2.1 Cerebellar tDCS induces mild electric fields across cerebellum

We considered four 3T MRI brain scans of human subjects from [26] (atlas 1, 2, 4, and 5; voxel dimension: 600 μm) and, for each subject, we used the ROAST software suite [27] to segment the images and estimate the spatial distribution of the EF, , induced by 2-mA-cerebellar tDCS (see definition of v in Methods section). We considered two electrode configurations, i.e., regular (R)-tDCS with two pad electrodes (anode and cathode; size: 50 mm×30 mm×3 mm, Fig 1A (i)) and high density (HD)-tDCS with five ring electrodes (1 anode and 4 cathodes; inner radius: 4 mm; outer radius: 6 mm; thickness: 2 mm, Fig 1B (i)). We compared R-tDCS versus HD-tDCS in terms of direction, magnitude, and spatial distribution of the polarization induced in the cerebellum.

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Fig 1. Estimation of the intensity and orientation of the electric field (EF).

(A-B) Estimated intensity and orientation of the EF induced by 2-mA-cerebellar regular tDCS (A) and high-density tDCS (B) in the brain for one human subject, i.e., atlas 5 from [26]. Panels (i) report the position of the anode and cathode for regular tDCS (two pad electrodes) and high-density tDCS (5 ring electrodes), respectively. Panels (ii), (iii), and (iv) report a coronal, sagittal, and axial view of the EF distribution in atlas 5 for regular (A) and high-density (B) 2-mA tDCS, respectively. Colormap in (i) indicates the distribution of the electric potential, v (scale on the left). In (ii)-(iv), color scales on the left indicate the EF intensity, and black arrows indicate the EF orientation.

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During the segmentation process, different conductivities were used for six tissue types (i.e., gray matter, white matter, CSF, skull, skin, and air, see S1 Table), with values 0.126 S/m and 0.276 S/m assigned to white matter, including the cerebellar cortices, and gray matter, including the cerebellar nuclei, respectively. After estimating the EF intensity and orientation across the entire brain, we then focused on the cerebellar regions, which were manually segmented by Park et al. [26]. Segmentation masks were obtained from http://cobralab.ca/atlases/Cerebellum/.

Fig 1A and 1B (ii)-(iv) report the spatial arrangement of the EF along a coronal (ii), sagittal (iii), and axial (iv) section in one subject (atlas 5) for R-tDCS and HD-tDCS, respectively. The sample distribution of the EF intensity estimated for R-tDCS and HD-tDCS in the cerebellar regions of atlas 5 is reported in Fig 2A and 2B, respectively. We estimated the EF intensity distribution separately for voxels spanning the left hemisphere, on which the tDCS anode was placed, and the right hemisphere, and we distinguished between voxels spanning the cerebellar cortices and nuclei. S1A–S1C Fig report the EF intensity distribution for R-tDCS applied on the remaining subjects (i.e., atlas 1, 2, and 4, respectively).

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Fig 2. Distribution of the EF intensity across the cerebellar volume.

(A-B) Sample EF intensity probability distribution estimated for the cerebellar hemispheres in one human subject, i.e., atlas 5 from [26], under a 2-mA cerebellar R-tDCS with two pad electrodes (A) and HD-tDCS with five ring electrodes (B), respectively. Panels (i)-(ii) report the EF intensity distribution estimated for voxels mapping the left hemisphere and right hemisphere, respectively. In both hemispheres, the EF intensity probability distribution is estimated separately for voxels mapping the cerebellar cortex (blue lines) and the cerebellar nuclei (red lines). Anode was placed on the left hemisphere for both R-tDCS and HD-tDCS.

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In case of R-tDCS, we found that, across all subjects, a significant portion of the cerebellum under the anode (i.e., source electrode on the left hemisphere) received EF at intensity between 0.4 V/m and 0.5 V/m, which is consistent with previous studies conducted using SimNIBS software [28,29]. The EF intensity was, on average, higher in the cerebellar nuclei (0.46 ± 0.07 V/m, mean ± S.D. across four atlases) compared to the cerebellar cortex (0.45 ± 0.34 V/m; one-way ANOVA test after Bonferroni correction, P-value P<0.001). However, high EF intensity values (i.e., 1 V/m or above) were found in cerebellar cortex (1.29% of the assigned volume) under the targeted (left) hemisphere but not in the cerebellar nuclei.

The electric field also spilled over the untargeted right hemisphere, Fig 1A and 1B (ii)-(iv). For R-tDCS, EF intensity in the right cerebellar nuclei was comparable to the intensity in the left nuclei (0.43 ± 0.06 V/m, mean ± S.D. across four atlases), Fig 2A (i)-(ii); vice versa, EF intensity in the right cerebellar cortex was significantly lower compared to the contralateral cortex (0.34 ± 0.17 V/m, mean ± S.D.; one-way ANOVA test after Bonferroni correction, P<0.001). Also, on the left hemisphere, EF intensity exceeded 1 V/m and 1.5 V/m in 1.27% and 0.8% of the cerebellar cortex, respectively. On the right hemisphere, instead, EF intensity exceeded 1 V/m and 1.5 V/m in just 0.3% and 0.12% of the cortex, respectively. No voxel spanning cerebellar nuclei received EF intensity at 1 V/m or above.

Compared to R-tDCS, the five-electrode HD-tDCS montage had the anode in a medial position between the two cerebellar hemispheres, Fig 1B (i), and this resulted in a significant reduction of the EF intensity across both hemispheres, Fig 2B (i)-(ii). HD-tDCS-induced EF had lower intensity compared to R-tDCS both in the cerebellar cortices and nuclei and presented no significant trend when transitioning from the target left hemisphere to the right hemisphere (left nuclei: 0.17 ± 0.029 V/m; left cortex: 0.14 ± 0.067 V/m; right nuclei: 0.24 ± 0.038 V/m; right cortex: 0.20 ± 0.075 V/m, mean ± S.D. across four atlases). The average EF intensity across all cerebellar tissues was approximately 49% lower under HD-tDCS compared to R-tDCS (0.20 ± 0.15 V/m versus 0.40 ± 0.27 V/m, respectively), and this remained consistent both for cerebellar nuclei and cortices (one-way ANOVA test after Bonferroni correction, P<0.001).

To further determine whether the differences between R-tDCS and HD-tDCS were influenced by the number and position of the electrodes or the size and shape of the electrodes, we considered the R-tDCS configuration and replaced the pad electrodes with two ring electrodes of the same size as the electrodes used for HD-tDCS, S2A (i) Fig. We found that the modified R-tDCS with ring electrodes produce an EF that is similar in shape to the EF elicited by R-tDCS with pad electrodes, S2A (ii)-(iv) Fig. The EF generated with ring electrodes, though, was both more spatially limited and of weaker intensity across the cerebellum compared to R-tDCS with pad electrodes. The EF intensity, in fact, was reported at 0.35 ± 0.05 V/m (cerebellar nuclei) and 0.31 ± 0.19 V/m (cortex) in the left hemisphere and 0.34 ± 0.05 V/m (cerebellar nuclei) and 0.24 ± 0.09 V/m (cortex) in the right hemisphere (mean ± S.D.), respectively. Also, in case of R-tDCS with ring electrodes, EF intensity across the cerebellar cortex was significant different in the left hemisphere versus the right hemisphere (one-way ANOVA test after Bonferroni correction, P<0.001), S2B (i)-(ii) Fig.

Altogether, these results indicate that cerebellar R-tDCS with pad electrodes produced the strongest and most diffuse EF within the cerebellum, with modest spillover from the target hemisphere to the contralateral hemisphere. EF intensity, though, remained below 1.5 V/m in over 99% of the cerebellar volume, which is consistent with earlier studies [15,16,30] and suggests that tDCS imposes an EF on cerebellar neurons that is mild and below regulatory safety limits [31].

