Spatially bivariate EEG-neurofeedback can manipulate interhemispheric inhibition

Projection neurons wire the brain over long distances and provide a network between different brain regions. In particular, both hemispheres are structurally connected by transcallosal projections and exhibit functional cross talks (Hofer and Frahm, 2006; Meyer et al., 1995); such interhemispheric interaction is essential for higher order cognitive and sensorimotor brain functions. An early argument for interhemispheric interaction was that dynamic interplay via the callosum not only allows for simple coordination of processing between the hemispheres but also has profound effects on attentional functioning (Banich, 1998). In the motor domain, neurons in the monkey primary motor cortex are firing bilaterally and motor signals from the two hemispheres interact during unimanual motor tasks (Ames and Churchland, 2019); in the human motor cortex, measuring these neural activities via electroencephalography (EEG) and paired-pulse transcranial magnetic stimulation (TMS) recently revealed that the two hemispheres act together and the related cortical oscillatory activity influences the inhibitory interhemispheric brain network (Picazio et al., 2014; Stefanou et al., 2018).

A critical issue is whether a specific interhemispheric activity can be volitionally controlled. Previous studies have focused on inhibitory interhemispheric sensorimotor network, evaluated by interhemispheric inhibition (IHI), from movement-related manner (Duque et al., 2007; Duque et al., 2005; Liang et al., 2014; Morishita et al., 2012; Murase et al., 2004; Nelson et al., 2009), and passive neuromodulation effects by non-invasive brain stimulation techniques such as transcranial direct current stimulation (tDCS) or repetitive TMS (rTMS) (Boddington and Reynolds, 2017; Gilio et al., 2003; Williams et al., 2010). Due to the experimental limitations related to these observational or open-loop paradigms, much less is known about the effects of changes in sensorimotor activity patterns in both hemispheres on IHI, and the possibility of manipulating IHI. Therefore, the closed-loop paradigms to manipulate bilateral sensorimotor activity patterns and IHI should be of great value for the understanding of human sensorimotor brain function (Figure 1A).

Figure 1

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As a recent neural manipulative tool, numerous studies have used a brain-computer interface (BCI)-based neurofeedback whereby participants learn to volitionally desynchronize and synchronize oscillatory sensorimotor rhythms (SMR-ERD/ERS) in the contralateral sensorimotor cortex (SM1) through visual and/or somatosensory feedback (Ang and Guan, 2017; Chaudhary et al., 2016; Ramos-Murguialday et al., 2013; Soekadar et al., 2015b). BCI-based neurofeedback is supported by the fact that the intensity of SMR-ERD in EEG represents states of high versus low excitability of not only the SM1 (Neuper et al., 2006; Neuper and Pfurtscheller, 2001; Pfurtscheller et al., 2006), but also the corticospinal descending pathway, as measured by the motor-evoked potential (MEP) amplitude (Takemi et al., 2015; Takemi et al., 2013). Because both hemispheres are structurally and functionally connected, it is likely that the balance of bilateral SMR-ERDs and transcallosal excitability states are linked, as indicated by common variation of conditioning MEP amplitude and IHI (Ferbert et al., 1992; Ghosh et al., 2013; Ni et al., 2009).

Here, we sought to investigate the association of IHI and the balance of SMR-ERDs to understand the inhibitory sensorimotor functions of interhemispheric interaction that may critically depend on the oscillatory brain activity in both hemispheres. Furthermore, if changes in the oscillatory brain activity influenced IHI, we tested whether it can be manipulated using a dual-coil paired-pulse TMS protocol (Daskalakis et al., 2002; Ferbert et al., 1992). To assess the association of IHI with ongoing oscillatory brain activity, we used a recently developed spatially bivariate BCI-based neurofeedback technique that allows volitional modulation of SMR amplitude in both hemispheres (Hayashi et al., 2021; Hayashi et al., 2020) and triggers the TMS pulses in real time at predetermined bilateral SMR amplitudes. Our system, therefore, overcomes previously unresolved experimental limitations of the ordinary observational or open-loop experimental paradigm. That is, our paradigm enables participants to control the bivariate sensorimotor excitability, and determines whether it is possible to manipulate the IHI magnitude.

