Enhancement of Sleep Slow Wave Activity using Transcranial Electrical Stimulation with Temporal Interference

Enhancement of Sleep Slow Wave Activity using Transcranial Electrical Stimulation with Temporal Interference | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Enhancement of Sleep Slow Wave Activity using Transcranial Electrical Stimulation with Temporal Interference View ORCID Profile Erin L. Schaeffer , Ido Haber , View ORCID Profile Zhiwei Fan , View ORCID Profile Simone Bruno , Beril Mat , Tariq Alauddin , Giulietta Vigueras , Lindsey Neumann , Richard Smith , View ORCID Profile Florian Missey , View ORCID Profile Adam Williamson , View ORCID Profile Peter Achermann , Stefan Beerli , Myles Capstick , View ORCID Profile Esra Neufeld , View ORCID Profile Niels Kuster , View ORCID Profile Robin I. Goldman , View ORCID Profile Richard J. Davidson , View ORCID Profile Larissa Albantakis , View ORCID Profile Stephanie G. Jones , View ORCID Profile Chiara Cirelli , View ORCID Profile Melanie Boly , View ORCID Profile Giulio Tononi doi: https://doi.org/10.1101/2025.08.11.25333452 Erin L. Schaeffer 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin 2 Medical Scientist Training Program, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Erin L. Schaeffer Ido Haber 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin 3 Department of Biomedical Engineering, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhiwei Fan 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin 4 International Institute for Integrative Sleep Medicine (WPI-IIIS), Tsukuba Institute for Advanced Research (TIAR), University of Tsukuba , Tsukuba, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zhiwei Fan Simone Bruno 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Simone Bruno Beril Mat 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tariq Alauddin 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site Giulietta Vigueras 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lindsey Neumann 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site Richard Smith 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site Florian Missey 5 International Clinical Research Center, St. Anne’s University Hospital Brno , Brno, Czech Republic 6 Institute de Neurosciences des Systèmes (INS), INSERM, Aix-Marseille Université , Marseille, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Florian Missey Adam Williamson 5 International Clinical Research Center, St. Anne’s University Hospital Brno , Brno, Czech Republic 6 Institute de Neurosciences des Systèmes (INS), INSERM, Aix-Marseille Université , Marseille, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Adam Williamson Peter Achermann 7 Institute of Pharmacology and Toxicology, University of Zurich , Zurich, Switzerland 8 Foundation for Research on Information Technologies in Society (IT’IS) , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Peter Achermann Stefan Beerli 8 Foundation for Research on Information Technologies in Society (IT’IS) , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Myles Capstick 8 Foundation for Research on Information Technologies in Society (IT’IS) , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Esra Neufeld 8 Foundation for Research on Information Technologies in Society (IT’IS) , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Esra Neufeld Niels Kuster 8 Foundation for Research on Information Technologies in Society (IT’IS) , Zurich, Switzerland 9 Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology (ETH) Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Niels Kuster Robin I. Goldman 10 Center for Healthy Minds, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Robin I. Goldman Richard J. Davidson 10 Center for Healthy Minds, University of Wisconsin-Madison , Madison, Wisconsin 11 Department of Psychology, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Richard J. Davidson Larissa Albantakis 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Larissa Albantakis Stephanie G. Jones 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephanie G. Jones Chiara Cirelli 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chiara Cirelli Melanie Boly 12 Department of Neurology, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Melanie Boly For correspondence: gtononi{at}wisc.edu boly{at}neurology.wisc.edu Giulio Tononi 1 Department of Psychiatry, University of Wisconsin-Madison , Madison, Wisconsin Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Giulio Tononi For correspondence: gtononi{at}wisc.edu boly{at}neurology.wisc.