# Single session cross-frequency bifocal tACS modulates visual motion network activity in young healthy population and stroke patients

Michele Bevilacqua, Sarah Feroldi, Fabienne Windel, Pauline Menoud, Roberto F Salamanca-Giron, Sarah B Zandvliet, Lisa Fleury, Friedhelm C Hummel, Estelle Raffin

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## ► To cite this version:

Michele Bevilacqua, Sarah Feroldi, Fabienne Windel, Pauline Menoud, Roberto F Salamanca-Giron, et al. Single session cross-frequency bifocal tACS modulates visual motion network activity in young healthy population and stroke patients. Brain Stimulation, 2024, 17 (3), pp.660-667. 10.1016/j.brs.2024.05.007. hal-04813985

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# Single session cross-frequency bifocal tACS modulates visual motion network activity in young healthy population and stroke patients

Michele Bevilacqua<sup>a,b,**,1</sup>, Sarah Feroldi<sup>a,c,1</sup>, Fabienne Windel<sup>a,b</sup>, Pauline Menoud<sup>a,b</sup>, Roberto F. Salamanca-Giron<sup>a,b</sup>, Sarah B. Zandvliet<sup>a,b</sup>, Lisa Fleury<sup>a,b</sup>, Friedhelm C. Hummel<sup>a,b,d</sup>, Estelle Raffin<sup>a,b,*</sup>

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## ARTICLE INFO

**Keywords:**

Cross-frequency interactions

Phase-amplitude coupling

Bifocal transcranial alternating current

stimulation

Motion direction discrimination

Visual stroke

## ABSTRACT

**Background:** Phase synchronization over long distances underlies inter-areal communication and importantly, modulates the flow of information processing to adjust to cognitive demands.

**Objective:** This study investigates the impact of single-session, cross-frequency (Alpha-Gamma) bifocal transcranial alternating current stimulation (cf-tACS) to the cortical visual motion network on inter-areal coupling between the primary visual cortex (V1) and the medio-temporal area (MT) and on motion direction discrimination.

**Methods:** Based on the well-established phase-amplitude coupling (PAC) mechanism driving information processing in the visual system, we designed a novel directionally tuned cf-tACS protocol. Directionality of information flow was inferred from the area receiving low-frequency tACS (e.g., V1) projecting onto the area receiving high-frequency tACS (e.g., MT), in this case, promoting bottom-up information flow (Forward-tACS). The control condition promoted the opposite top-down connection (from MT to V1, called Backward-tACS), both compared to a Sham-tACS condition. Task performance and EEG activity were recorded from 45 young healthy subjects. An additional cohort of 16 stroke patients with occipital lesions and impairing visual processing was measured to assess the influence of a V1 lesion on the modulation of V1-MT coupling.

**Results:** The results indicate that Forward cf-tACS successfully modulated bottom-up PAC (V1 α-phase-MT γ-amplitude) in both cohorts, while producing opposite effects on the reverse MT-to-V1 connection. Backward-tACS did not change V1-MT PAC in either direction in healthy participants but induced a slight decrease in bottom-up PAC in stroke patients. However, these changes in inter-areal coupling did not translate into cf-tACS-specific behavioural improvements.

**Conclusions:** Single session cf-tACS can alter inter-areal coupling in intact and lesioned brains but is probably not enough to induce longer-lasting behavioural effects in these cohorts. This might suggest that a longer daily visual training protocol paired with tACS is needed to unveil the relationship between externally applied oscillatory activity and behaviourally relevant brain processing.

## 1. Introduction

Neural oscillations have been shown to play an essential role in

orchestrating communication within and across brain regions. Phase synchronization over long distances underlies inter-areal communication and importantly, modulates the flow of information processing to

<footnote><sup>a</sup> Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland</footnote>

<footnote><sup>b</sup> Neuro-X Institute, Clinique Romande de Réadaptation, (EPFL Valais), Sion, Switzerland</footnote>

<footnote><sup>c</sup> School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy</footnote>

<footnote><sup>d</sup> Department of Clinical Neuroscience, University of Geneva Medical School, Geneva, Switzerland</footnote>

<footnote>* Corresponding author. Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL) - Campus Biotech, Room H4.3.132.084, Chemin des Mines 9, 1202, Geneva, Switzerland.</footnote>

<footnote>** Corresponding author. Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL) - Campus Biotech, Room H4.3.132.084, Chemin des Mines 9, 1202, Geneva, Switzerland.</footnote>

<footnote>E-mail addresses: michele.bevilacqua@epfl.ch (M. Bevilacqua), estelle.raffin@epfl.ch (E. Raffin).</footnote>

<footnote n="1">¹ Authors contributed equally.</footnote>
adjust to cognitive demands. In support of this, phase coherence between different brain areas has been linked to a variety of cognitive and behavioural functions (e.g., [1-5]). For example, the enhancement of alpha band occipitotemporal coherence improved performance in tasks that require visuo-tactile integration [6], the high-alpha band phase synchronization in frontal, parietal, and occipital cortices decreased reaction times in a stimulus discrimination task [7], and the theta coupling between frontal and parietal areas induced faster reaction times during a visual memory matching task [8].