Using the novel closed-loop bivariate (bi-hemispheric brain state-dependent) EEG-triggered dual-coil paired-pulse TMS system (bi-EEG-triggered dual-TMS), we evaluated the effects of different states of the targeted bidirectional up- or down-regulated one-sided hemisphere on effective inhibitory interhemispheric network expressed by IHI: (1) resting-state (REST), (2) during right finger motor imagery (MI) without visual feedback (NoFB), (3) high (HIGH), (4) middle (MID), and (5) low (LOW) excitability states of the ipsilateral SM1 (conditioning side) to the unilateral imagined hand movement while maintaining constant contralateral excitability during BCI-based neurofeedback (Figure 1B). Inspired by the findings that SMR-ERD was associated with corticospinal excitability amplitude (Takemi et al., 2015; Takemi et al., 2013), we hypothesized that the ipsilateral SMR-ERD to the imagined (right) hand is a potent up- or down-regulator of IHI from the ipsilateral (right) to the contralateral (left) hemisphere. This multimodal research provides strong evidence for the dynamic interplay between distinct regions underlying IHI through BCI-based neurofeedback, and may therefore form the basis for interhemispheric sensorimotor activity.

Values corresponding to each statistical figure are presented in each table (Supplementary file 1) for readability.

Modulation effects of bilateral SM1 at EEG level

We tested whether participants could learn volitional up- or down-regulation of the ipsilateral sensorimotor excitability to the imagined right hand while maintaining constant contralateral sensorimotor excitability at the EEG level using a spatially bivariate BCI-based neurofeedback. For the contralateral SMR-ERD, a one-way rmANOVA for the sessions (five levels: REST, NoFB, HIGH, MID, and LOW) revealed a significant difference (F_(4,102) = 4.81, p=0.014, and η²=0.16). Post-hoc two-tailed paired t-tests demonstrated no significant differences in the contralateral SMR-ERD between the neurofeedback sessions (HIGH-MID: Cohen’s d=0.13, p=1.00; HIGH-LOW: Cohen’s d=0.22, p=1.00; MID-LOW: Cohen’s d=0.11, p=1.00), whereas significant differences were found between REST and other sessions (all p<0.05; Figure 2A). In contrast, after showing significant differences in the ipsilateral SMR-ERD between sessions (F_(4,102) = 156.0, p<0.001, and η²=0.86), post-hoc two-tailed paired t-tests showed that ipsilateral SMR-ERD increased during the HIGH session (HIGH-MID: Cohen’s d=3.12, p<0.001; HIGH-LOW: Cohen’s d=6.13, p<0.001) and decreased during the LOW session (MID-LOW: Cohen’s d=4.44, p<0.001), revealing a significant bidirectional modulation compared to baseline sensorimotor endogenous activity (Figure 2B). The spatial patterns of SMR-ERD in each session are depicted in Figure 2C. We found that the SMR-ERDs were predominantly localized in bilateral parieto-temporal regions (C3 and C4 channels and their periphery), and strong ipsilateral SMR-ERD was observed in the HIGH session with constant contralateral SMR-ERD. Modulation effects of the contralateral and ipsilateral excitabilities (power attenuation) in the theta (4–7 Hz), low beta (14–20 Hz), high beta (21–30 Hz), and gamma (31–50 Hz) bands were shown in Figure 2—figure supplement 1 (see also Supplementary file 1–Table 2).

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IHI curves at rest

To validate IHI measurement under bi-EEG-triggered dual-TMS setup, IHI curves were obtained at rest (Figure 3A) in 20 out of 24 participants, with conditioning stimulus (CS) of varying intensity (five different intensities, 100–140% of resting motor threshold [RMT], in steps of 10% RMT). A one-way rmANOVA for intensities (six levels: 0 [single test stimulus: TS-only], 100, 110, 120, 130, and 140% RMT) revealed significant difference in intensity (F_(5,109) = 8.31, p<0.001, η²=0.28). Post-hoc two-tailed paired t-tests showed significant difference between TS-only and 100–140% of RMT (TS-only versus 100% RMT: p=0.039, TS-only versus 110–140% RMT: all p<0.001). There were no significant differences across CS intensities, while the size of MEP amplitude tended to be smaller for larger CS intensities. The IHI was approximately half of the maximum when the CS intensity was approximately 130% of RMT, which was compatible with a previous EEG-TMS experiment (Stefanou et al., 2018; Tsutsumi et al., 2012). In addition, the stimulus intensities of the left and right M1 to evoke MEP of 1 mV peak-to-peak amplitude from the relaxed right and left first dorsal interosseous (FDI) muscles (SI_1mV) were determined and used for the following dual-coil paired-pulse TMS setup. The average SI_1mV of TS and CS were 124 ± 11% RMT and 132 ± 13% RMT, respectively. We also confirmed that the SI_1mV of CS showed approximately 50% of the mean conditioned MEP over the mean unconditioned test MEP (Figure 3B).

