A total of 25 healthy young participants (13 female and 12 male; mean age ± s.d. = 25.6 ± 4.8 years) were recruited from the University of Texas Southwestern Medical Center (UTSW) and surrounding areas for this study. Participants who met the following inclusion criteria were enrolled in the study: (1) no prior diagnosis of a psychiatric disorder, (2) no history of brain injury or nervous disorders, (3) no consumption of caffeine or nicotine within 2 h prior to the experiment, and (4) no current pregnancy. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (World Medical Association, 2013). The study protocol was approved by the Institutional Review Board (IRB) of UTSW. All participants provided written informed consent prior to participating in the study, ensuring their full understanding and agreement to participate.

The tPBM protocol utilized a continuous-wave 1,064-nm laser system (CG-5000, Cell Gen Therapeutics, LLC; Dallas, TX, United States). This light stimulation device has received FDA clearance for the treatment of inflammation and pain relief. The laser unit delivered a power density of 250 mW/cm² across a 4.2-cm-diameter aperture during stimulation of the right prefrontal cortex of the participants, as reported in our earlier studies (Wang et al., 2019, 2021).

The MEG data were acquired using a MEGIN Neuromag TRIUX Neo system, which included 204 planar gradiometers and 102 magnetometers, with a sampling rate of 1 kHz during the 6-min pre- and post-tPBM epochs. Because of the large size of the MEG helmet and the close distance to the human head required for MEG scans, it was impossible to deliver laser light concurrently with the MEG data acquisition. During the 8-min tPBM stimulation, the participant was asked to sit in a lower position with the forehead exposed below the MEG helmet for light delivery (see the photo in Figure 1A).

Structural T1-weighted brain MR images for each participant were acquired during the visit using a 3 T Siemens scanner and an MPRAGE sequence with a resolution of 0.9 mm (TR = 1,900 ms; TE = 2.93 ms; TI = 900 ms; flip angle = 9°; 176 slices). These T1 images were used for source-space localization of the MEG/EEG measurements. The MRI data were normalized to the Montreal Neurological Imaging (MNI) space using SPM12 (Tadel et al., 2011).

Participants were first seated comfortably, followed by the placement of an EEG cap on their heads. The EEG electrodes were cleaned with an alcohol solution and then covered with a conductive EEG electrolyte-rich gel. The impedance of each electrode was checked to ensure they were under 5 kΩ. Next, Head Position Indicator (HPI) coils were inserted into the EEG cap slots. The HPI coils allowed tracking and continuous position and orientation monitoring of the head in the MEG machine during the experiment. Anatomical fiducials were marked and digitized using the Polhemus system, which also marked the EEG electrodes, HPI coil locations, and an additional 300 scalp points for co-registration. Next, the participants were escorted into the MEG room and positioned in the MEG helmet. As shown in Figure 1B, a pre-tPBM resting eyes-open epoch of 6 min was recorded using simultaneous MEG/EEG. After this period, the participants were lowered to expose the tPBM stimulation site (1 cm below Fp2 in the right frontal region), and protective goggles (900–1,000 nm,5+, 1,000–2,400 nm,7+; 2,900–10,600 nm,7+) were worn by both participants and the research staff in the room. EEG data were continuously collected prior to, during, and after tPBM (see Figure 1B). The participants received active tPBM at 250 mW/cm² light irradiance for 8 min. After tPBM stimulation, the participants were raised back into the MEG helmet and a 6-min post-tPBM resting eyes-open epoch was recorded using simultaneous MEG/EEG. Finally, the participants were led to an MRI scanner for T1-weighted MRI scans, which were utilized to co-register with the anatomical fiducials marked for MEG sensors.

Several data processing steps were performed to reveal significant tPBM effects in sensor space, as outlined in Figure 2A.

Each T1-weighted MRI scan from each participant was co-registered with anatomical fiducials of the MEG/EEG sensors to ensure spatial alignment between the different modalities. Next, the MNI-ICBM152 template source grid, a widely recognized brain atlas, was warped to fit each participant’s individual T1 MRI scan. This process enabled the creation of personalized head models, onto which MEG (EEG) power changes were projected for source space analysis. Standardization of the source grids across participants facilitated the identification of consistent patterns across different participants. Subsequently, four processing steps were performed to reveal the significant tPBM effects in the source space, as outlined in Figure 2B.