2.2 Cerebellar tDCS induces mild polarization of cerebellar neurons

We investigated the effect of applied EF of various intensity and orientation on multicompartment models of the Purkinje cell (PC), granule cell (GrC), and deep cerebellar neuron (DCN), Fig 3A (i)-(iii). The maximum EF intensity (i.e., 1.5 V/m) was obtained from the estimated EF induced in cerebellum under R-tDCS with pad electrodes, Fig 1A. For each neuron type, the EF orientation was defined with respect to the neuron’s somato-axonal axis and expressed by angle θ, Fig 3A (i). As in [11–13,32], EF pointing towards the axonal terminal of the neuron were defined “anodal” and assigned negative intensity. EF pointing towards the dendritic tree, instead, were defined “cathodal” and assigned positive intensity. EF intensity of 0 V/m indicates no stimulation.

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Fig 3. Coupling EF estimation and multicompartment cerebellar neuron models.

(A) Morphology of the multicompartment neuron models of Purkinje cell (i), granule cell (ii), and deep cerebellar neuron (iii) used in this study. For each model, the orientation of the applied electric field (EF), , is measured by the angle, θ, between and the neuron’s somato-axonal axis. EF oriented towards the dendrites and the axonal terminals are considered cathodal (blue arrow) and anodal (red arrow), respectively. (B) Panel (i) shows the polarization profile along the somato-dendritic axis of the PC soma under anodal EF with orientation θ = 0°. Red and blue indicate maximum depolarization and maximum hyperpolarization, respectively (scale not shown). Panel (ii) reports the transmembrane voltage at the PC soma estimated under EF for several combinations of EF intensity and orientation. Negative and positive EF intensities denote anodal and cathodal EF, respectively. Panel (iii) reports the change in somatic transmembrane voltage, ΔV, of the GrC, DCN, and PC model, respectively, as the EF intensity increases (θ = 0°), along with the polarization length (s). In (ii)-(iii), neuron models were simulated with blocked sodium channels to prevent the generation of action potentials.

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Neuron models under EF-induced polarization were simulated in NEURON [33]. The PC and DCN models were presented in [34] and [35], respectively. The GrC model was developed for this study by adding the Hodgkin-Huxley models of ionic channels proposed in [36] to the morphology of a granule cell from an adult human subject (cell NMO_32569) from NeuroMorpho.Org [37], Fig 3A (ii). Electrotonic properties and maximum ionic conductance values of the GrC model are reported in S2 and S3 Tables, respectively. Other cerebellar neuron types (e.g., Golgi cells and interneurons) were omitted in this study because they are significantly less numerous and more sparsely distributed compared to PC, DCN, and GrC.

For each neuron type, we adopted the quasi-uniform assumption [38] and calculated the EF-induced polarization as proposed in [39–41]. Specifically, for any neuron compartment, n, the extracellular potential, V_e,n, was assigned as , where V_e,0 is the extracellular potential of a reference node (i.e., the soma compartment), E is the EF intensity, and is the distance of the compartment to the reference node along the EF direction. Each neuron compartment was assigned an extracellular potential via the extracellular() mechanism in NEURON.

As in previous studies [42], a linear polarization profile was observed along the soma and dendrites of PC model, Fig 3B (i), and a similar trend was observed for the GrC and DCN models. When applied to the neuron membrane, though, the EF-induced polarization profile elicited limited variation to the somatic transmembrane voltage at rest (PC: ±0.78 mV; DCN: 1.26 mV; GrC: ±0.12 mV, for EF intensity at ±1.5 V/m, respectively; values calculated while ionic channels are blocked), regardless of the EF orientation or intensity, Fig 3B (ii). The maximum variation, ΔV, to the somatic voltage at rest, instead, occurred when the EF is aligned with the dendritic-somato-axonal axis, i.e., θ = ±1°, Fig 3B (ii). The modest variation of the transmembrane voltage stemmed from the small amplitude of the EF-induced polarization across all cell types as well as the small slope, s, of the polarization line (a.k.a. “polarization length” or “cell susceptibility” [13]) as the EF intensity increases, Fig 3B (iii).

The change in resting transmembrane voltage had an impact on the firing rate of the PC and DCN. These neurons exhibit tonic spiking at rest when no tDCS is applied (61.3 Hz versus 27.2 Hz, PC model versus DCN model). For EF intensities up to ±1.5 V/m (θ = 0°), the change in firing rate was noticeable for the PC model (2.14% of the baseline value, i.e., 1.31 Hz) and larger compared to the DCN model (0.29% of the baseline value, i.e., 0.08 Hz), which is consistent with previous in vitro findings [43,44]. The GrC model, instead, was silent at rest, and EF intensities up to ±1.5V/m did not facilitate action potentials, likely because of the small geometric size of the GrC model compared to PC and DCN. Small cells like GrC, in fact, have higher axial resistance and shorter length constants compared to cells like PC and DCN due to smaller diameters (see S2 Table), and this determines a lower polarization response to applied electric fields [45].

Altogether, these results indicate that cerebellar neurons have low susceptibility to tDCS currents up to 2 mA. tDCS-induced EF, in fact, would cause generally mild variations to the neurons’ baseline polarization and firing pattern at rest. Among these neurons, small cells like GrC remain likely unaffected by tDCS. Purkinje cells and deep cerebellar neurons, instead, have comparable susceptibility to EF but only Purkinje cells will likely experience a change in tonic firing under tDCS.

2.3 Rebound firing of deep cerebellar neurons is unaffected by cerebellar tDCS

We then investigated the effect of EF on the response of cerebellar neurons to exogenous stimuli. We focused on GABAergic input from Purkinje cells, as the resultant inhibition can cause a robust rebound spiking in the DCN, whose duration and rate are related to the strength and timing of motor responses [46]. We concurrently applied a single stimulus to all GABAergic synapses disseminated along the DCN dendrites and soma and measured the resultant rebound spiking at the soma, Fig 4A (i) (inset). The rebound spiking occurred at the end of a rapid repolarization of the DCN membrane (~50 ms), consisted of a sequence of high-frequency action potentials, and terminated with prolonged adaptation of the instantaneous firing rate (IFR), Fig 4A (i).

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Fig 4. Predicted effects of EF on deep cerebellar neuron and granule cell models.

(A) Panel (i) shows the instantaneous firing rate (IFR) of the DCN soma versus time during the after-hyperpolarization (AHP) rebound phase when no EF is applied. Time of synaptic activation is t = 0 ms. Inset: Membrane voltage at the DCN soma during the AHP rebound phase. A red triangle marks the activation of the GABAergic synapse on the DCN dendrites. Panel (ii) shows the variation (Δ) in IFR for the DCN soma during the AHP rebound phase under anodal (red line) and cathodal (blue line) EF at 1.5V/m. Variation are expressed as differences from the IFR under no EF in (i). Inset: Changes are reported as percentual variations from the IFR under no tDCS. (B) Comparison between the multicompartment GrC models proposed in this study (black lines) and in [36] (green lines). The average somatic firing rate (i) and the first-spike latency (ii) during injection of depolarizing current steps in the GrC soma are shown. (C) Relay fidelity of the GrC soma in response to the tonic activation of presynaptic mossy fibers (MF). The ratio between the number of GrC action potentials and MF stimuli versus the frequency of MF stimuli is depicted when no EF (black line) or 1.5 V/m EF (red line: anodal; blue line: cathodal) is applied.

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We found that EF at ±1.5V/m had modest effects on the high-frequency spiking and the subsequent firing adaptation process. The maximum change in IFR was approximately 5 Hz compared to the case without stimulation, Fig 4A (ii), was observed 2-to-3 action potentials after the first rebound spike, and then decayed rapidly to the pre-rebound value within the next few spikes, Fig 4A (ii) (inset). On average, the maximum change in IFR remained within ±4% of the pre-stimulation value (i.e., firing rate: 98.04 Hz; 99.01 Hz; 101.01 Hz; no tDCS, 1.5 V/m anodal EF, and 1.5 V/m cathodal EF, respectively). At the membrane level, EF polarization produced a modest shift in the chloride reverse potential and a small delay in the activation of the hyperpolarization-activated cyclic nucleotide-gated cationic (HCN) currents, which are the main factors shaping the rebound spiking [35].