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IHI manipulation via spatially bivariate BCI-based neurofeedback

Typical examples of MEP amplitude elicited by single TS (TS-only) and paired-pulse stimulation (CS + TS) of a representative participant are shown in Figure 4A. The manipulation range of IHI (i.e. the difference between HIGH and LOW sessions) was 32.6 ± 30.7% (Cohen’s d=1.50) in all participants excluding 2 participants with data corruption (i.e. 22 participants). A one-way rmANOVA of the sessions (five levels: REST, NoFB, HIGH, MID, and LOW) revealed significant differences in IHI magnitude (F_(4,101) = 6.85, p<0.001, η²=0.22). Across the three BCI-based neurofeedback sessions (i.e. HIGH, MID, and LOW sessions) for the comparison of IHI magnitude, post-hoc two-tailed paired t-tests showed significant difference between HIGH and MID sessions (Cohen’s d=0.94, p=0.025), and between HIGH and LOW sessions (Cohen’s d=1.36, p<0.001), but not between MID and LOW sessions (Cohen’s d=0.42, p=0.424; Figure 4B). In addition, we found disinhibition in NoFB session compared to REST session, although there was no statistical difference (Cohen’s d=0.42, p=0.279); this trend is consistent with the previous studies, which reported that in healthy participants, IHI targeting the moving index finger leads to disinhibition before the movement onset to produce voluntary movement (Duque et al., 2005; Murase et al., 2004) and during isometric contraction (Nelson et al., 2009). Importantly, rmANOVA for the MEP amplitudes induced by TS-only revealed no significant differences between all sessions (F_(4,101) = 2.44, p=0.104, η²=0.09), suggesting that participants could learn to volitionally regulate the ipsilateral sensorimotor excitability while maintaining constant contralateral sensorimotor excitability (Figure 4C). A linear mixed-effect model that considered the contralateral SMR-ERD and ipsilateral SMR-ERD as fixed effect in the statistical regression model, and participants as random effect revealed a significant effect of the ipsilateral SMR-ERD (p<0.001) on IHI magnitude, but no main effect of the contralateral SMR-ERD or contralateral SMR-ERD × ipsilateral SMR-ERD interaction (p=0.828 and p=0.058, respectively).

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To examine the IHI magnitude during neurofeedback, we further compared the IHI magnitude across sessions by calculating the percentage changes in IHI magnitude to baseline (i.e. NoFB session), and investigating the difference between REST, HIGH, MID, and LOW sessions. A one-way rmANOVA for sessions (four levels: REST, HIGH, MID, and LOW) revealed significant differences (F_(3,81) = 8.32, p<0.001, η²=0.24). Post-hoc two-tailed paired t-tests showed significant differences between HIGH and MID sessions (Cohen’s d=1.06, p=0.017), and between HIGH and LOW sessions (Cohen’s d=1.42, p<0.001), but not between MID and LOW sessions (Cohen’s d=0.45, p=0.734; Figure 4—figure supplement 1). The normalized results were compatible with the results without normalization presented in Figure 4.