After data pre-processing, global PSD curves were first calculated for each of the MEG sensors (i.e., 204 planar gradiometers) and then averaged over all the sensors for the 6-min pre-and post-tPBM periods. Figures 3A,B show examples of such global PSD curves from both MEG and EEG measurements, respectively, of a participant during 6-min pre-and post-tPBM epochs.

To accurately attribute these changes in MEG/EEG powers to oscillatory activity affected by tPBM, it is essential to separate aperiodic activity from oscillatory activity. After performing FOOOF analysis on both MEG and EEG data, we were able to split the electrophysiological power spectra into aperiodic and periodic components from each of the MEG/EEG PSD pre-and post-tPBM. For example, Figure 4 shows aperiodic (Figure 4A) and periodic (Figure 4B) features extracted from the combined MEG/EEG PSD in the 0.5–40 Hz region, which is the main frequency band of tPBM-induced significance in our data, averaged across all subjects (n = 25). Figure 4A shows a high degree of aperiodic noise in the delta (1–3 Hz) and theta (4–7 Hz) regions, whereas Figure 4B illustrates a large spike in periodic activity in the alpha (8–12 Hz) and beta (13–30 Hz) bands. These results support the significant increase in oscillatory power by tPBM in these two frequency bands.

Next, to observe the spatial effects of tPBM on the MEG/EEG spectral power in sensor space, we followed Steps 2 and 3 by calculating individual PSD curves for each sensor and determined the band-averaged MEG/EEG powers for all six (or five) bands during pre-or post-tPBM epochs. Specifically, we quantified the percent changes in the MEG/EEG signal power [see Eq. (1)] by computing the tPBM-induced relative PSD power changes for each sensor at each of the respective frequency bands. The sensor-based percentage alterations in MEG power were plotted as interpolated topographies on a standard circular head model (Figure 5A), with the most pronounced increases (>20% with respect to the baseline/pre-tPBM) in the delta, alpha, and beta bands. An increase in power appeared in the frontal to central regions near the site of the tPBM. Following the same data processing steps, we also obtained five interpolated topographies of EEG power percentage alterations in the five frequency bands, as shown in Figure 5B. Note that the tPBM-induced alteration patterns in both MEG and EEG power were highly consistent in both spatial and spectral aspects.

The Welch method was also employed to calculate tPBM-induced effects in source space. Constrained modeling was performed after subtracting the pre-tPBM power from the post-tPBM power to generate voxel-wise power differences. Similar to the format of the topographies seen in sensor space (Figure 5), the source imaging solutions of tPBM effects in source space are presented as relative (percentage) changes in MEG/EEG power with respect to the pre-tPBM baseline, as shown in Figure 6. 8-min of tPBM facilitated increases in 6-min-averaged MEG/EEG power mainly in the alpha and beta bands (Figure 6). Specifically, the alpha band demonstrated a 15%–30% increase in power in the frontal, central, and parietal regions on the ipsilateral (right) side (with respect to the stimulation site), while the beta band exhibited a 25%–30% increase in power widespread throughout the ipsilateral cortex. Again, the tPBM-induced MEG and EEG power alterations in source space showed similar trends in spatial (frontal, central, and ipsilateral) and spectral (alpha and beta bands) aspects, while MEG source images showed more ipsilateral effects than the EEG source images.

After family wise error correction using cluster-based permutation statistics in Brainstorm, PSD source space maps for both MEG and EEG measurements are presented with cortical views in Figures 7A,B. Significant increases in electrophysiological activity following 8-min tPBM occurred (1) surrounding the site of stimulation in the alpha band and (2) spreading across the ipsilateral hemisphere in the beta band. These two specific observations were highly consistent between the brain/cortex images derived from the concurrently measured MEG and EEG time series.