Altogether, these results indicate that, even though a similar level of polarization is induced in the deep cerebellar nuclei and the cerebellar cortex, the direct effects of tDCS on glutamatergic deep cerebellar neurons are modest in terms of encoding synaptic stimuli from the Purkinje cells. Accordingly, the influence of tDCS on deep cerebellar nuclei is likely indirect and stems from EF-induced polarization of the presynaptic axon terminals of Purkinje cells. This is expected because the spiking rate of the deep cerebellar neuron depends on the balance between the activation rates of glutamatergic and GABAergic synapses [35]. Moreover, tDCS-induced polarization of presynaptic axon terminals has been shown in vitro to alter the efficacy of synaptic transmission [12].

2.4 GrC responses to mossy fiber activation are robust against tDCS

Next, we considered the response of the GrC model to applied EF. The GrC model predictions before EF application were compared against in vitro data reported in [36]. Specifically, we applied step currents of increasing intensity to the GrC soma and measured the induced firing rate and the latency between the current injection and the time of the first action potential. These two metrics were used to assess the validity of the GrC model. Due to difference in size between the granule cells considered in our study and in [36], the current density (i.e., injected current divided by cell surface) associated with each current step was calculated.

We found that the metrics calculated for the GrC model were closely aligned with the data for step currents at 10 μA/cm² and above, Fig 4B (i)-(ii). Differently from [36], though, currents less than 8 μA/cm² did not elicit action potentials, Fig 4B (i). This may be due to the different size of our GrC model, which was reconstructed from a human granule cell and had a larger surface compared to the rodent GrC considered in [36], and the lack of transient receptor potential (TRP) channels [47] in our model. This was further confirmed by the long latency to the first action potential that was reported in [36] for currents around 10 pA (i.e., 10 μA/cm²), Fig 4B (ii).

The GrC model was then used to investigate the fidelity of the GrC response to mossy fibers (MF) under EF. Recent evidence indicate that granule cells can show adaptation and acceleration in response to MF as the frequency of MF stimuli increase, and such adaptation can directly affect short-term synaptic plasticity throughout the cerebellar cortex [47].

We applied glutamatergic synapses on the GrC dendrites and mimicked MF activation with a regular sequence of presynaptic stimuli whose frequency varied linearly up to 140 Hz. For every value of the MF stimulation frequency, the GrC model was allowed to reach steady state, and then the relay fidelity of the GrC was measured as the ratio, r, between the number of action potentials evoked at the soma and the number of MF stimuli delivered in a 2,000-ms window.

We found that, under no tDCS, adaptation emerged for MF stimulation frequencies greater than 40 Hz and resulted in a rapid reduction of the relay fidelity r, followed by a linear decay for frequencies up to 140 Hz, Fig 4C. Compared to no tDCS, -1.5 V/m EF (anodal) and +1.5 V/m EF (cathodal) induced mild changes to the relay fidelity. These changes depended on the MF stimulation frequency and were mainly observed for 80 Hz MF stimulation, Fig 4C. In this case, anodal EF decreased the relay fidelity ratio (r = 0.429; 2.81% decrease) compared to the baseline value under no EF (r = 0.441). Cathodal EF, instead, increased the ratio (r = 0.453, i.e., 2.81% increase). For MF stimulation frequencies beyond 80 Hz, the difference between the values of r under anodal EF and cathodal EF was less than 1.2% of the baseline value at all times and for every EF intensity up to 1.5 V/m.

Overall, these results suggest that tDCS-induced EF have limited effect on the firing activity of granule cells. Combined with the modest modulation of DCN reported above, these results suggest that the modulatory effect of cerebellar tDCS primarily focuses on the outer layers, with Purkinje cells in the cortex being the most prominent target.

2.5 Cerebellar tDCS modulates the response of Purkinje cells to synaptic stimuli

We quantified the effects of cerebellar tDCS on the response of Purkinje cells to synaptic stimuli. First, we measured the transmission delay, T_d, which is the time required for an impulse at the most distal dendritic compartment to travel to the soma and evoke an action potential. As in [34], we blocked Na⁺ and Ca²⁺ channels in the axonal initial segment of the PC model and let the soma compartment transitioning from silence to tonic spiking in response to a distal dendritic input, Fig 5A (inset).

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Fig 5. Predicted effects of EF on Purkinje cell models.

(A) Transmission delay, T_d, of the PC in response to distal dendritic stimuli under EF versus EF intensity. Inset: Definition of T_d as interval between the post-stimulus activation of the dendrite (red) and the action potential at the soma (black). (B) Phase-response curve (PRC) of the PC model estimated without stimulation (black line) and EF at 1.5 V/m (red line: anodal; blue line: cathodal). (C) Examples of transmembrane voltage at the PC soma during dense (i), irregular (ii), bursting (iii) and sparse (iv) firing, respectively, with no EF. For each example, the coefficient of variation (CoV) of the spiking pattern is reported. (D) Effects of EF on the postsynaptic firing rate (FR) at the PC soma. Panels (i), (ii), (iii) show the mean FR at the soma for different combinations of δ_syn and EF intensity, E (i), mean FR versus δ_syn (E = 0 V/m) (ii), and mean FR versus EF intensity (δ_syn = 0) (iii), respectively. (E) Combinations of EF intensity and δ_syn for which the PC soma exhibits dense (red), irregular (blue), bursting (green), or sparse (gray) firing. (F) Effects of EF on the postsynaptic instantaneous firing rate (IFR). Panels (i)-(ii) show the maximum IFR at the soma for various combinations of δ_syn and EF intensity, E (i), and the maximum IFR vs. EF intensity (δ_syn = 0) (ii), respectively.

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We found that, while the steady-state firing rate (FR) at the soma was modestly affected and varied linearly with the EF intensity (correlation value R = 0.99; coefficient of determination R² = 0.97), the transmission delay was dramatically affected by EF orientation and intensity. First, anodal EF consistently reduced T_d while cathodal EF significantly increased T_d, Fig 5A. Second, the percentual variation of T_d from the baseline value (i.e., T_d = 8.5 ms when no EF was applied) increased linearly with the EF intensity (Fig 5A), ranged from a 71.8% decrease (T_d = 2.4 ms, 1.5 V/m anodal EF) to a 134.1% increase (T_d = 19.9 ms, 1.5 V/m cathodal EF), and resulted in an average variation for T_d of 5.8 ms per 1 V/m increment of the electric field.

Because of the importance of PC spike timing in learning and coordination [48,49], we also measured the effects of EF on the phase response curve (PRC) of the Purkinje cells when parallel fibers are activated. The PRC measures the sensitivity of Purkinje cells to the presynaptic stimuli [50], and therefore changes to the PRC indicate a shift in the excitability of Purkinje cells. Fig 5B shows the PRC estimated when no tDCS is applied and for EF at ±1.5 V/m. The effects of the EF were mainly concentrated at phase ϕ = 0°, where anodal EF produced a maximum anticipation of the phase response by 6.2%, which corresponds to a 0.66-ms-reduction in latency between the synaptic stimulus and the somatic action potential. Cathodal EF, instead, resulted in a maximum phase increment of 5.3%, i.e., 0.65 ms increase in latency. No significant alteration was reported, instead, at higher phases for anodal or cathodal EF, while changes to the PRC at low phase values remained less than 5% for EF below 1.5 V/m.

Altogether, these results suggest that tDCS has a modest impact on the sensitivity of Purkinje cells to inputs from the parallel fibers. The impact can be attributed primarily to the response of Purkinje cells to stimuli arriving close to simple spikes and results in a modulation of less than 1 ms. Hence, although it is possible that tDCS alters the cerebellar response to cortical projections, the effects of cerebellar tDCS are likely focused on the activity along the parallel fibers and/or the potentiation of the synapses onto the Purkinje cells rather than on the encoding capabilities of Purkinje cells in response to cortical projections.