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Slow waves mediate restorative functions of non-rapid eye movement (NREM) sleep. To enhance slow wave activity (SWA, 0.5-4 Hz) non-invasively, we employed a novel neuromodulatory tool, Transcranial Electrical Stimulation with Temporal Interference (TES-TI). Healthy participants (n=21) received TES-TI during NREM sleep over several nights (∼10 stimulation periods, 3min each, first half of night, 4-week protocol), targeting left ventromedial prefrontal cortex—a “hot spot” of slow wave generation. Two high frequency carriers with 1Hz difference (TES 15kHz -TI 1Hz ) produced amplitude modulated temporal interference at 1Hz. SWA, measured with simultaneous high-density electroencephalography (hd-EEG), was increased by TES 15kHz -TI 1Hz , with the effect outlasting the stimulation period. Higher frequencies (sigma and beta) decreased. Pure TES 15kHz did not have comparable effects. The incremental effects of TES 15kHz -TI 1Hz on SWA between first and last intervention night were positively correlated with subjective ratings of restorative sleep. This is the first study demonstrating that TES-TI can enhance sleep and its restorative function. Introduction During non-rapid eye movement (NREM) sleep, which comprises 75–80% of total time spent in sleep, individual slow waves are detected on the EEG when many cortical neurons switch near-synchronously between ON periods of firing and OFF periods of silence 1 . Sleep slow waves are homeostatically regulated. Thus, slow wave activity (SWA, electroencephalographic power in 0.5–4 Hz range), a combined measure of the number and amplitude of slow waves, is high in early sleep, even higher after sleep deprivation, and declines in the course of sleep when sleep need is discharged 2 - 4 . Slow waves are believed to mediate key restorative functions attributed to NREM sleep, including synaptic renormalization and memory consolidation 5 - 8 . Disrupted SWA is associated with worsened cognitive functioning, including poorer performance on visuomotor and perceptual learning tasks 9 - 11 . Conversely, the enhancement of SWA has been shown to improve memory performance 12 - 16 . Two interventions commonly used to enhance SWA are acoustic stimulation and transcranial electrical stimulation (TES) 12 - 14 . However, both techniques have limitations. Acoustic stimulation strong enough to elicit SWA can be close to the threshold for awakening the participant 10 . Conventional TES, which typically employs frequencies below 1 kHz, results in electrical artifacts during stimulation that prevent adequate evaluation of simultaneous EEG recordings, particularly at the stimulation frequency 14 . In addition, conventional TES stimulates broadly, affecting superficial regions with diminishing efficacy over deeper brain regions when using parameters that do not produce scalp discomfort 17 . An innovative neuromodulatory tool potentially able to overcome these limitations is Transcranial Electrical Stimulation with Temporal Interference (TES-TI) 18 . It uses two or more high frequency alternating current carriers (e.g., 15 kHz and 15 kHz + 1 Hz) to produce “temporal interference,” that is, amplitude modulation at the difference frequency (e.g., 1 Hz) capable of modulating neural activity at a chosen location in an individual’s brain 18 . This approach achieves both depth of penetration and focality 18 . Moreover, TES-TI is steerable, in a way not easily attainable with other non-invasive neuromodulation techniques, by adjusting the current ratio of the two channel pairs 18 . Importantly, high frequency carriers allow for effective stimulation intensities without the participant perceiving any peripheral stimulation that could cause awakening from sleep 19 , 20 . Several studies have now implemented TES-TI with high frequency carriers (up to 20 kHz) in a safe and effective manner in humans−for example, in studies of cognitive tasks 