To further examine the influence of spontaneous SMR fluctuations on IHI, non-triggered TMS trial (referred to as the failed trial) was delivered in random timing ranging from 5.5 to 6 s during the MI epoch. For example, data from HIGH session in Figure 4—figure supplement 2 were collected from MID and LOW sessions, with the target ranges of SMR-ERD in HIGH session (Figure 4—figure supplement 2). A two-way rmANOVA for sessions (three levels: HIGH, MID, and LOW) and trials (two levels: triggered TMS trials and non-triggered TMS trials) revealed a significant main effect for sessions (F_{(2, 117)} = 5.08, p=0.008, and η²=0.08), but no main effect for trials (F_{(1, 117)} = 0.32, p=0.575, and η²<0.01) and for interaction (F_{(2, 117)} = 1.82, p=0.167, and η²=0.03). Thus, no significant difference in IHI between the volitional control of SMR-ERDs in the closed-loop environment differs from spontaneous SMR fluctuations was observed. Post-hoc two-tailed paired t-tests showed significant differences between the HIGH and MID sessions (Cohen’s d=0.94, p=0.017), and between HIGH and LOW sessions (Cohen’s d=1.36, p<0.001) in the triggered TMS trials. In contrast, in the non-triggered TMS trials, post-hoc two-tailed paired t-tests showed no significant differences between HIGH and MID sessions (Cohen’s d=0.20, p=1.00), and between HIGH and LOW sessions (Cohen’s d=0.28, p=1.00).

Although we revealed that modulating the excitability of the right hemisphere changes IHI magnitude from the right to the left hemisphere, the specificity of the IHI manipulation has not been proved yet. We presented additional results related to changes in corticospinal excitability as assessed by MEPs in the left FDI in response to the CS over the right hemisphere (Figure 4—figure supplement 3B). To investigate whether changes in IHI from the right to the left hemisphere and changes in corticospinal excitability in the right hemisphere are independent phenomena, a generalized linear model with the percentage changes of left MEP amplitude as a covariate of no interest was applied. This approach tests whether the effect of IHI manipulation survives even if the covariate is added. During the triggered TMS trials, a generalized linear model for sessions (three levels: HIGH, MID, and LOW) revealed a significant main effect for sessions (F_{(3, 60)} = 12.75, p<0.001, and η²=0.44). Post-hoc two-tailed paired t-tests in the increase in IHI showed significant differences between HIGH and MID sessions (Cohen’s d=1.06, p<0.001), and between HIGH and LOW sessions (Cohen’s d=1.42, p<0.001). During the non-triggered TMS trials, a generalized linear model for sessions (three levels: HIGH, MID, and LOW) revealed no main effect for sessions (F_{(3, 60)} = 1.13, p=0.319, and η²=0.10). Therefore, we successfully demonstrated that IHI changes greater than the variance explained by the CS effect and manipulation is not simply an epiphenomenon.

In the control muscle (abductor digiti minimi, ADM), such observed modulation was not driven. A one-way rmANOVA for sessions (five levels: REST, NoFB, HIGH, MID, and LOW) revealed significant differences (F_(4,101) = 2.51, p<0.048, and η²=0.12). Across the three BCI-based neurofeedback sessions (i.e. HIGH, MID, and LOW sessions) for the comparison of IHI magnitude, post-hoc two-tailed paired t-tests showed no significant difference between sessions (HIGH-MID: Cohen’s d=0.10, p=1.00; HIGH-LOW: Cohen’s d=0.05, p=1.00; MID-LOW: Cohen’s d=0.15, p=1.00), whereas significant difference was observed between REST and NoFB sessions (Cohen’s d=0.45, p=0.024; Figure 4—figure supplement 4A). Statistical analysis for TS-only revealed no significant differences in MEP amplitude between the sessions (F_(4,101) = 0.62, p=0.649, and η²=0.03; REST: 0.54 ± 0.51 mV, NoFB: 0.50 ± 0.39 mV, HIGH: 0.73 ± 0.61 mV, MID: 0.59 ± 0.50 mV, and LOW: 0.65 ± 0.51 mV; Figure 4—figure supplement 4B), indicating that IHI manipulation occurred only in the targeted muscle.