Central to this concept is the interaction between distinct neural oscillations, with a particular focus on the dynamic coupling between the phase of low-frequency oscillations and the amplitude of high-frequency oscillations, known as phase-amplitude coupling (PAC) [9, 10]. This phenomenon is especially evident in the synchronization of alpha (8-13 Hz) and gamma (>40 Hz) oscillations, which has been consistently observed in both human and animal studies [11-14]. This coupling is crucial for facilitating effective neural processing and has important implications for understanding brain motor, sensory, and cognitive function and dysfunction [15,16]. In the visual system, these oscillatory interactions are fundamental for selecting relevant communicating neuronal networks [10,14,17,18]. Likewise, animal and human studies have shown that the primary visual cortex (V1) and the motion sensitive middle temporal visual area (MT) are co-activated in a complementary manner, with phase synchronization at lower frequencies, temporally driving high-frequency activities [19-23]. As a consequence, the external modulation of the inter-regional V1-MT phase-amplitude coupling might enhance visual motion discrimination abilities.

By emitting low-power sinusoidal currents at different frequencies over V1 and MT, bifocal tACS provides a non-invasive method to study and potentially modulate effective communication and cross-frequency communication flow along the cortical motion pathway [24-28]. Furthermore, by comparing the electrode/frequency combination, it is possible to assess the preferred directionality of rhythmic neural transmission between V1 and MT. To do so, we applied two versions of the bifocal cross-frequency transcranial alternating current stimulation (cf-tACS) protocol in a cohort of young healthy participants undertaking a motion coherence discrimination task. One cf-tACS condition consisted in promoting bottom-up direction of information flow by injecting synchronized V1-alpha signals onto MT-gamma signals (Forward-tACS). This assumes that low-frequency neural populations project into high frequency populations [29]. The other cf-tACS condition rather promoted top-down direction of information flow by injecting synchronized MT-alpha signals onto V1-gamma signals (Backward-tACS). These two experimental cf-tACS conditions were compared to Sham tACS in another sample of healthy participants. Furthermore, to assess the neurorehabilitation potential of this approach, we applied the two experimental cf-tACS conditions to visual stroke survivors, in whom the part of the visual field was missing due to a lesion in/affecting V1. Changes in motion discrimination performances in the lower-left quadrant of healthy volunteers and in the blind field of patients were compared across tACS conditions, as well as EEG-based V1-MT phase-amplitude coupling (PAC). We expected that both active tACS conditions would modulate phase-amplitude coupling in a direction-dependent manner, but that strengthening feedforward information flow from the residual V1 neurons to the higher-level MT area in patients, would more efficiently modulate inter-areal communication and improve motion discrimination in the blind visual field. This study can provide a proof of concept that endogenous frequencies can be re-orchestrated through directionally tuned bifocal cf-tACS over V1 and MT. Furthermore, it might shed light on the oscillatory dynamics underlying visual motion discrimination, highlight potential differences between intact and lesioned brain networks, and ultimately pave the way towards new interventional strategies to enhance motion discrimination abilities in healthy individuals or boost/promote motion discrimination recovery in patients.

## 2. Methods

### 2.1. Participants

A total of 45 young healthy participants took part in the study. Mean (SD) age was 24.6 (11.2) years, range (18-40); 55 % were female, all right-handed with normal or corrected to normal vision. None of them reported cognitive or neurological dysfunction. Additionally, sixteen stroke patients were enrolled at least one week after stroke-induced occipital damage (verified using structural MRIs), with reliable 24-2 Humphrey Visual Field (HVF) perimetry (<20 % fixation losses, false-positive and false-negative errors) in both eyes and ability to fixate precisely (error smaller than ±1° relative to fixation spot) during psychophysical testing. Mean (SD) age was 59.93 (11.2) years, range (34-74); 18.75 % were female, 81.25 % male. Mean time since stroke was 12.5 months (range: 1-60 months). Exclusion criteria were unreliable HVFs, neglect, neurologic disease unrelated to occipital stroke, use of neuroactive drugs, and any contra-indication to MRI or Non-Invasive Brain Stimulation. In all patients but one, an etiology of brain injury, as verified by cranial CT and/or MRI, was an infarction in the territory of the posterior cerebral artery causing a lesion to the occipital cortex; one patient had a carotid artery rupture. All patients were native French speakers except one German patient (see Table 1 for patients' characteristics and demographics).