2.6 Cerebellar tDCS modulates the synaptic inhibition of Purkinje cells

We then considered the effects of tDCS on the spiking pattern that Purkinje cells display in response to input from the parallel fibers. We created trains of presynaptic stimuli for each one of over 1,000 glutamatergic and GABAergic synapses on the PC dendrites, and we studied the resultant spiking pattern elicited at the PC soma, both with and without EF application. The trains of presynaptic stimuli were designed to reproduce the combination of feedforward inhibition due to granular layer interneurons and diffused depolarization due to parallel fibers [51,52], and resulted in a cumulative presynaptic input to the PC model with spectral proprieties similar to those of field potentials recorded in vivo at rest in rodent models [53]. See S3A Fig for a power spectrum analysis of the cumulative synaptic activation sequence.

Depending on the synaptic gain scaling factor, δ_syn, used in the definition of the synaptic input (see definition in the Methods section), four types of discharge patterns were defined at the soma compartment when no stimulation is applied, i.e., dense (inter-spike intervals [ISI] are less than 80 ms, and the ISI coefficient of variance [CoV] is less than 0.5), irregular (the maximum ISI value is between 80 ms and 200 ms, and the ISI CoV value is between 0.5 and 2), bursting (the maximum ISI value is above 200 ms, and the ISI CoV value is above 2), or sparse (i.e., the pattern includes only a few spikes followed by a silent periods exceeding 3,000 ms, and the ISI CoV, if defined, is greater than 3), see example patterns in Fig 5C (i)-(iv), respectively. Conditions for firing pattern classification were modified from [54], and the classification of bursting patterns versus sparse patterns was set based on inspection of the cases with CoV>3.

In case of no stimulation, the average somatic firing rate (FR) across all discharge patterns was 29.5 ± 0.3 Hz (δ_syn = 0) and ranged between 6.5 ± 0.1 Hz (δ_syn = -0.6) and 48.4 ± 0.1 Hz (δ_syn = +0.6), Fig 5D (i) (mean ± S.D. across three distinct combinations of presynaptic trains and synapse locations on the PC dendrites). The range of FR was consistent with the firing rate values observed in vivo (e.g., see Table 1 in [55]) and depended linearly on δ_syn, Fig 5D (ii) (slope = 173.2 Hz per unit increment of δ_syn, R² = 0.966).

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Table 1. Effects of tDCS on Average PC Firing Rate.

Effects of the tDCS-induced EF intensity, E, and the synaptic gain on the average firing rate at the PC soma. δ_syn indicates the variation from the baseline synaptic gain expressed in percentage. Firing rates are in Hz, and percentual changes in firing rate are computed with respect to the no-tDCS case under the same level of synaptic gain.

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Applied EF resulted in two significant changes to the PC firing pattern. First, the average FR at the PC soma varied widely in response to EF intensity, Fig 5D (iii) and Table 1. For δ_syn >0, EF at 1.5 V/m and EF at 0.75 V/m varied the FR up to 25% and 12%, respectively, compared to the baseline FR (i.e., estimated when no EF is applied), Fig 5D (i), and the variation was statistically significant compared to baseline (two-sample t-test, P-value P<0.01, Table 1). For reductions in the synaptic conductance exceeding 20% (i.e., δ_syn<-0.2), cathodal EF reduced the average FR by up to 84.7% compared to baseline (EF intensity: 1.5V/m; δ_syn = -0.6, two-sample t-test, P-value P<0.01), while anodal EF maintained the baseline FR (31.1 ± 1.6 Hz; EF intensity: -1.5V/m; δ_syn = -0.6; no significant difference from the baseline 29.5 ± 0.3 Hz; two-sample t-test).

In addition, EF effectively modulated the spiking pattern at the PC soma. First, the CoV of the ISI was highly sensitive to the combination of the synaptic gain and EF intensity, with the largest variations occurring for δ_syn<0, S3B Fig. Secondly, although the type of pattern remained largely consistent as the EF intensity varied, we found that the PC soma can transition from an irregular pattern to a dense, bursting, or sparse pattern for EF intensities as low as 0.75 V/m, depending on the value of δ_syn, and the shift in pattern is more likely to occur for δ_syn<0, Fig 5E.

Finally, to assess whether these changes in pattern could be due to an alteration of the baseline FR of the PC model before synaptic activation, we measured the maximum instantaneous firing rate (IFR) at the soma for several combinations of synaptic gain δ_syn and EF intensity. We found that, while the maximum IFR was highly dependent of the synaptic gain, i.e., it varied from 64.4 Hz to 98.9 Hz as δ_syn ranged from -0.6 to +0.6, Fig 5F (i), the maximum IFR was modestly affected by the EF intensity, see Fig 5F (ii) for the case δ_syn = 0 and Table 2 for other values of δ_syn.

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Table 2. Effects of tDCS on Maximum Instantaneous PC Firing Rate.

Effects of the tDCS-induced EF intensity, E, and the synaptic gain on the maximum instantaneous firing rate (IFR) at the PC soma. δ_syn indicates the change from the baseline synaptic gain expressed in percentage. IFR values are in Hz, and percentual changes in firing rate are computed with respect to the no-tDCS case under the same level of synaptic gain.

https://doi.org/10.1371/journal.pcbi.1009609.t002

The behavior of the IFR under EF indicates that the variation of PC discharge pattern did not result from a modulation of the PC spontaneous FR, i.e., before synaptic activation. Instead, it likely depends on the polarization of the cell membrane, which is either shifted towards values closer to the spiking threshold (i.e., in case of anodal EF) or towards values further away from the spiking threshold (i.e., in case of cathodal EF). To further clarify this point, we considered the scenario reported in S3C Fig. Here, we set δ_syn = -0.4, used the presynaptic pulse trains in Fig 5D–5F, and varied the EF intensity between -1.5V/m and +1.5V/m. Panels (i)-(v) in S3C Fig report the synaptic current I_syn(t) (red line, see definition in Eq (2) in the Methods section) after low-pass filtering (5^th order Butterworth filter, cutoff frequency: 30 Hz), and the somatic transmembrane voltage (black line) for different EF intensities. We observed that, while EF affected I_syn(t) moderately, the resultant spiking pattern varied significantly and switched from bursting without stimulation, S3C (iii) Fig, to irregular under anodic EF, S3C (i) and (ii) Fig, or drastically thinned under cathodal EF, S3C (iv) and (v) Fig. Under cathodal EF, the inhibitory effects of the hyperpolarizing synaptic currents were amplified and resulted in a prolonged suppression of spiking.

Altogether, these results indicate that, through modulation of the postsynaptic activation, cerebellar tDCS can alter the timing and duration of the feedforward inhibition to the Purkinje cells. This would result in a modulation of the spiking pattern elicited by bouts of excitatory synaptic stimuli, but the magnitude of such modulation critically depends on the strength of the synapses along the PC dendritic tree. The latter point is interesting because previous studies [43,44] have primarily used alternate, high-intensity EF to modulate the firing pattern of Purkinje cells and have shown that Purkinje cells can be entrained in a common, EF-driven rhythm. Our simulations now suggest that constant, low-intensity EF can also modulate Purkinje cells, but the mechanisms of modulation would be different compared to alternate EF. While no common rhythm would be evoked, constant low-intensity EF can steer Purkinje cells to discharge patterns that have similar statistical properties. Also, given the range of EF intensities reported across the cerebellar region, this similarity can be promoted on neurons across a large region of cerebellar cortex, thus suggesting that the effects of tDCS could be diffused well beyond the area under the electrode.

Finally, these results suggest that anodal stimulation can impede the emergence of bursting patterns in favor of an irregular, upregulated spiking activity by lifting the PC above the spiking threshold, whereas cathodal EFs likely result in a more sporadic spiking due to membrane hyperpolarization, which may be associated with reduced neural encoding and motor learning [56].

2.7 Cerebellar tDCS modulates the silence period following complex spikes

A complex spike (CS) is generated at the soma of Purkinje cells in response to climbing fiber activation and consists of a large spike followed by 2 to 4 spikelets [57,58], Fig 6A (i). Complex spikes are important to the cerebellar function as they mediate the response to unconditioned stimuli and control the redistribution of the synaptic weights across different dendritic branches of the Purkinje cell [59,60].