Moreover, discrepancies seemingly arise between the sensor (Figure 5) and source (Figure 6) space evaluations of tPBM in the MEG. Specifically, relative changes in delta and theta band activities in the sensor topographies with clusters exhibit 10 to 30% changes, whereas the source space activity changes are all limited to below 10%. We expect or assume that these discrepancies might be attributed to the inherent limitations of source space reconstruction techniques, which could underestimate activity in deeper or more complex brain regions. To address this point, we also showed a sagittal view of significant changes in electrophysiological power in both MEG and EEG source space activity in Figures 7C,D, respectively. These two figure panels provide a more comprehensive visualization of the deep spatial distribution of changes in all five frequency band activities. It is clear that right-forehead 8-min tPBM induced significant increases in electrophysiological activity only in the alpha and beta bands with consistent reconstructed sources from both MEG and EEG concurrent measurements, but no significant tPBM-induced changes in delta and theta frequency bands.

As shown in Figures 5–7, tPBM has a significant post-stimulation impact on the power of electrophysiological oscillations in the alpha (8–12 Hz) and beta (13–30 Hz) bands, in both sensor and source space derived from both MEG and EEG measurements. The MEG source images, resulted from 204 gradiometers, should enable a higher spatial resolution than that obtained from the concurrent 64-channel EEG measurements. After cluster-based permutation statistical analysis, both MEG and EEG brain source images in response to 8-min tPBM were in excellent agreement in that tPBM significantly increased (1) the alpha power near the right prefrontal or frontal cortex, and (2) the beta power in the ipsilateral hemisphere (on the same side of tPBM) spreading from the frontal to central and then to parietal cortical regions. 8-min of prefrontal 1,064-nm laser tPBM facilitates significant enhancement in electrophysiological activity in both alpha and beta oscillations.

The alterations in alpha and beta frequency power in the cortex are of significant interest as recent studies have documented their prevalence in several resting-state studies (Rosanova et al., 2009; Barry et al., 2019; Capilla et al., 2022). These frequencies have also been recognized in previous research as playing a critical role in various auditory (Teplan et al., 2006a,b; Kumar et al., 2016) and visual (Wróbel, 2000) data processing pathways within the brain. However, the exact connection or association between these frequency bands and tPBM stimulation is unclear and needs further investigation. Future task-based studies, specifically designed to elicit a response in these frequency bands, are necessary to fully elucidate the relationship between tPBM and the alterations in alpha and beta power within the brain.

Source localization in the prefrontal cortex (Figure 7) exhibited significant increases in MEG/EEG alpha power after 8 min of tPBM. The sources were located more on the ipsilateral side and extended from the right prefrontal (near or at the light delivery site), right dorsolateral prefrontal, to right medial prefrontal regions. The latter two regions are part of the default mode network. Furthermore, significant increases in MEG/EEG beta power occurred in the cortical regions, both spread over the ipsilateral hemisphere and spanned the contralateral areas in a reduced proportion. Notably, these beta-power-stimulated regions include the bilateral dorsolateral prefrontal region, which is a crucial cortical area for complex cognitive activities, such as planning and decision-making; the right parietal cortex, which is involved in spatial awareness and sensory integration; and both the right and left occipital regions, associated with visual processing (Culham and Kanwisher, 2001; Curtis and D'Esposito, 2003; Bullmore and Sporns, 2009; Bressler and Menon, 2010; Levy and Wagner, 2011; Huang and Sereno, 2013). The involvement of these regions suggests a widespread activation pattern with potential network engagement. This could contribute to the observed effects of tPBM on improved cognition and behavior reported in other studies. This set of beta source localizations indicates a more generalized effect associated with tPBM stimulation, with the most significant effect still at the lateral site of tPBM stimulation.

All tPBM-stimulated cerebral regions found in this study were highly consistent with those reported using an EEG-derived network analysis based on the group singular value decomposition algorithm and the eLORETA software package (Wang et al., 2022). The consistency of the results obtained using the independent modalities from two different groups of participants and operators provided strong evidence of the reliability of the measurements. Moreover, the observation that tPBM significantly stimulated more widespread cerebral regions at the beta rhythm provides a rationale for why only in the beta frequency band, tPBM was shown to enhance the complexity of the global brain network, augment local information flow, and integrate oscillations across prefrontal cortical regions (Shahdadian et al., 2022). Overall, our MEG and EEG findings contribute to a growing body of evidence that tPBM facilitates significant alterations in neuro-electrophysiological activity (Zomorrodi et al., 2019).