Written informed consent was obtained from all participants and the study was approved by the local Swiss Ethics Committee (2017-01761) and performed within the guidelines of the Declaration of Helsinki.

#### 2.1.1. Study design

Healthy participants were randomly and evenly allocated to one of the experimental tACS conditions: Forward tACS (V1α-MTγ) (n = 15) or Backward tACS (V1γ-MTα) (n = 15). An additional group of healthy participants was recruited on whom Sham tACS (n = 15) was applied. While a parallel design was preferred in the healthy cohort to avoid carry-over effects in this population, we used a parallel design for patients to account for more challenging recruitment. Therefore, patients were randomly allocated to one of the two treatment groups. Group A (n = 8) first received Forward tACS followed by Backward tACS. Group B (n = 8) received Backward tACS first, followed by Forward tACS in a cross-over design. The two blocks were performed at least one month apart. Each block started with a familiarization phase, composed of 150 practice trials, followed by the actual experiment. During the familiarization phase, we ensured that the subject understood the visual discrimination task and reached stable performance. After EEG acquisition was prepared, a baseline block ("PRE"), which consisted of a task-

<table><caption>**Table 1**

Patients' characteristics and demographics.</caption><thead><tr><th>ID</th><th>AGE<br>(years)</th><th>SEX</th><th>LESSION</th><th>LESSION<br>SIDE</th><th>TIME SINCE<br>STROKE<br>(months)</th><th>MMSE</th></tr></thead><tbody><tr><td>P101</td><td>34</td><td>f</td><td>cort</td><td>left</td><td>12</td><td>27</td></tr><tr><td>P102</td><td>62</td><td>m</td><td>cort</td><td>right</td><td>12</td><td>28</td></tr><tr><td>P103</td><td>74</td><td>m</td><td>cort</td><td>left</td><td>60</td><td>25</td></tr><tr><td>P104</td><td>62</td><td>m</td><td>cort</td><td>right</td><td>12</td><td>27</td></tr><tr><td>P105</td><td>66</td><td>m</td><td>cort +<br>subcort</td><td>right</td><td>30</td><td>27</td></tr><tr><td>P106</td><td>68</td><td>m</td><td>cort</td><td>right</td><td>8</td><td>29</td></tr><tr><td>P107</td><td>53</td><td>m</td><td>cort</td><td>left</td><td>2</td><td>30</td></tr><tr><td>P201</td><td>69</td><td>m</td><td>cort +<br>subcort</td><td>left</td><td>28</td><td>25</td></tr><tr><td>P202</td><td>40</td><td>f</td><td>cort</td><td>left</td><td>11</td><td>28</td></tr><tr><td>P203</td><td>59</td><td>m</td><td>cort</td><td>right</td><td>4</td><td>28</td></tr><tr><td>P204</td><td>63</td><td>m</td><td>cort</td><td>right</td><td>4</td><td>29</td></tr><tr><td>P205</td><td>51</td><td>m</td><td>cort</td><td>left</td><td>3</td><td>30</td></tr><tr><td>P206</td><td>56</td><td>m</td><td>cort</td><td>right</td><td>11</td><td>30</td></tr><tr><td>A201</td><td>55</td><td>m</td><td>cort</td><td>right</td><td>1</td><td>29</td></tr><tr><td>A203</td><td>66</td><td>m</td><td>cort</td><td>left</td><td>1.5</td><td>29</td></tr></tbody></table>
related EEG recording without tACS was recorded. After a few minutes of rest, electrodes were placed over the occipital and temporal cortex, and electrical stimulation was started. The stimulation remained on for the entire duration of the task. Thereafter, the stimulation electrodes were removed and after 10 min of rest, a post stimulation block (“POST”) was measured using the same task and EEG recordings (Fig. 1A). This study belongs to a larger registered trial (ClinicalTrials.gov: NCT05220449) investigating longer term effects of repeated cf-tACS. The full evaluation of the clinical effects will be reported elsewhere (Raffin et al. in preparation).

The study was approved by the local Swiss Ethics Committee (2017-01761) and performed within the guidelines of the Declaration of Helsinki.