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Fig 6. Predicted effects of EF on PC complex spiking and silence period.

(A) Examples of complex spike (CS) generated by the PC model when no EF is applied (i) and EF with intensity -1.5 V/m (ii) and +1.5 V/m (iii) is applied, respectively. (B) Panel (i) shows that evoking a CS (black triangle) disrupts the tonic spiking of the PC soma and elicits a prolonged repolarization phase. Panel (ii) magnifies the area within the dashed red box in (i) and defines the silence period T_sp as the interval between the highest spikelet of the CS and the first spike after the repolarization phase following the CS. (C) T_sp versus EF intensity (black line). T_sp versus EF intensity is also reported when no EF is applied and a bias, V_bias, is applied to the Nav1.6 channels only (blue line) or all ionic channels (red line) of the PC soma. For every field intensity, V_bias is the polarization induced by the EF on the ionic channels (range: ±0.215 mV).

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We found that the morphology and duration of the CS were affected by tDCS, where changes were modest in size compared to the no stimulation case, and no trend occurred as the EF orientation and intensity changed, Fig 6 (i)-(iii), even though cathodal EFs occasionally resulted in one additional spikelet at the end of the CS, e.g., Fig 6A (iii).

The silence period, T_sp, between a CS and the following simple spike (Fig 6B), instead, is an important feature of complex spikes [61] and was significantly affected by tDCS. Specifically, Fig 6C reports T_sp as a function of the EF intensity and indicates that anodal EF caused consistent reductions (i.e., up to -22.4% compared to no tDCS) in silence period while cathodal EF caused a remarkable prolongation of the silence period, i.e., up to +80% of the value under no tDCS (T_sp = 440.6 ms, 342.1 ms, and 792.4 ms for no EF, -1.5 V/m EF, and +1.5 V/m EF, respectively).

To further understand the cellular mechanisms that mediate the variation in silence period, we considered the PC model under no EF and selectively applied the EF-induced polarization to the soma compartment, while the other compartments remained unaltered. This resulted in a bias, V_bias, to the transmembrane voltage at the soma versus the other compartments, with V_bias depending on the soma’s length constant (range: ±0.215 mV for EF at ±1.5V/m, see red line in Fig 6C).

Alternatively, we considered the PC model under no stimulation and selectively applied the bias V_bias to the transmembrane voltage applied to somatic Na⁺ v1.6 (Nav1.6) channels (Fig 6C, blue line). Remaining ionic channels and compartments, instead, were unaltered. Nav1.6 channels are responsible for the initiation of simple spikes and mediate the rapid recovery from inactivation in Purkinje cells, thus controlling the duration of the silent period [62]. Accordingly, this manipulation aimed to assess whether a shift in the Nav1.6 dynamics due to the polarization of the cell membrane can modulate the recovery period leading to simple spiking.

We found that a selective polarization of the soma resulted in a modulation of the silence period like the nominal case (Fig 6C, red line). Vice versa, a selective alteration of the Nav1.6 channel dynamics resulted in a more rapid variation of the silence period (Fig 6C, blue line), with higher reduction in T_sp for negative values of V_bias, which correspond to anodal stimulation, and a prolongation of the silence period beyond 10,000 ms for V_bias≥0.1 mV.

Altogether, these results indicate that the modulation of the silence period under tDCS is primarily mediated by somatic polarization. A change in transmembrane voltage at the soma likely causes a rapid modulation of the Nav1.6 channels, which is partially compensated by the activation of K⁺ channels. This would result in higher sensitivity of the CS to cathodal EF versus anodal EF. This is further confirmed by the fact that Nav1.6-mediated currents are highly sensitive to EF intensity compared to the remaining currents, see S4 Table.

2.8 Cerebellar tDCS modulates spontaneous bursting in Purkinje cells

Purkinje cells exhibit a bursting pattern with limited duration upon receiving steady depolarization [63], e.g., see Fig 7A for a 2-nA-step current injected in the soma at time t = 200 ms. When observed in vivo, this bursting can contribute to the short-term memory capabilities of the cerebellar network [64,65] and is mediated by a sustained depolarization of the PC dendrites secondary to the activation of granule cell axons [23]. It can also be elicited by activating climbing fibers in response to sensory stimuli [66].

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Fig 7. Predicted effects of EF on PC bursting.

(A) Spontaneous bursting at the PC soma. The somatic transmembrane voltage is depicted when no EF is applied. Inset: Definition of burst period (T_burst), burst interval (T_intv), and spiking period (T_spike). (B-D) T_burst (B), T_intv (C), and T_spike (D) are reported for consecutive bursts at the PC soma under no EF (black line) and 1.5 V/m EF (red line: anodal; blue line: cathodal), respectively. (E) Count of consecutive bursts at the PC soma versus EF intensity (black line). Burst count versus EF intensity is also reported when no EF is applied and a bias, V_bias, is applied to all dendritic ionic channels (green line), all somatic ionic channels (red squares), or somatic K⁺ channels only (i.e., Kv1.1, Kv3.4, and Kir2.3; blue circles). (F) Mean burst period, , at the soma versus EF intensity (black line). versus EF intensity is also reported when no EF is applied and a bias, V_bias, is applied to all dendritic ionic channels (green line), all somatic ionic channels (red line), or somatic K⁺ channels only (blue line). is the average value of T_burst across the first 5 consecutive bursts. For every EF intensity in (E-F), V_bias is the polarization induced by EF on the ionic channels (range: ±0.215 mV).

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The effects of applied EF on the burst period, T_burst (Fig 4A, inset), and the count of bursts in the entire sequence were assessed as the EF polarization and intensity varied. We found that the duration of the transient bursting sequence and the burst count varied up to 25% for EF at ±1.5 V/m (duration burst sequence: 1,620 ms, 1,207 ms, and 1,993 ms; burst count = 12, 9, and 15; values reported for no EF, -1.5 V/m EF, and +1.5 V/m EF, respectively). The burst period, instead, increased over consecutive bursts as the transient bursting pattern decreases, Fig 7B.

By further decomposing the burst period into an inter-burst interval and a spiking phase (T_intv and T_spike, respectively, Fig 7A inset), we found that the variation in the burst period was mainly contributed by the inter-burst interval, whose relationship with the number of bursts follows a similar trend as T_burst, Fig 7C. In contrast, the spiking period, T_spike, of each burst varied modestly under EF, with no significant trend across the EF intensities, Fig 7D. Furthermore, the average value, , of the burst period over the first five consecutive bursts was 4% higher under anodal EF and 3% lower under cathodal EF compared to baseline (no EF: 116.7 ± 6.0 ms; -1.5 V/m EF: 121.1 ± 7.6 ms; +1.5 V/m EF: 113.4 ± 5.0 ms, respectively; mean ± S.D.).

To further understand the cellular mechanisms that mediate the rate adaptation during transient bursting, we considered the PC model under no EF and selectively applied the polarization due to the applied EF to the soma, while the other compartments remained unaltered (case 1), or the dendrites, while the other compartments remained unaltered (case 2). The reason for comparing case 1 versus case 2 is that the transient bursting pattern may be regulated by calcium (Ca²⁺) channels distributed across the dendrites [25].

We selectively applied a bias, V_bias, to the transmembrane voltage of the compartments of interest in both cases, and we determined the resultant burst count (Fig 7E) and mean burst period (Fig 7F). We found that the dendritic polarization (case 2, green lines in Fig 7E and 7F) under applied EF modestly altered the mean burst period (e.g., T_burst slightly decreases for EF above 0.6 V/m) with no change to the burst count, while the adaptation of the firing pattern over consecutive bursts was mainly mediated by somatic polarization (case 1). Under somatic polarization, in fact, the burst count ranged from 9 to 15 (Fig 7E, red squares) and the mean burst period ranged from 113.8 ± 5.9 ms to 120.9 ± 7.3 ms (Fig 7F, red line), which correspond to a variation of -2.5% and +3.7% from the value under no EF, respectively.