21 - 26 −but none of them have used this method to promote sleep and, more specifically, to enhance SWA. The current study presents data from simultaneously recorded hd-EEG and TES-TI during overnight sleep in a laboratory setting in healthy humans. TES 15kHz -TI 1Hz (15 kHz carrier, 1 Hz difference) targeted left ventromedial prefrontal cortical regions given evidence from source modeling 27 and invasive recordings 28 suggesting that these areas are a “hot spot” for slow wave generation. With hardware filters, simultaneous hd-EEG recording was obtained during TES 15kHz -TI 1Hz delivered during periods of NREM sleep in the first half of the night. The results show that TES 15kHz -TI 1Hz can enhance sleep SWA. Results Twenty-eight participants (30.5±9.9 years of age, 61% Female) from the first five cohorts of the STRENGTHEN clinical trial were included in the current study. As described in the Methods, participants were assigned to one of four groups, receiving interventions over the course of four weeks. Twenty-one participants (Groups 2,3,4) received TES 15kHz -TI 1Hz and seven other participants (Group 1) received TES 15kHz . All participants had a baseline overnight with polysomnography (PSG) and hd-EEG recordings. Afterwards, overnight interventions consisted of PSG, hd-EEG, and stimulation. Hardware filters allowed for simultaneous hd-EEG recordings and stimulation ( Figure 1A ). TES 15kHz -TI 1Hz protocols performed during NREM sleep had the following parameters: 15,000 Hz carrier frequency, 1 Hz difference frequency, sinusoidal waveform, 5 mA peak to peak, 3-minute duration with 15-second ramping up/down periods (Figure lB,D). Electric field modeling was used to target the left ventromedial prefrontal cortical region. The montage chosen (channel 1: E34-E20, channel 2: E70-E95) generated a theoretical electric field that exceeded 0.7 V/m in the target region when simulated for a representative participant (Figure lC). Across all TES 15 kHz_nrnz participants (N=21), maximal intensity values in left ventromedial prefrontal cortex grey matter were 0.82±0.09 V/m, ranging 0.62-0.99 V/m. Average intensity values were 0.46±0.05 V/m, ranging 0.36-0.52 V/m. Stimulations occurred in three-minute protocols repeated ten times during NREM sleep identified online by sleep technicians ( Figure 1D ). In most participants, stimulations were completed within the first four hours from sleep onset ( Figure 1D ). Stimulation parameters were the same across groups except that TES 15 kHz had a difference of 0 Hz. Download figure Open in new tab Figure 1. Experiment overview. A. The Transcranial Electrical Stimulation with Temporal Interference (TES­ T!) system comprised the Temporal Interference Brain Stimulator for Research (TIBS-R) with components including the Intelligent Current Source (JCS), electrode connector box (ECB), and high-pass filter (HPF). Current was delivered through two pairs of stimulating electrodes (four electrodes total) embedded in the hd-EEG net. All other hd-EEG recording channels (252) passed through a low-pass filter (LPF) before reaching the recording amplifier. The dual HPF and LPF system allowed for simultaneous TES-TI and hd-EEG recording. Trigger (TRIG) outputs from the TIBS-R allowed for identification of when stimulation occurred. B . TES 15kHz -TI 1Hz consisted of one pair of electrodes delivering high frequency alternating current (E 1 : 15,000 Hz) and the other pair delivering high frequency alternating current with an offset (E 2 : 15,0001 Hz) to create amplitude modulation at the difference frequency (1 Hz) deep in the brain. C . Simulated temporal interference electric field in grey matter for an individual participant. Maximum electric field values occurred in deep, left ventromedial prefrontal cortical regions with values exceeding 0.7 V/m. D . (Top) A single stimulation protocol with three-minute periods before (PRE), during (STIM), and after (POST) stimulation. Three channels of example EEG data during ten seconds within each three-minute period shown. (Bottom) Representative example of the set of stimulations during NREM sleep, with ten stimulations occurring within the first hours after sleep onset. Baseline, first, and last intervention nights were included in the current study. In