## 2.2. tACS intervention

**Electrode design and electrical stimulation devices:** Two customized center-surround electrodes (outer and inner diameters: 5 and 1.5 cm respectively) were connected to two Neuroconn DC Plus stimulators (Neurocare, Germany). The two stimulators were synchronized so that the “alpha” stimulator would trigger the “gamma” stimulator at each individual new peak of alpha cycle, ensuring constant phase lag between the two signals. Note that the “gamma” stimulator was constantly on during the full alpha cycle, ensuring similar “dose” of injected current at the two sites.

**Electrode placement:** The International 10/20 system was used for the location of V1 (O2 or O1 for the left or right hemisphere respectively) and MT (PO8 or PO7 for the right and left hemisphere respectively) on the lesioned hemisphere. In healthy participants, electrodes were always placed on the right side (O2 for V1 and PO8 for MT) (Fig. 1B).

**tACS intensity:** A constant current intensity of 3 mA was applied in both stimulators creating a current density of 0.18 mA/cm².

**Frequencies:** A priori defined individual α [8–12 Hz] and γ [30–45 Hz] peak frequencies were used to apply tACS. The peak frequencies were extracted from a 5 min-resting state EEG prior to the intervention.

**Stimulation duration:** Each tACS session lasted approximately 15 min for healthy participants and 25 min for patients depending on the individual time to complete the training session, including 150 trials for healthy and 255 trials for patients. The inter-individual variability in the stimulation duration is explained by the fact that participants were told to be as accurate as possible without having any time pressure to complete the task.

The Sham stimulation corresponded to a ramp up and ramp down

stimulation lasting the duration of an individualized Alpha cycle. Data from the Sham group have been published elsewhere but were acquired concurrently to the present data and under the same conditions [26].

## 2.3. Direction discrimination and integration task

While undergoing tACS, participants engaged in a well-established task designed for coarse direction discrimination and integration (CDDI) [26,30,31], where they observed a random dot stimulus. This stimulus, lasting for 500 ms, was displayed within a circular aperture with a 5° diameter on a computer screen (resolution: 1024 × 768 Hz, frame rate: 144 Hz). The visually presented black dots were moving against a mid-grey backdrop. These dots were presented for 500 ms, travelled at a speed of 5° per second, and were spread at a density of 3.5 dots per square degree. The overall motion of the dots was uniformly distributed around either leftward or rightward vectors, creating a range of motion directions. Participants were required to identify and report the predominant direction of the dots' movement, either leftward or rightward. To modulate the challenge of the task, a 3:1 staircase method was employed, which varied the range of dot directions from 0° to 360° in increments of 40°.

For the stroke patients, the visual stimulus location was individually defined as followed: after Humphrey perimetry, each subject would undergo an extensive psychophysical mapping of the blind field border as previously described [30,31]. The final stimulus location was chosen as the location where performances on the motion direction discrimination task declined to chance level (50–60 % correct).

All experiments took place inside the same, shielded Faraday cage designed for EEG recordings and under the same light conditions (Fig. 1C). Participants' heads were placed over a chin-rest at a distance of 60 cm from the presentation screen, assuring a fixed position across all trials. The task ran on a Windows OS machine, based on a custom Matlab (The MathWorks Inc., USA) script, using the Psychophysics Toolbox. Gaze and pupil movements were controlled in real time with an EyeLink 1000 Plus Eye Tracking System (SR Research Ltd., Canada) sampling at a frequency of 1000 Hz. The task required the subject to fixate a target at the center of the screen for every trial, with a maximal tolerance for eye deviation from this fixation target of about 1°. If the participant broke fixation during stimulus presentation, the moving stimulus froze and then disappeared; the trial was discarded, and an auditory tone (at 400 Hz) was presented. Once the participant positioned their gaze correctly, a novel trial was started.

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** A. Study design consisting of 5 blocks, with approximate duration of each block and waiting time between tACS and POST session. B. Experimental setup showing the EEG headset and the montage for performing the visual task C. Scheme representing the location of the concentric tACS electrodes over PO8 and O2 and an indication of the way the motion takes places in the task.</figcaption></figure>
## 2.4. Direction range threshold

The performance at the task was fitted using a Weibull psychometric function on single trial data with a threshold criterion of 72 % correct to calculate direction range thresholds (where percent correct = 1-(1-chance) * exp(-(k*x/threshold) slope), and k = (-196 log((1-0.72)/(1-chance))) (1/slope).

These direction range thresholds were then normalized to the maximum range of directions in which dots could move (360°) and expressed as a percentage using the following formula: Normalized Direction Range (NDR) threshold (%) = [360° - direction range threshold]/360° × 100. For ease of analysis, when participants performed at chance (50-60 % correct for a given session), the CDDI threshold was set to 100 %. Thus, a lower score stands for better performance.