Associations between IHI magnitude and bilateral EEG patterns

To examine the association between IHI magnitude and bilateral EEG patterns, we performed within- and across-participant correlation analyses, respectively. In the within-participant correlation analysis between IHI magnitude and bilateral SMR-ERDs in each participant, 7 of the 22 participants showed a significant correlation between IHI magnitude and ipsilateral SMR-ERD (r = –0.307 ± 0.252 [mean ± SD]), but not between IHI magnitude and contralateral SMR-ERD in three neurofeedback sessions (r = –0.059 ± 0.298 [mean ± SD]). A chi-squared test was performed to determine the overall significance of the 22 individual correlations between IHI magnitude and contralateral or ipsilateral SMR-ERDs. A comparison of the proportions of significant results obtained from the ipsilateral (7 out of 22 participants) and contralateral (1 out of 22 participants) SMR-ERDs with the proportion expected due to chance (i.e. alpha level = 0.05) showed statistically significant differences (chi squared value = 5.50, p=0.019 [Fisher’s exact test: p=0.046]). The distributions of correlation coefficients across all participants were shown in Figure 5A.

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In the across-participant correlation between IHI magnitude and contralateral or ipsilateral SMR-ERDs, we performed a repeated measures correlation analysis (Bakdash and Marusich, 2017). We found a significant correlation in the ipsilateral SMR-ERD (r_rm=–0.436, p=0.004; Figure 5B), but not in the contralateral SMR-ERD (r_rm=0.008, p=0.960; Figure 5C).

To analyze the associations between IHI magnitude and bilateral neural network from another perspective, interhemispheric functional connectivity during MI was examined. Interhemispheric functional connectivity was quantified as a Network-intensity measure (Hayashi et al., 2020), which is calculated from the corrected imaginary part of coherence (ciCOH) (Ewald et al., 2012; Vukelić and Gharabaghi, 2015). For the Network-intensity_MI, a one-way rmANOVA for the sessions (five levels: REST, NoFB, HIGH, MID, and LOW) revealed significant differences (F_(4,102) = 3.04, p=0.020, and η²=0.10). Post-hoc two-tailed paired t-tests in all five sessions demonstrated significant differences between HIGH and MID sessions (Cohen’s d=0.83, p=0.033; Figure 5D). We found no significant difference between HIGH and LOW sessions (Cohen’s d=0.40, p=1.00; Figure 5D), suggesting that interhemispheric functional connectivity did not reflect the excitatory or inhibitory activity. Associations of IHI magnitude and contralateral or ipsilateral EEG patterns in the theta (4–7 Hz), low beta (14–20 Hz), high beta (21–30 Hz), and gamma (31–50 Hz) bands were shown in Figure 5—figure supplement 1 (see also Supplementary file 1–Table 10).

Individual characteristics associated with the manipulation capability of IHI

Finally, we investigated the neural characteristics associated with the manipulation capability of IHI calculated from the percentage change between HIGH and LOW sessions using correlation-based analysis (Figure 6A). In the relationships between the manipulation capability of IHI and IHI magnitude in REST session (IHI_rest), we found a significant correlation (r = –0.447, p=0.044), indicating that participants with greater IHI at rest were able to strongly manipulate the IHI (Figure 6B). For the relationships between resting-state effective inhibitory interhemispheric network assessed by IHI_rest and interhemispheric functional connectivity at the EEG level (Network-intensity_rest), participants with greater IHI_rest showed larger Network-intensity_rest (r=0.547, p=0.013; Figure 6C). Furthermore, we verified whether IHI_rest may be associated with intrinsic EEG profiles in NoFB sessions including bilateral SMR-ERDs. We found a significant correlation between IHI_rest and ipsilateral SMR-ERD during MI (r = –0.619, p=0.004), but not between IHI_rest and contralateral SMR-ERD during MI (r = –0.283, p=0.228).

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In addition to the alpha band, the beta band is also a well-established EEG signature of motor execution and imagery (Crone et al., 1998; Pfurtscheller, 2001). To verify whether resting-state functional connectivity in other frequency bands (i.e. outside of feedback frequency) was associated with IHI, an across-participant Pearson’s correlation was calculated. In the across-participant correlations between IHI_rest and Network-intensity_rest in the theta (4–7 Hz), low beta (14–20 Hz), high beta (21–30 Hz), and gamma (31–50 Hz) bands, we found significant correlation in the high beta (r=–0.618, p=0.004), but not in theta (r=–0303, p=0.194), low beta (r=–0.229, p=0.331), and gamma (r=–0.390, p=0.089) bands (Figure 6—figure supplement 1A). To attenuate the aberrant effect of values in some especially low Network-intensity_rest or high (CS + TS)/TS at rest (outliers) in the theta, low-beta, high-beta, and gamma bands, the jackknife correlation and the bias were calculated (theta: r=–0.207, bias = 2.02; low-beta: r=–0.121, bias = 3.65; high-beta: r=–0.528, bias = –4.09; gamma: r=–0.310, bias = 0.06). In addition, the correlation between IHI_rest and the contralateral or ipsilateral EEG patterns in frequency bands outside of a target alpha band were in Figure 6—figure supplement 1B and C, respectively.