It is well known that specific regions of the cortex, such as the right frontal, parietal, and occipital regions, are cerebral sources for human cognitive functions (Sehatpour et al., 2008; Tse et al., 2021). This study using MEG and EEG neuroimaging revealed significant alterations in alpha and beta power following an 8-min tPBM intervention across similar regions. Moreover, numerous studies have reported that tPBM facilitates the improvement of cognitive function and reduces the symptoms of neurodegenerative diseases (Rojas and Gonzalez-Lima, 2013; Purushothuman et al., 2014; Cassano et al., 2015; Farfara et al., 2015; Blanco et al., 2017). tPBM may be a promising therapeutic tool for treating certain neurological diseases.

The results shown in Figure 5 correspond to tPBM-induced percentage changes of EEG alpha and beta power in sensor space with results similar to previous EEG studies (Wang et al., 2019; Zomorrodi et al., 2019; Wang et al., 2021). The same experimental and tPBM protocols were used in those studies with differences in post-tPBM of 6 min (current study) vs. 3 min (prior studies; Wang et al., 2019, 2021). Regardless of the temporal length difference in post-tPBM data acquisition, the EEG alpha power topographies between these studies are highly consistent. The observation that post-tPBM increases in beta power in the previous study were weaker than those in this study (Figure 5) can be attributed to a shorter data acquisition time (3 vs. 6 min, respectively). Overall, the tPBM-induced EEG power alterations in both the alpha and beta bands in sensor space were in good agreement between this study and previous reports. In addition, our results highlight significant post-tPBM effects lasting for at least 6 min after stimulation. The duration of significant tPBM effects however is not known.

Because tPBM uses light and thus may create non-negligible thermal effects, especially with a 1,064-nm laser, it is reasonable to question whether the observed electrophysiological signal changes resulted from tissue heating by the laser. However, a recent study of Dmochowski et al. (2020) employed magnetic resonance thermometry to measure brain temperature during 10-min tPBM (n = 20) with an 808-nm laser and found no significant temperature differences in the cortex between active and sham stimulation. Another group conducted computer simulations of the motor cortex tPBM at 500 mW/cm² at three wavelengths (630, 700, and 810 nm). They found a temperature increase in the scalp below 0.25°C and a minimal temperature increase in the gray matter of less than 0.04°C at 810 nm. Similar tPBM heating outcomes were obtained for 630 nm and 700 nm light (Bhattacharya and Dutta, 2019). More specifically, one of our recent studies demonstrated that 1,064-nm tPBM and thermal stimulations induced significantly different topographies of changes in EEG alpha and beta power, providing evidence to support that laser-induced heat on the human forehead is not a mechanistic source causing increases in EEG power during and after tPBM (Wang et al., 2021). Accordingly, the observed changes in MEG spectral powers are unlikely to result from thermal effects.

We acknowledge that the inclusion of data analysis from the corresponding sham-conditioned measurements would be ideal to overcome potential confounding factors. However, our current data analysis took a simpler statistical approach to compare the respective MEG/EEG parameters pre-and post-intervention to assess the impacts of tPBM. More comprehensive statistical analyses should be performed in future studies.

Given the current analysis methodology for MEG/EEG data, a key concern was the specific confounding effect of short-time sleepiness spent within the MEG scanner, which would affect changes in alpha-band activity related to vigilance. To address this concern, it is imperative to consider both the context of our study and relevant findings in the literature. Gibbings et al. (2021) investigated the EEG and behavioral correlates of mild sleep deprivation, a condition analogous to the potential effects of short-term drowsiness on vigilance. They reported that vigilance decrement or drowsiness is typically associated with an increase in alpha band power in the occipital region and a general decrease in beta band power, which are indicative of reduced alertness or sleepiness. In contrast, our observations showed neither the anticipated increase in occipital alpha power nor the expected decrease in beta band power. Instead, the electrophysiological data we collected were indicative of maintained, if not enhanced, cognitive engagement and alertness, inconsistent with the patterns of decreased vigilance described by Gibbings et al. (2021). There is also a larger body of studies with similar supporting evidence, specifically in the alpha and beta frequency bands (Lockley et al., 2006; van Dijk et al., 2008; Lew et al., 2021). This discrepancy between the context of our study and relevant findings in the literature underscores our position that the approximately 20-min resting state in the MEG scanner environment in our study does not significantly compromise alpha or beta frequency band data.