## 2.5. EEG recording and analyses

Resting-state EEG and task-EEG activity (during the CDDI task) was recorded using a 64-channel active system (BrainAmp DC amplifiers and BrainCap EEG cap, Brain Products GmbH, Germany) for stroke patients and a passive system with 64 electrodes (Brain Products GMBH, Germany) for healthy participants.

The EEG cap set up was done following the 10-20 standard system. Electrode impedances were adjusted and kept under 10 kOhms using conduction gel. The impedance levels were checked throughout the experiment and corrected if needed during breaks between conditions. The signal was recorded using DC mode, filtered at 500 Hz anti-aliasing low-pass filter, and digitalized at 5 kHz sampling frequency. During the experiment, the ground electrode was Fpz, and the reference electrode was Cz.

All preprocessing of EEG data was performed on periods without tACS (at PRE and 10 MIN POST) using MNE-Python [32], MATLAB, with the EEGLAB toolbox [33] and customized MATLAB scripts.

For the preprocessing of the healthy participants, EEG-data were referenced to the average of signals, filtered through a Finite Response Filter of order 1, between 0.5 and 45 Hz, epoched in 3 s blocks, corresponding to -1.5 s before and +1.5 s after the stimulus onset. Every epoch corresponded to the time interval of a trial from the behavioral task. They were visually inspected to clear up noisy channels or unreadable trials. Bad channels were interpolated, and data was re-sampled to 250 Hz. Independent component analysis was used to remove physiological artifacts (i.e., eyeblinks, muscle torches).

The preprocessing of stroke patients' EEG datasets went through an equivalent pipeline: data were referenced to the average of all channels, band-passed filtered between 1 Hz and 80 Hz, notch filtered between 48 and 52 Hz, and divided in epochs of 1.5 s length [-0.5 1 s] around the trial stimulus onset. Visual inspection was used to remove explicit artifacts among channels and trials, followed by the reconstruction of dropped channels and epochs. Ultimately, an Independent Component Analysis was applied to down-sample data (1000 Hz) to remove electrophysiological interferences, such as eyeblinks or muscle artifacts. For both healthy participants and stroke patients, Brainstorm software [34] together with OpenMEEG BEM plugins were used to perform source level reconstruction of EEG data. For stroke patients, the individual cortex and head mesh (15,000 and 10,000 vertices respectively) were generated based on previously acquired T1-weighted images, using the automated MRI segmentation routine of FreeSurfer [35]. For healthy participants, instead, a default head model (ICBM152) with cortex and head meshes (15,000 and 10,000 vertices respectively) was used.

For both populations, the forward model was then computed using the symmetric Boundary Element Method developed in the open OpenMEEG freeware, using default values for conductivity and layer thickness [36]. The covariance noise matrix was computed from the concatenated epoch baselines, e. g. the recorded activity before the

onset of each trial [-0.5 -0.005 s]. Source level activation was computed using a minimum norm imaging linear method with sLORETA as the inverse model. The dipole orientation of the source model was defined as unconstrained to the cortex surface. Source orientation was kept orthogonal to the cortical surface and source amplitude was estimated using the default values of the Brainstorm implementation of the whitened and depth-weighted linear L2-minimum norm solution. All metrics involving a frequency domain decomposition were calculated through Morlet wavelets between 2 and 60 Hz. The source points belonging to specific areas of interest (i.e. V1, MT), were defined manually and ipsilesionally for each stroke patient according to the fMRI localizer recordings performed before the EEG acquisitions (for V1 were 16 ± 9, -86 ± 8, -5 ± 22 and for MT were 66 ± 9, -55 ± 9, -7 ± 17 -coordinates x, y z, MNI space), covering about 200 vertices of cortical mesh for V1 and 200 for MT. For healthy subjects, default ROIs were manually commonly defined on the right hemisphere according to the ICBM 152 brain atlas from Brainstorm [37] (V1: 14.7, -103, -2; MT: 49.3, -74.5, 14.9 - x, y z coordinates in MNI space), covering the same number of vertices as for the patients.

In more details, Phase Amplitude coupling (PAC) [38] was obtained through:

$$ PAC = n - 1n \sum t = 1 at (f) \cdot ei\theta t \lor $$

Where a corresponds to the amplitude of the instant t among n intervals multiplied by the imaginary component of the phase angle θ. Cross-frequency interactions were computed from the EEG data recorded before and after tACS in phase-amplitude coupling of α-γ between V1 and MT. From a signal processing point of view, directionality arises from the fact that the low frequency Alpha wave that holds the information, must travel and be imposed over the amplitude of the local, high frequency Gamma that turns into the carrier wave [39].