In the present study, we aimed to determine whether it is possible to manipulate the IHI magnitude and uncover related neural activity behind IHI modulation, while controlling bilateral SMR-ERDs via spatially bivariate BCI-based neurofeedback. This was the first study to show IHI-state manipulation with a large dynamic range induced by volitional variation in ipsilateral sensorimotor excitability expressed by SMR-ERD. In addition, resting-state interhemispheric networks at the TMS (IHI_rest) and EEG (interhemispheric Network-intensity_rest) levels were associated with the manipulation capability of IHI magnitude.

Previous neurofeedback studies have demonstrated that it is possible to gain voluntary control over central nervous system activity without externally administered interventions, if appropriate neurofeedback is embedded in a reinforcement learning task, such as food rewards for animals (Engelhard et al., 2013; Fetz, 2013) and visually rewarding stimuli for humans (Thompson et al., 2009). However, conventional BCI-based neurofeedback of the SMR signal from one hemisphere does not guarantee spatial specific activation of the sensorimotor region in the targeted hemisphere (Buch et al., 2008; Caria et al., 2011; Soekadar et al., 2015a) because sensorimotor activities in the left and right hemispheres potentially influence one another, making IHI manipulation difficult. Using spatially bivariate BCI-based neurofeedback enables participants to volitionally increase or decrease (bidirectional) the ipsilateral sensorimotor excitability, while maintaining constant contralateral sensorimotor excitability. Ipsilateral SMR-ERD to the imagined hand (but not contralateral), reflecting IHI magnitude (Figure 2B) and their significant correlation (Figure 5C), were explained by previous studies (Haegens et al., 2011; Khademi et al., 2018; Kraus et al., 2016; Madsen et al., 2019; Naros et al., 2020; Ros et al., 2010; Sauseng et al., 2009; Takemi et al., 2013; Thies et al., 2018; Zarkowski et al., 2006). The in vivo cortical recordings in monkeys revealed that pericentral alpha power was inversely related with the normalized firing rate in the sensorimotor regions (Haegens et al., 2011). In humans, several studies using single-pulse TMS of the motor hand area combined with EEG tested how ongoing pericentral oscillatory activity impacts corticomotor excitability reflected by the MEP amplitude. These EEG-TMS studies found a negative relationship between pre-stimulus alpha power and MEP amplitude through both offline (Sauseng et al., 2009; Takemi et al., 2013; Zarkowski et al., 2006) and online (Madsen et al., 2019) approaches, whereas the opposite of that was also found (Thies et al., 2018). Furthermore, another EEG-TMS study using neurofeedback demonstrated that the endogenous suppression of alpha rhythms at rest can produce robust increase in MEP amplitude and decrease in short-interval intracortical inhibition (Ros et al., 2010). Similarly, in the beta band, some evidences were revealed that a negative relationship between beta power and MEP amplitudes (Khademi et al., 2018; Kraus et al., 2016; Naros et al., 2020). As for IHI through the transcallosal fiber, it is predominantly regulated through direct postsynaptic mechanisms in the apical dendritic shafts of pyramidal neurons and a specific cortical microcircuitry mediated by dendritic GABA_B receptors in inhibitory interneurons (Palmer et al., 2012). Additionally, consistent with this phenomenon, down-regulation of ipsilateral SMR-ERD, e.g., may influence the excitation of the ipsilateral transcallosal pyramidal neuron followed by disinhibition of the contralateral inhibitory interneurons during unilateral upper limb MI. Therefore, pronounced changes in cortical mechanism despite the absence of sensory input and constant MEP amplitude in the imagined right hand elicited by single TS suggest that the modulation of IHI magnitude from the ipsilateral (right) to the contralateral (left) hemisphere was at least partly of cortical, rather than spinal origin. These neural mechanisms can be explored further by using triple pulse procedures (Ni et al., 2011) in which (dis)inhibition can be directly measured as the modulation of short-interval intracortical inhibition.