Finally, we determined whether the burst adaptation depends on the polarization of all somatic ionic channels or mainly the voltage-gated K⁺ channels (Kv1.1, Kv3.4, and Kir2.3, [34]), as these channels are expected to control the inter-burst interval [25]. We found that a selective polarization of the voltage-gated K⁺ channels resulted in a similar number of bursts as in case 1 (i.e., polarization of all somatic ionic channels), Fig 7E (blue circles) but led to an oversensitivity of the mean burst period to the EF intensity (Fig 7F, blue line, percentual change from the no-stimulation case: -8.4% to +11.0%). Vice versa, a selective polarization of somatic Na⁺ and Ca²⁺ channels or calcium-activated K⁺ channels (rather than the voltage-gated K⁺ channels) resulted in negligible changes to the burst count and mean burst period, i.e., less than 1%.

Altogether, these results indicate that tDCS-induced EF can modulate transient bursts and rate adaptation in Purkinje cells by up to 25% of the baseline value. This modulation is primarily mediated by the polarization of the somatic ionic channels, with the changes to the burst period being primarily driven by modulation of the K⁺ currents.

2.9 Spatial distribution of the PC response to cerebellar tDCS

We considered the EF induced by R-tDCS with pad electrodes (Fig 1A) on each subject and determined the distribution of the angle, θ (Fig 2A), between EF and the somatic-axonal axis of the Purkinje cells along the cerebellar cortex. We then used θ to estimate the polarization induced on the Purkinje cells in every voxel along the cerebellar cortex. We repeated this for the EF induced by anodal tDCS and cathodal tDCS. The procedure to compute the EF projections for the segmented cerebellar cortices is reported in the Methods section.

Fig 8A reports the intensity of the EF projections across all voxels covering the cerebellar cortex of atlas 5 (both hemispheres). In case of anodal tDCS, EF projections with high intensity were mainly concentrated on the dorsal side of the cerebellum and on the left hemisphere, while, in case of cathodal tDCS, EF projections with high intensity were mainly concentrated on the rostral side of the cerebellum. PC dendritic trees, which are mainly located along the convex gyri of the cerebellar cortex, received anodal polarization (i.e., negative values and warm colors on the color scale). PC axonal terminals, instead, are primarily located along the concave sulci and were aligned with the entering EF, thus receiving mainly cathodal polarization (i.e., positive values and cool colors on the color scale). The intensity of the EF projections had a similar distribution across hemispheres for the remaining subjects, see S4A Fig.

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Fig 8. Distribution of EF-induced variations of PC spiking across the cerebellar cortices.

(A) Distribution of the EF projection onto the somato-dendritic axis of Purkinje cells along the cerebellar cortical surface for one subject, i.e., atlas 5 in [26]. Camera view azimuth and elevation angles are -90° and 0°, respectively. (B) Sample distribution of the angle θ between the PC somato-dendritic axis and the EF along the cerebellar cortical surface (gray line: right hemisphere; black line: left hemisphere). The tDCS pad electrode was placed on the left cerebellar hemisphere. (C) Distribution of the EF projections on the somato-dendritic axis of Purkinje cells for the left hemisphere (black line) and the right hemisphere (gray line), respectively, under anodal R-tDCS. Anodal EF intensities (red arrow) and cathodal EF intensities (blue arrow) are indicated. (D-F) Distribution of mean firing rate under presynaptic activation (δ_syn = 0) (D), silence period, T_sp (E), and mean burst duration, (F), respectively, of the Purkinje cells along the cerebellar cortical surface. Panels (i) in (D-F) show the spatial mapping of the estimated values on the cerebellar cortical surface of atlas 5 (azimuth: -90°; elevation: 0°). Panels (ii) in (D-F) report the sample distribution of the estimated metrics across four subjects. In (D), panel (ii) reports the distribution of the mean firing rate for δ_syn = 0 (blue line) and δ_syn = – 0.6 (red line), respectively. Silence period in (E) and mean burst periods in (F) are calculated as in Figs 6 and 7, respectively.

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Across all subjects, the portion of left cerebellar cortex where Purkinje cells receive a projected EF higher than 0.5V/m accrued to 3.0 ± 0.6% for anodal tDCS (1.16 ± 0.53% received EF <-1.5 V/m) and 0.93 ± 0.37% for cathodal tDCS (0.47 ± 0.48% received EF > 1.5V/m). The portion of right cerebellar cortex that received a projected EF higher than 0.5 V/m was 1.56 ± 1.12% (anodal tDCS) and 0.37 ± 0.23% (tDCS). Table 3 reports the cumulative percentage of cerebellar cortex across both hemispheres for every subject and averaged across all subjects. Smaller percentages, instead, were obtained with HD-tDCS configurations because of the reduced spatial extent of the electric field induced by tDCS, see S4B (i)-(ii) Fig for HD-tDCS with two and five ring electrodes, respectively.

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Table 3. Percentage of Purkinje Cells Under Field Intensity Exceeding 0.5 V/m and 1.5 V/m.

Percentage of Purkinje cells under projected EF intensities exceeding 0.5 V/m and 1.5 V/m in each atlas and averaged across all atlases, mean ± S.D.

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Altogether, these results indicate that anodal tDCS evokes a net polarization of the cerebellum on both hemispheres, even though the largest polarization occurs in the hemisphere underneath the electrode pad. Moreover, Fig 8B reports the distribution of the angle θ in both hemispheres and indicates that the distribution exhibited symmetry around θ = 90°, was dispersed across the entire range 0°-180° (86.8% of the distribution falls in the range 30°-150°) in the targeted (left) hemisphere and had a unimodal distribution in the right hemisphere. A validation of the method used to estimate the angle θ between the PC axis and the EF is shown in S5 Fig (details in S1 Text).

The EF projections also showed a symmetric distribution in both hemispheres, with almost equal populations of Purkinje cells receiving polarization effects that are consistent with anodal stimulation and cathodal stimulation, Fig 8C. A significant difference, though, occurred between hemispheres (PC population that received anodal EF projections: 52.0 ± 0.31% versus 48.1% ± 0.25%, left versus right hemisphere; two-sample t-test, P-value P<0.001). We estimated that, across the whole cerebellum, roughly equal numbers of Purkinje cells are either depolarized by anodal EF or hyperpolarized by cathodal EF, even though there is a slight preference over one direction depending on the electrode polarity over the cerebellum. Specifically, when the anodal electrode is placed on the cerebellum, a few more PCs would be depolarized than hyperpolarized. The opposite scenario would occur, instead, by placing the cathodal electrode on the cerebellum.

Based on the values of the EF locally projected onto the Purkinje cells, we estimated the spatial distribution of the average firing rate (FR) of the PC soma under polysynaptic activation. We also estimated the distribution of the post-CS silence period, T_sp, and average burst period, . Fig 8D, 8E and 8F report the variation of FR, T_sp, and from the baseline values (i.e., values estimated under no stimulation) throughout the cerebellar cortex when 2-mA R-tDCS is applied. Across four subjects, we found that, under nominal synaptic gain value (i.e., δ_syn = 0) the stimulation caused a significant departure of the average FR from the baseline value, Fig 8D (i) (average variation: 5.68 ± 0.78%, mean ± S.D.; one-sample t-test, P-value P<0.0001), with approximately 9.91%, 1.47%, and 1.07% of Purkinje cells reporting a change (either increase or decrease) of more than 10%, 20% and 30%, respectively, across the entire cerebellum, Fig 8D (ii) (blue line). In case of stronger synaptic inhibition (i.e., δ_syn = -0.6), instead, tDCS resulted in a wider range of FR values, i.e., 3–24 Hz, and caused a significant shift in the average FR from the baseline (average variation: 95.1 ± 23.8%, mean ± S.D.; one-sample t-test, P-value P<0.0001), with 69.2% of Purkinje cells reporting a change (either increase or decrease) of more than 30%. A similar trend was reported for T_sp, Fig 8E (i), and , Fig 8F (i), even though the maximum variation was smaller, i.e., ±10% and ±4%, respectively. Moreover, the percentages of Purkinje cells that exhibited a reduction or an increment of T_sp higher than 10% and higher than 2% were similar (T_sp: 6.01% ± 1.40%; : 0.79% ± 0.24%), Fig 8E and 8F (ii), thus resulting in a limited average variation across the cerebellum (average change: 1.28% ± 0.38% and 0.16 ± 0.00% for T_sp and , respectively, mean ± S.D.).