## 2.6. Statistical analyses

To compare the EEG metrics (Phase-Amplitude Coupling and Time-Frequency values) before and after the interventions, we used cluster-based time-wise (in the case of PAC, referring to the maximal PAC for each specific phase and frequency value considered) and time and frequency-wise (in the case of Time-Frequency) non-parametric permutation tests [40].

To evaluate differences in threshold performance at the motion direction discrimination task between interventions and across timepoints ("Pre" - before stimulation, "tACS" - during stimulation, and "Post" - 10 min after the intervention), we designed a repeated measure ANOVA for patients with Timepoints and tACS condition as within-subject factors. In healthy participants we designed a mixed ANOVA with the factor Timepoints as a within-subject factor and tACS condition as a between-subject factor. A probability of type I error of P < 0.05 was considered statistically significant. Correlational analyses were performed between the changes in PAC averaged over the first and second half of motion processing and the changes in NDR were conducted. Cook's distance was used to detect any influential points and p-values were adjusted for multiple comparisons using Bonferroni correction.

# 3. Results

## 3.1. Changes in PAC in young healthy participants

We compared the maximal PAC values in the time domain during motion processing before and after Forward-tACS and Backward-tACS. Fig. 2 shows the maximal PAC values Pre and Post tACS for the three tACS conditions for both PAC directions (upper panels) and the associated comodulograms of the difference Post-Pre (lower panels). The Pre/Post comparison of the bottom-up V1 α-phase-MT γ-amp. PAC showed an early increase between 100 and 150 ms after stimulus onset for Forward-
tACS (Fig. 2, left panel). The reversed direction, MT αᵢₚhase-V1 γ-amp. PAC showed two late time windows of decrease around 300 ms and 400 ms post stimulus onset. None of the two PAC was significant for Backward-tACS nor for Sham tACS (Fig. 2, middle and right panels). To explore further possible electrophysiological changes induced by the interventions, we also compared the V1 and MT source-based time-frequency representations and the V1-to-MT and MT-to-V1 Granger causality before and after the interventions (see supplementary materials for the methodological details). The results showed stronger alpha desynchronization slightly before the onset of the visual stimuli in V1 in the Forward-tACS and Sham-tACS group. We observed the same phenomenon in MT, slightly prolonged during stimulus presentation for the Backward-tACS condition. No other differences were noted (Fig. S1A). As a control analyses, we also computed spectrally-resolved Granger Causality (GC) between V1 and MT [41], which is a measure of directed connectivity in the frequency domain. Most of the changes again occurred in the Forward-tACS intervention with a significant increase in V1-to-MT inputs in the alpha/beta band and a significant increase in MT-to-V1 in the beta and low gamma range. Backward-tACS only resulted in a significant increase in top-down MT-to-V1 inputs in the gamma band while Sham-tACS resulted in a slight but significant decrease in bottom-up V1-to-MT connectivity in beta (Fig. S1B).

## 3.2. Changes in PAC in stroke patients

Similar to the healthy cohort, this analysis revealed an early increase in V1 α-phase-MT γ-amp. PAC around 100 ms post stimulus onset for Forward-tACS. This was followed by a significant decrease around 250 ms and 350 ms post stimulus onset. The reversed direction, MT α-phase-V1 γ-amp. PAC, showed in this case a very early increase of coupling strength (40–70 ms post stimulus onset) (Fig. 3, left panel). Backward-tACS condition induced a decreased V1 α-phase-MT γ-amp. PAC around 100 ms after the stimulus onset. The reversed PAC direction showed no changes in this condition (Fig. 3, right panel). Similar to the healthy

dataset, we mainly report stronger alpha desynchronization on the source-based time-frequency analyses although slightly delayed, with a short period of alpha synchronization in the Backward-tACS condition (Fig. S2A). Granger causality analyses also revealed significant increases in V1-to-MT inputs in the high-alpha/low-beta range after Forward-tACS. No other significant differences were found (Fig. S2B).

