effect of monkey: p = 0.54; interaction: p = 0.43) revealed any difference in MinRT values between stimulation conditions. Monkeys can execute visually guided saccades in approximately 200 ms [], so it is unlikely that a floor effect masked possible effects of tDCS on arousal, sensory processing, or similar factors related to task execution.

39 Kobayashi Y.

Saito Y.

Isa T. Facilitation of saccade initiation by brainstem cholinergic system. 38 Fischer B.

Boch R. Saccadic eye movements after extremely short reaction times in the monkey. 40 Braun D.

Breitmeyer B.G. Relationship between directed visual attention and saccadic reaction times. 41 Fischer B.

Breitmeyer B. Mechanisms of visual attention revealed by saccadic eye movements. Second, we examined RT data from the saccade-to-target trials, which have no learning or memory components. Instead, the RTs are determined by the speed with which the monkey detects the target’s onset and plans/executes saccades toward it. We defined the RT for this task as the time elapsed between saccade target onset and the monkeys’ gaze landing within 2° of the target. These RTs did not significantly differ between tDCS blocks (M ± SE: 546 ± 13 ms) and sham blocks (M ± SE: 539 ± 20 ms), according to a Fisher-Pittman test (p = 0.99) and a two-way randomized F-test with a per-monkey covariate (main effect of stimulation p = 0.83; main effect of monkey: p = 0.24; interaction p = 0.43). Monkeys were not trained or incentivized to respond rapidly in this part of the task. Reaction times were therefore relatively slow so, as described above, it is unlikely that a tDCS-mediated effect is masked by floor effects here either. This result is in contrast to previous work showing a relationship between arousal [] or attention [] and saccade initiation.

42 Beatty J. Task-evoked pupillary responses, processing load, and the structure of processing resources. 43 Bradley M.M.

Miccoli L.

Escrig M.A.

Lang P.J. The pupil as a measure of emotional arousal and autonomic activation. Finally, we looked for tDCS-mediated changes in pupil size, a well-known biomarker for arousal and attentional state []. For this analysis, we returned to the passive fixation paradigm described above because the visual stimulus and eye positions remained constant throughout the entire experiment. Changes in pupil area are therefore likely to reflect only changes in arousal or attention. Pupil area was recorded throughout the experiment and divided into three epochs: a baseline period containing the 2 min before each tDCS/sham application, a “ramp” period containing the 6 s surrounding stimulation onset, and a “steady-state” period containing the second half of the stimulation block. Data from each epoch were summarized using the median because it is robust against blinks and other outliers.