Altogether, these results indicate a net anodal polarization across the cerebellar cortex, with a skewed distribution of higher EF intensities toward the region directly underneath the electrode. This distribution (i) primarily affects the discharge pattern of the Purkinje cells that receive strong synaptic inhibition, (ii) causes a significant shift in the spiking mode across the entire cerebellar cortex, and (iii) results in a variation of the average firing rate that may be, in localized regions of the cortex, as high as 90% of the pre-stimulation baseline value, depending on the underlying level of synaptic activity. Moreover, the changes in silence period and the small but steady variation of the average burst period suggest that 2-mA-tDCS can evoke a significant, polarization-dependent modulation of the activity of Purkinje cells in vivo, which can directly influence the mechanisms of neural encoding and synaptic plasticity controlled by the spiking of the Purkinje cells.

3.1 Translational implications for clinical applications

Cerebellar tDCS has been recently proposed as a neuromodulation therapy to treat motor dysfunctions in ataxia [2,3], focal dystonia [90], and essential tremor [91,92], but the mechanisms of action remain unclear, and the titration of the stimulation has resulted remarkably challenging thus far. Our study provides a characterization of the acute effects of tDCS on cerebellar neurons and shows that the effects can both recapitulate and reconcile a diverse range of clinical results recently reported in the literature. More importantly, our study indicates that, depending on the placement of the electrode on cerebellum, the consistent polarization of the Purkinje cells underneath the electrode region may provide a bulk effect that can directly percolate to the cerebellar nuclei. Since abnormal oscillations and pathological cerebellar activity are expected to mediate the manifestation of motor disfunctions [53,93,94], our study contributes to understand the mechanisms through which tDCS may alter these abnormal activity patterns, thus enabling the treatment of movement disorders.

Clinically, severe neurological conditions of various etiology have been linked to a maladaptive change of the excitability and firing pattern of Purkinje cells. For instance, a dysregulation of the Purkinje cells’ postsynaptic activity has been shown to lead to the motor symptoms of restless leg syndrome [95], vestibulo-ocular reflex impairment [96], and paroxysmal kinesigenic dyskinesia-induced dystonia [97]. Moreover, a diffused reduction of the Purkinje cells’ excitability throughout the cerebellar cortex has been shown to impair cerebellar zonal patterning and contribute to cerebellar atrophy [98,99], which are both associated with impaired motor learning, loss of coordination, and ataxia. Vice versa, recent optogenetic work showed that a selective inhibition of Purkinje cells of the vermis could help control temporal lobe seizures [100].

Altogether, these studies suggest that the excitability of the Purkinje cells can be a key control factor in the formation of severe symptoms across a wide range of chronic neurological conditions. Accordingly, our findings indicate that cerebellar tDCS can be a useful therapeutic option for several conditions beyond those for which tDCS is currently tested. Moreover, our study points out that, by carefully designing the field intensities and electrode shape and configuration, tDCS can selectively modulate the excitability of the Purkinje cells in specific zones of the cerebellum. Although speculative at the moment, this could have therapeutic effects in seizure control and modulation of nonmotor circuits.

Methods

Numerical calculations of the EF induced by cerebellar tDCS montages in the brain were conducted in ROAST ver. 3.0 [27]. Numerical simulations of multicompartment computer models of the cerebellar neurons were conducted in NEURON, ver. 7.7.2 [33] with CNEXP ode solver and 0.025 ms integration step. Simulation results were analyzed in MATLAB, rel. 2017a, The MathWorks, Inc., Natick, MA. Neuron models and simulation scripts are available in ModelDB, URL: http://modeldb.yale.edu/267189.

Modeling the EF generated by cerebellar tDCS

The 3T MRI atlases from human subjects considered in this study were reported in [26] (atlas 1, 2, 4, and 5) along with segmentation masks to isolate the cerebellar regions. Atlas 3 was not included due to insufficient tissue contrast around the cerebellar areas. The voxel resolution of the brain scans was resampled from 300 μm (original value in [26]) to 600 μm to reduce the computational cost. The R-tDCS montage was simulated with one electrode (anode) centered at location E133, EGI HCGSN-256 EEG system, Electrical Geodesics, Inc., Eugene, OR, and one supraorbital electrode (cathode) centered at location E18. Electrode locations were chosen as in [2] to apply unilateral stimulation over the left cerebellar hemisphere. See [29] for comparison with electrode configurations with cathode centered on the buccinator or deltoid muscles. The current intensity of each electrode was set as 2 mA.

The HD-tDCS montage, which is used to facilitate motor adaptation, was simulated as in [101]. Specifically, we adopted a 10–20 system with five ring electrodes of the same size, with the anodal electrode on the inion (Iz) and four cathodal electrodes in position Oz, O2, P8, and PO8, respectively. The current intensity was set at 2 mA for the anode and 0.5 mA for each cathode electrode.

For each combination of brain scan, tDCS montage, and current intensity, we first segmented the brain images into six tissue categories (S1 Table) and built the volumetric mesh of the reconstructed brain via tetrahedral meshing [102] (MATLAB function iso2mesh()). Then, we solved the Laplacian equation ∇²v = 0, where v is the electric potential, at the nodes of the mesh by using the open-source solver getDP [103] with boundary conditions given by the current intensity at the anode and cathode. Finally, we assigned the EF to every voxel as , and we extracted the voxels that span the cerebellar regions by applying the segmented masks provided in [26]. All lobules were combined as the cerebellar cortex, and the white matter was labeled as the cerebellar nuclear region [104].

Cerebellar neuron models

We considered 3D multicompartment models of PC, GrC, and DCN because these cell types account for more than 80% of cerebellar neurons [105]. Although relevant in shaping the firing rate of Purkinje cells [51], cerebellar interneurons were omitted because they are significantly less numerous than granule cells [106], smaller than Purkinje cells [21], and with a limited dendritic span. Furthermore, interneurons have low firing rate at rest (e.g., ~5 Hz in vivo, [107]) and have shown modest responses to low-intensity EF [43]. Golgi cells were also omitted because they target only granule cells and have therefore no direct contact with Purkinje cells [108]. Finally, Golgi cells and interneurons are scarcely arranged within the molecular layer of the cerebellum [109].

The PC model was adopted from [34], consists of 1,611 compartments, and provides a 3D reconstruction of the morphology of a whole Purkinje cell obtained from a 2–3-month-old Guinea pig. Fifteen types of ionic channels were included in the PC model, including Na⁺ channels Nav1.6, potassium channels Kv1.1, 1.5, 3.3, 3.4, and 4.3, and calcium channels KCa1.1, 2.2, and 3.1. The corresponding ionic channel mechanisms were modeled from published references without modifications of their kinetics. See [34] for details on compartment dimensions, ionic channel models, distribution of channels across compartments, and tuning of free parameters.

The DCN model was adopted from [35], consists of 516 compartments, and provides a 3D reconstruction of the morphology of a deep cerebellar neuron obtained from a 13-to-19-day-old Sprague-Dawley rat. Nine types of ionic channels were included in the DCN model, including fast and persistent Na⁺ channels, K⁺ channels Kv2 and Kv3, purely Ca²⁺-gated K⁺ channels, and Ca²⁺ channels Cav3.1. Ionic channel kinetics were modeled after published analyses of DCN neuron conductance, and free parameters were estimated by fitting in vitro DCN voltage responses to current injection pulses via genetic algorithms. See [35] for details on compartment dimensions, ionic channel models, distribution of channels across compartments, and tuning of free parameters.