## 3.3. Changes in motion discrimination

Fig. 4 shows the mean and individual baseline corrected NDR values across timepoints for the healthy participants (upper panels) and the stroke patients (lower panels). A significant task improvement was found irrespective to the stimulation condition in the healthy cohort, as shown by a significant main effect of Time ($F(2,24) = 10.958, p < 0.001$) on the mixed model ANOVA, acknowledging the effect of task exposure. However, there was no tACS condition effect ($F(2,24) = 2.694, p = 0.088$) nor Time by tACS condition interaction ($F(4,48) = 1.314, p = 0.278$). Baseline values were not different in the three healthy groups (V1α-MTγ vs V1γ-MTα: t = 1.475, p = 0.160, p cor = 1.0, V1α-MTγ vs Sham: t = 1.225, p = 0.232, p cor = 1.0, V1γ-MTα vs Sham: t = -0.057, p = 0.954, p cor = 1.0). For the stroke cohort, the repeated measures ANOVA showed no main effect of Time ($F(2,30) = 1.11, p = 0.34$), nor tACS condition effect ($F(1,15) = 2.9e-4, p = 0.98$) or Time by tACS condition interaction ($F(2,30) = 0.415, p = 0.66$). Again, baseline performances did not differ significantly ($t(15) = 1.67, p = 0.12$). No association between behavioral and electrophysiological changes was found in the two cohorts for any of the tACS condition.

## 4. Discussion

The current study examined the effects of bifocal cross frequency (i.e., V1α-MTγ vs. V1γ-MTα) single session tACS applied to two different cohorts, young healthy (compared to a Sham-tACS condition) and ocipital stroke patients, undertaking a visual motion discrimination and

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Maximal Phase-Amplitude Coupling (PAC) between V1α-phase-MTγ-amplitude and the opposite V1γ-amplitude-MTα-phase for the three tACS group (upper panels) and the associated comodulograms (bottom panels). Significant differences in PAC are indicated with grey rectangles.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 3. Maximal Phase-Amplitude Coupling (PAC) between V1α_phase-MTγ_amplitude and the opposite V1γ_amplitude-MTα_phase for the two tACS group (upper panels) and the associated comodulograms (bottom panels). Significant differences are indicated with grey rectangles.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 4. Averaged evolution of the baseline-corrected NDR across all time points for the young healthy cohort (upper left panel) and for the stroke cohort (upper right panel). Individual data for the different tACS condition and time points are shown below.</figcaption></figure>

integration task. Our primary objective focused on the possibility to alter
inter-areal cross-frequency coupling between V1 and MT, 10 min after
the stimulation period, in the two different cohorts. The oscillatory ac-
tivity along the V1-MT pathway at the source level was analyzed
through phase-amplitude coupling (PAC). A second objective was to
gather information about potential similarities or differences in oscil-
latory re-orchestration in healthy, optimally functioning visual networks
versus networks disrupted by a lesion affecting V1. Finally, we aimed to
determine if any of these exogenous non-invasive modulations of neural
oscillatory activity could affect behavioral performance at a motion
coherence discrimination visual task.

The main results were threefold: 1) a single session of bifocal cross-frequency Forward tACS (V1α-MTγ) proved to have the potential to modulate inter-areal synchronization both in healthy and in stroke

patients; 2) Forward tACS seemed to increase bottom-up PAC (i.e., V1 α_phase-MT γ_amp.) in early latencies in both patients and healthy participants. This did not sustain in the patient cohort, which showed a significant decrease in the late time window of motion processing. In parallel, healthy participants showed an associated decrease in the opposite direction (top-down MT-to-V1 pathway) while patients show a non-specific increase in the top-down connectivity; 3) the induced electrophysiological changes did not translate into a significant group-level behavioural difference in any of the cohorts.

The early increase in bottom-up V1 α_phase-MT γ_amp. coupling induced by Forward-tACS is occurring about 100 ms post stimulus appearance. This timing corresponds to the first feedforward inputs reaching V1, suggesting that in both cohorts, the early feedforward synchronization to MT got promoted by the intervention. This was
further confirmed using a different computational approach based on Granger Causality (see supplementary results). The same communication channel became significantly downregulated in patients in the second half of motion processing. This can be due to several reasons, including the incapacity of the residual V1 neurons to sustain such enhanced processing. It can also be explained by an over-exaggerated homeostatic response of the same residual V1 neurons, probably functioning sub-optimally, aiming to prevent the lesioned system to saturate [42].

In addition, the opposite direction of PAC, MT α_phase-V1 γ_amp, was associated with a decrease in the late latencies for the healthy cohort. This finding could reflect an optimal visual processing from an intact visual system, where late phasic decoupling through the feedback pathway allows other functional feedback pathways to synchronize. Patients instead, showed a non-direction specific increase. This opposite effect could represent a functional reorganization state of the peripheral areas, with spared higher order visual areas contributing more to information transfer through the feedback pathway. Of note, Backward-tACS and Sham tACS did not result in any change in PAC in healthy participants. In contrast, Backward-tACS led to a brief decrease in bottom-up V1 α_phase-MT γ_amp coupling around 100 ms post stimulus in patients.