The potential contribution of resting-state IHI magnitude and interhemispheric functional connectivity in the alpha and high beta bands to the current results of manipulation capability in IHI (Figure 6B and C and Figure 6—figure supplement 1A) can be speculated by considering the previous studies. Inter-regional communication is accompanied by synchronized oscillations in different brain regions (Fries, 2005; Varela et al., 2001), and this synchronization can be evaluated by functional connectivity. Because both hemispheres are structurally connected by transcallosal projection and exhibit functional cross talks (Hofer and Frahm, 2006; Meyer et al., 1995), the manipulation capability of IHI is likely associated with a structural connectivity as well as local oscillatory power entrainment. As for frequency band outside of neurofeedback target, it is well known that the intensity of SMR-ERD in the beta band reflects the sensorimotor cortical excitability and corticomuscular activation (Hussain et al., 2019; Schulz et al., 2014). This multimodal EEG-TMS approach proves that the bilateral alpha and beta activities observed in the current study served to modulate the functional interhemispheric interaction over motor cortices.

For the other modalities, e.g., real-time fMRI-based neurofeedback to a single region of interest (Sitaram et al., 2017; Weiskopf et al., 2004) or inter-regional functional connectivity (Liew et al., 2016; Pereira et al., 2019) can be used for volitional modulation of neuronal connectivity and could serve as a possible therapeutic tool for motor or cognitive training in diseases related to impaired inter-regional connectivity. However, even fMRI-based neurofeedback with high spatial resolution, based on blood oxygenation level-dependent signal, cannot easily distinguish between the excitatory and the inhibitory activities from each region (Moon et al., 2021). Additionally, neurofeedback using interhemispheric functional connectivity has the possibility to counteract the modulation of IHIs from the right to left hemisphere and from the left to right hemisphere. Our results showed no significant difference in Network-intensity during MI between HIGH and LOW sessions (Figure 5D), indicating that interhemispheric functional connectivity at the EEG level was not changed, whereas IHI was modulated. This may indicate that IHI and Network-intensity simply reflect the activity of different neural populations; interhemispheric projections act via surround/lateral inhibition in the sensory and motor cortices (Carson, 2020), while the synchrony at the EEG level reflects changes in the activity of local interactions between pyramidal neurons and interneurons in the thalamocortical loops (Pfurtscheller and Lopes da Silva, 1999). Furthermore, it is possible that Network-intensity reflects the degree of bidirectional synchrony between the two motor cortices, while IHI reflects one-way (right-to-left) inhibitory effects. Therefore, it is unclear whether fMRI-based neurofeedback using interhemispheric functional connectivity is linked to ipsilateral excitability, and whether it modulates IHI magnitude.

Interhemispheric activity is also passively modulated by externally administered interventions, e.g., tDCS or rTMS over the motor cortices (Gilio et al., 2003; Pena-Gomez et al., 2012; Williams et al., 2010). Although such tools have neuromodulation efficacy, their long-term sustained effects are often limited and they do not have spatial specificity due to the remote effects (Di Pino et al., 2014; Notturno et al., 2014; Weiskopf et al., 2004). Conversely, under the conscious selflearning environment, volitional control of MEP amplitudes is retained for at least 6 months without further training (Ruddy et al., 2018), which supports the future prediction of long-term efficacy of IHI manipulation. In addition, we successfully demonstrated that IHI changes greater than the variance explained by the CS effect. Based on our findings, further experiments may help to better understand the pathway specificity. To further confirm whether the participants learned pathway-specific IHI manipulation, the CS effect should be kept constant across the conditions by adjusting the CS intensity to evoke left MEP of 1 mV peak-to-peak amplitude. Furthermore, the effect size of IHI manipulation in the current study (Cohen’s d=1.50) was comparable with that of representative tDCS (Cohen’s d=1.55) (Williams et al., 2010) and superior to that of rTMS studies (Gilio et al., 2003) (Cohen’s d=0.80; note that it is a read from the graph). Two meta-analyses of clinical trials using rTMS and tDCS also indicated that the effect sizes were medium (0.4–0.6) (Adeyemo et al., 2012; Hsu et al., 2012). Based on a previous study of neurofeedback training combined with externally administered interventions (Ang et al., 2015), their combination may contribute to facilitating neural plasticity.