The GrC model consists of 65 compartments and was created for this study by combining the morphology of a whole human granule cell and the ionic channel modeling proposed in [36]. The morphology (NMO_32569) includes soma and dendritic arborization and was reconstructed from a neurologically normal, 54-year-old male who had died of acute myocardial infarction [110]. We processed the cell morphology using the CellBuilder tool in NEURON [33] to obtain the somatic and dendritic compartments, see S2 Table. We then extended the morphology by adding the hillock (5 compartments), and an axon perpendicular to the somato-dendritic plane, including the axonal initial segment (AIS, 30 identical compartments) and ascending axon (AA, 4 compartments). The dimensions of the hillock, AIS, and AA were chosen as in [36]. Nine types of ionic channels were assigned to dendrites, soma, hillock, and axon as in [36], and include fast, persistent, and resurgent Na⁺ channels, N-type Ca²⁺ channels, voltage-activated K⁺ channels, and Ca²⁺-gated K⁺ channels. Kinetics of the ionic channel models were adopted from [111,112], while the passive electric properties and the ionic channel maximum conductance values were optimized in NEURON (PRAXIS optimization method) to fit the I-f (i.e., injected current vs. spike frequency) curve reported in [36], see S3 Table. For all three models, the temperature was set as 37°C wherever applicable. Models were simulated when no EF was applied and for EF of intensity ranging from -1.5 V/m to +1.5 V/m (13 values).

DCN rebound firing

We added 450 GABAergic synapses to the DCN model (one synapse per dendritic compartment and 50 synapses on the soma [35]), and all synapses were simulated in NEURON as NetCon objects [33]. Briefly, every synapse responded to a presynaptic pulse with a postsynaptic current (1) where τ is the arrival time of the presynaptic pulse, V is the membrane potential at the postsynaptic dendritic compartment to which the synapse is attached, and is the activation function, with A chosen such that 0≤α(z)≤1 for all z. The maximum conductance g_syn = 1.89×10⁻³ μS and the time constants τ₁ = 0.18 ms and τ₂ = 3.61 ms are as in [113], and the reversal potential E_syn = -90 mV was as in [35] to further hyperpolarize the neuron, thus inducing strong rebound bursts afterwards.

To mimic the activation of a presynaptic Purkinje cell, all synapses on the DCN model were driven by a common train of pulses, and the arrival times of the pulses were irregularly spaced by drawing the inter-pulse intervals, T_syn, from an exponential function, i.e., T_syn~t_r+Exp(1/λ), with λ = 2 ms and t_r = 2 ms, where t_r accounts for the refractory period between spikes. The presynaptic train started at 300 ms, spanned 15 ms (i.e., the typical duration of a PC complex spike [24]), and was designed to simulate the arrangement of the CS spikelets [114].

The after-hyperpolarization (AHP) instantaneous firing rate (IFR) histogram was constructed as in [35] to quantify the rebound firing activity. The AHP IFR was obtained as convolution of the somatic spikes with a Gaussian kernel, whose S.D. is . The values ISI_before and ISI_after are the inter-spike interval (ISI) preceding and immediately following the spike, respectively. AHP IFR histograms were estimated when no EF is applied and under ±1.5 V/m EF. The difference between the AHP IFR histograms estimated under EF and at baseline (i.e., no EF applied) was measured throughout the AHP rebound phase, i.e., until the firing rate at the DCN soma returned to the pre-hyperpolarization level.

GrC relay fidelity to mossy fibers

We added one AMPA synapse and one NMDA synapse on the most distal compartment of the longest dendritic branch. Synapses were modeled as in (1) with g_syn = 2.88×10⁻³ μS (AMPA) and g_syn = 4.51×10⁻⁴ μS (NMDA) and remaining parameters τ₁ = 0.6 ms, τ₂ = 1.0 ms, and E_syn = 0 mV. The number, type, location, and conductance of the synapses were chosen as in [47] to mimic the adaptation of the GrC response to mossy fiber inputs.

Both synapses were driven by a regular train of pulses, and the frequency of the train, f_stim, was varied from 5 Hz to 140 Hz (9 values). For each value of f_stim, the GrC model was simulated for 2,300 ms, with the train starting 100 ms after the simulation onset. The last 2,000 ms of GrC activity were considered to estimate the fidelity relay ratio, r, i.e., r was the ratio between the number of spikes at the GrC soma and the number of pulses delivered in the 2,000-ms-window.

PC response to synaptic stimuli

We placed synapses across the dendritic tree of the PC model, and we measured the response of the PC soma to the selective activation of these synapses. The following three configurations of synaptic stimuli were implemented.

PC complex spiking and spontaneous bursting

To simulate the activation of climbing fibers, we adopted the configuration proposed in [18], i.e., we placed glutamatergic synapses on all dendritic compartments with a diameter d≥3.5 μm, including the dendritic trunk (105 compartments altogether), we modeled the postsynaptic current at each synapse as in (1), with g_syn = 1.59×10⁻³μS, E_syn = 0 mV, τ₁ = 0.3 ms, and τ₂ = 3.0 ms, and we drove all synapses with a common presynaptic pulse applied at time t = 500 ms, thus resulting in a complex spike at the soma followed by silence period. The silence period T_sp was defined as the interval between the largest spike in the complex spike and the first simple spike, i.e., a spike occurring as part of the regular self-sustained somatic spiking pattern, after the prolonged hyperpolarization.

Purkinje cells also exhibit spike adaptation and transient bursting in response to the activation of the granular layer both in vitro and in vivo [23]. To assess this bursting activity, we simulated the in vitro experiment conducted in [25], i.e., we injected a 2-nA-depolarizing step current into the soma of the PC model at time t = 200 ms to trigger a transient burst pattern. Bursting was then measured in terms of burst count, burst period T_b, and mean burst rate. For any burst k, k = 1, 2, 3, …, T_b was defined as the interval between the last spike of burst k and the last spike of the following burst, i.e., burst k+1. The “burst interval” associated with burst k, instead, was defined as the interval between the last spike of burst k and the first spike of the following burst, and the “spiking period” of the burst was the interval between the first spike and the last spike of the burst. In our analyses, a burst is a sequence of action potentials with less than 10 ms between any two consecutive action potentials.

Spatial distribution of tDCS-induced modulation of Purkinje cell firing

The effects of tDCS on PC firing (i.e., firing rate, silence period, and average burst period) were mapped onto the cerebellar cortex according to the following steps. The procedure was repeated for each subject (i.e., atlas) individually, and results were averaged across atlases.

Step 1. We isolated the cerebellar cortex by setting a threshold on the voxel intensity (threshold is 1,300 for atlas 1–2 and 1,100 for atlas 4–5). We processed voxels spanning the surface of the cerebellar cortex and up to 1.2 mm in depth, which is the region where Purkinje cells are located. For each voxel, we calculated the EF intensity and orientation in ROAST.

Step 2. For each voxel, we determined the orientation of the somato-axonal axis and somato-dendritic axis of the Purkinje cells in the voxel. The somato-axonal axis was assumed perpendicular to the voxel outer surface. For the somato-dendritic axis, instead, we observed that Purkinje cells have dendritic trees growing perpendicularly to the folds of the cerebellar cortex [117,118]. Hence, we determined the iso-surfaces of the image using the MATLAB function isosurface() (isovalue parameter set as 0.5) and we computed the correspondent normal vectors with the MATLAB function isonormals(), which provided the orientation of the PC dendritic trees. Normal vectors on the white matter were removed because they correspond to the cerebellar nuclei, which do not contain Purkinje cells [104].

Step 3. For each voxel and iso-surface of interest, we used the EF intensity in the voxel and the relative orientation of the EF with respect to somato-dendritic and somato-axonal axes along the iso-surface to estimate the EF projection along the PC somato-axonal axis. We then interpolated the sensitivity curves in Figs 5–7 at the intensity of the EF projection and determined the specific value of firing rate, silence period, and burst period expected for the Purkinje cells along the iso-surface in the assigned voxel.