Despite slight differences between the two groups, these results overall suggest that bifocal cf-tACS applied to the cortical motion discrimination pathway might improve information gating by optimizing inter-areal alpha-gamma interactions. Mechanistically, such cross-frequency interactions are thought to reflect synchronized alpha oscillations between remote neurons belonging to the same network. This synchronization is accompanied by a reduction in alpha power, which leads to extended periods of neuronal excitability within each cycle, increasing the duty-cycles [43]. This corresponds to the time window in which gamma activity of the nodes can synchronize to increase information exchange [44]. Specifically, gamma oscillations will be integrated within these alpha oscillations, meaning they will primarily occur during the alpha oscillations' excitability phase, the cycle's through. Since the excitability phases of the alpha oscillations are synchronized, gamma activity originating in one node can significantly influence neuronal activity in remote nodes. The rapid activity coupling is expected to profoundly affect the activity of the receiver region due to the synaptic accumulation within the gamma cycle timeframe [45].

Accordingly, the magnitude of the interplay between the alpha oscillation phase and the intensity of gamma oscillations, measured in our study through inter-areal alpha-gamma PAC, could represent the temporal synchronization of information transfer across V1 and MT and, consequently, reflect the network potential to enhance visual motion discrimination ability. However, our findings showed that the causal manipulation of phase synchrony between V1 and MT did not translate into significant behavioral group-level differences between tACS conditions. Healthy participants showed a significant improvement with task repetition regardless of the tACS condition. In contrast, there was no significant effect of Timepoints nor tACS condition in the patient group. In patients, the absence of overall improvement after only a few task repetitions has been reported and is probably due to stroke-related or age-related impaired visual processing [31,46]. Furthermore, visual re-learning studies suggest that patients might require several dozens of practice sessions before showing some improvements in motion direction discrimination in their blind field [31,47]. A protocol involving multiple sessions of cf-tACS paired with motion discrimination visual training over weeks or months might be needed in order to observe relevant improvements at the behavioural level. Of note, none of the electrophysiological changes was significantly corelated with individual task performance. However, this might be due to the relatively low sample sizes in the present study.

Finally, the absence of differences between tACS conditions in the two cohorts might point towards a more complex relationship between externally applied oscillatory activity and its interaction with

behaviourally relevant brain processing. One may argue that the continuous application of cross-frequency tACS (i.e. during the entire visual task) may prevent the optimal synaptic asymmetries between V1 and MT to happen [48,49]. Consequently, a future development of the present approach could entail the adaptation of the tACS dosage and timing either to the online state of the brain, towards individualized closed loop applications [50] or to precisely time lock the bursts of cross-frequency tACS to the visual stimulus [51]. Further experiments systematically varying the timing and dose of cross-frequency tACS, including closed-loop applications, might be required to adjust the externally provided oscillatory activity to the ongoing, endogenous oscillatory activity and effectively modulate motion discrimination.

## 5. Conclusion

This study showed that a single session of bifocal cross-frequency tACS can modulate inter-areal synchronization in both healthy individuals and stroke patients. Specifically, Forward tACS (V1α-MTγ) induced changes in early bottom-up coupling in both cohorts. Despite these electrophysiological changes, no significant behavioral improvements were noted in the motion discrimination task. This result might point towards two possible explanations: 1) The need for longer-term interventions in healthy participants and particularly in patients; 2) The complex interaction between externally applied oscillatory activity and behaviourally relevant brain processing, which warrants future research to focus on adjusting tACS dosage and timing to align with the brain's ongoing oscillatory activity. This optimization could be achieved through state-dependent or closed-loop applications, effectively enhancing motion discrimination abilities.

## Funding

Funding was obtained from the Defitech Foundation (to FCH), Berparelli Foundation (Catalyst BC7707 to FCH & ER), by the Swiss National Science Foundation (PRIMA PR00P3_179867 to ER).

## CRediT authorship contribution statement

**Michele Bevilacqua:** Writing – original draft, Formal analysis, Data curation. **Sarah Feroldi:** Writing – original draft, Formal analysis. **Fabienne Windel:** Writing – review & editing, Data curation. **Pauline Menoud:** Writing – review & editing, Data curation. **Roberto F. Salamanca-Giron:** Formal analysis, Data curation. **Sarah B. Zandvliet:** Writing – review & editing, Data curation. **Lisa Fleury:** Writing – review & editing, Data curation. **Friedhelm C. Hummel:** Writing – review & editing, Supervision, Conceptualization. **Estelle Raffin:** Writing – review & editing, Supervision, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We would like to thank the EEG and neuromodulation facilities of the Human Neuroscience Platform of the Foundation Campus Biotech Geneva, for technical advice.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.brs.2024.05.007.
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