We did not consider the tPBM effects caused by differences in skin pigmentation in this study. In theory, darker or lighter skin pigmentation can affect the absorption of light used for tPBM.

Last, head movements are a problem in EEG data collection. While the EEG coordinate system is always aligned to the head, as it is fixed to the scalp, EEG data must always be scrutinized for large muscle movement artifacts. However, use of the HPI coil in our MEG scanning effectively tracked motions and made corrections using the MEGIN MAXFilter program (Taulu and Simola, 2006; Medvedovsky et al., 2007).

tPBM, TMS, and tDCS are noninvasive neurostimulation methods with diverse mechanisms of action and neurophysiological effects. While TMS uses magnetic fields to induce electric currents in the neurons of the brain (Aberra et al., 2020) and tDCS employs direct electrical currents to modulate neuronal activity (Stagg et al., 2018), tPBM utilizes near-infrared light to affect mitochondrial activity and cellular metabolism, potentially leading to alterations in neural activity. Our findings demonstrated significant changes in alpha and beta band activities following tPBM, suggesting its efficacy in modulating cortical excitability and neural dynamics. This is particularly relevant in the context of cognitive enhancement and mental health applications, where the modulation of specific frequency bands has been linked to therapeutic outcomes. Comparatively, TMS and tDCS have been shown to influence cortical excitability with varying degrees of specificity and penetration depth, often depending on the parameters used (Ardolino et al., 2005; Zaghi et al., 2010). As suggested by this study, the distinct advantage of tPBM lies in its ability to induce widespread neurophysiological changes without direct electrical stimulation, potentially reducing discomfort and increasing the feasibility of its application.

This study has several limitations. (1) Although MEG can identify electrophysiological activity, it has limitations in measuring deeper brain effects of tPBM. This is because of the magnetic nature of the MEG device, which detects magnetic fields orders of magnitude smaller than those detected by EEG (Malmivuo, 2012). This limitation becomes increasingly prevalent as the signal amplitude decreases with time after stimulation. (2) The sensors used in MEG preferentially detect magnetic fields perpendicular to the orientation of the sensor surfaces (Srinivasan et al., 2006; Ahlfors et al., 2010). Thus, MEG is not as sensitive to tPBM-induced magnetic signals parallel to the sensor surface. On the other hand, EEG can detect electromagnetic fields perpendicular and parallel to sensor surfaces. Thus, an advanced algorithm that jointly analyzes both EEG and MEG signals may be advantageous in providing a more comprehensive assessment of tPBM with better spatial and temporal resolution. (3) We also acknowledge the limitation associated with not employing a neuronavigation system to precisely identify the stimulation area, which is commonly used in TMS studies. This could potentially introduce variability in our findings due to the less precise localization of the stimulated area. We recognize that neuronavigation could enhance the specificity of our results by ensuring consistent targeting across participants. However, the observed widespread effects of tPBM suggest that tPBM affects broader neural networks, which may mitigate some concerns regarding the exact stimulation site. Future studies could benefit from incorporating neuronavigation to further explore the specificity of the tPBM effects. (4) Last, we recognize the limitation of using the identity matrix for applying noise covariance in source modeling, as opposed to deriving it from empty room noise recordings. While our approach was practical and aligned with common practices in the field, we understand that it may oversimplify the assumption that noise is uncorrelated across sensors. This could potentially impact the accuracy of the source localization and noise estimation. Thus, future research should consider employing comprehensive methods for noise covariance estimation, such as using empty room recordings, to improve the reliability of source modeling results.