Our techniques and evidences are expected to be applied in various fields, e.g., in the context of neurorehabilitation. In stroke patients, the interhemispheric imbalance/competition model predicts the presence of asymmetry in the interhemispheric sensorimotor network, with excessive inhibition from the non-affected hemisphere limiting motor performance and maximal recovery (Duque et al., 2005; Murase et al., 2004). Therefore, it is believed that guiding inhibitory interhemispheric network to the appropriate pattern through both targeted up-conditioning in the affected hemisphere and down-conditioning in the non-affected hemisphere may contribute to reduced abnormal IHI and enhanced achievable functional recovery (Chieffo et al., 2013; Di Pino et al., 2014; Dong et al., 2006; Hummel and Cohen, 2006). Although the rehabilitation strategy of attempting to rebalance interhemispheric networks in order to improve motor recovery after stroke is controversial (Bundy et al., 2017; Carson, 2020; Xu et al., 2019), the current technique can be tailored either to up-conditioning the damaged hemisphere, down-conditioning the intact hemisphere, a combination of both, or vice versa depending on the patient’s specific states. Furthermore, a previous review advocated the conceptual framework and suggested that a fundamental role of IHI is to support the narrowing of excitatory focus by co-opting the capacities of the two cerebral hemispheres (Carson, 2020). This framework is consistent with the known associations between the structural integrity of callosal projections and the magnitude of the motor deficits after stroke (Auriat et al., 2015; Granziera et al., 2012; Koh et al., 2018). Thus, our spatially bivariate EEG-neurofeedback approach might be highly relevant to application perspective in the context of stroke recovery as well as a basic science perspective. Future work probing the residual ability to manipulate IHI in stroke patients is warranted.

Source data used to generate the figures are publicly available via Dryad Digital Repository, accessible here: Hayashi, Masaaki (2021), Spatially bivariate EEG-neurofeedback can manipulate interhemispheric rebalancing of M1 excitability, Dryad, Dataset, https://doi.org/10.5061/dryad.hhmgqnkj3 Scripts used for the neurofeedback experiment are available on GitHub (https://github.com/MasaakiHayashi/elife-neurofeedback-experiment, (copy archived at swh:1:rev:20fab186058b4f59577c1d4da31f9fff377a129e)).

1. 1. Hayashi M

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   Data from Spatially bivariate EEG-neurofeedback can manipulate interhemispheric rebalancing of M1 excitability.

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Author details

1. Masaaki Hayashi

   Graduate School of Science and Technology, Keio University, Kanagawa, Japan

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   is employed by LIFESCAPES Inc

   "This ORCID iD identifies the author of this article:" 0000-0002-7104-6765

2. Kohei Okuyama

   Department of Rehabilitation Medicine, School of Medicine, Keio University, Tokyo, Japan

   Contribution

   Data curation, Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Nobuaki Mizuguchi

   Research Organization of Science and Technology, Ritsumeikan University, Shiga, Japan

   Contribution

   Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Ryotaro Hirose

   Graduate School of Science and Technology, Keio University, Kanagawa, Japan

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Taisuke Okamoto

   Graduate School of Science and Technology, Keio University, Kanagawa, Japan

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Michiyuki Kawakami

   Department of Rehabilitation Medicine, School of Medicine, Keio University, Tokyo, Japan

   Contribution

   Resources, Validation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Junichi Ushiba

   Faculty of Science and Technology, Keio University, Kanagawa, Japan

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing – review and editing

   For correspondence

   ushiba@bio.keio.ac.jp

   Competing interests

   is a founder and the Representative Director of the University Startup Company, LIFESCAPES Inc. involved in the research, development, and sales of rehabilitation devices including brain-computer interfaces. He receives a salary from Connect Inc., and holds shares in Connect Inc. This company does not have any relationships with the device or setup used in the current study

   "This ORCID iD identifies the author of this article:" 0000-0003-1161-983X

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