total, twenty-two first nights (17 TES 15kHz -TI 1Hz , 5 TES 15kHz ) and twenty-seven last nights (20 TES 15kHz -TI 1Hz , 7 TES 15kHz ) were used in analyses that evaluated effects during stimulation (see Methods for details). One additional first night and one additional last night for a TES 15kHz -TI 1Hz participant were included in analyses that evaluated the effects before or after stimulation. Analyses comparing baseline, first, and last intervention nights were restricted to only participants with usable data on all nights (16 TES 15kHz -TI 1Hz , 5 TES 15kHz ). Across all participants, the age range was 19–49 years, race was 85.7% White, 14.3% Asian, and ethnicity was 96.4% non-Hispanic, 3.6% Hispanic, with no statistically significant differences in age or sex between TES 15kHz -TI 1Hz and TES 15kHz ( Table 1 ). Sleep characteristics during the baseline night also did not differ between TES 15kHz -TI 1Hz and TES 15kHz , indicating adequate allocation of participants between groups ( Table 2 ). View this table: View inline View popup Download powerpoint Table 1. Basic demographics. TES 15kHz -TI 1Hz and TES 15kHz participants showed no statistically significant differences in age (Wilcoxon rank sum test) or sex (chi-squared test). Mean ± standard deviation. View this table: View inline View popup Download powerpoint Table 2. Baseline sleep characteristics. TES 15kHz -TI 1Hz (N=21) and TES 15kHz (N=7) participants showed no statistically significant differences in any sleep characteristics during the baseline night (Wilcoxon rank sum test). Mean ± standard deviation. TES 15kHz -TI 1Hz preserves sleep architecture Participants did not report feeling the stimulation during the night. In addition, we compared sleep architecture metrics across baseline, first, and last intervention nights for those TES 15kHz -TI 1Hz participants with usable data on all nights ( Table 3 , TES 15kHz -TI 1Hz N=16). There was a weak main effect of night on Total Sleep Time (X 2 =6.12, p=0.047) when assessed with a non-parametric, omnibus Friedman test; however, individual pairwise comparisons were not statistically significant with correction for multiple comparisons using the Benjamini-Yekutieli procedure (Baseline v. First: W=35.5, p=0.093; First v. Last: W=82.5, p=0.453; Baseline v. Last: W=36.0, p=0.098, Wilcoxon signed rank test). There were no differences in any other sleep characteristics when assessed with Friedmans tests or individual pairwise comparisons with correction for multiple comparisons. In TES 15kHz participants ( Supplementary Table 1 , TES 15kHz N=5), the Friedman test showed an overall effect of night on Sleep Onset Latency (X 2 =8.40, p=0.015); however, individual pairwise comparisons were not statistically significant with correction for multiple comparisons using the Benjamini-Yekutieli procedure (Baseline v. First: W=13.0, p=0.188; First v. Last: W=15.0, p=0.063; Baseline v. Last: W=15.0, p=0.063, Wilcoxon signed rank test). There were no differences in any other sleep characteristics. SWA (power spectral density, PSD in 0.5–4 Hz range) was quantified throughout each entire night then averaged across channels for each participant. There was no main effect of night (baseline, first, or last) on whole-night SWA in either the TES 15kHz -TI 1Hz or TES 15kHz group (TES 15kHz -TI 1Hz : N=16, df=2, F=1.14, p=0.334; TES 15kHz : N=5, df=2, F=3.65, p=0.075, one-way repeated measures ANOVAs). Similar results were found when SWA was measured during the first four hours after sleep onset; this early SWA did not differ between baseline, first, or last nights in either group (TES 15kHz -TI 1Hz : N=16, df=2, F=0.78, p=0.467; TES 15kHz : N=5, df=2, F=3.07, p=0.103, one-way repeated measures ANOVAs). Additionally, the number of stimulation protocols completed during NREM sleep did not differ between first and last nights for either group (TES 15kHz -TI 1Hz : N=16, First=10±2, Last=9±1, W=29.0, p=0.910; TES 15kHz : N=5, First=9±1, Last=8±2, W=13.0, p=0.250, Wilcoxon signed rank test). View this table: View inline View popup Download powerpoint Table 3. Sleep characteristics across nights in TES 15kHz -TI 1Hz . Values are Mean ± standard deviation. SWA increases during and after TES 15kHz -TI 1Hz We measured SWA in the immediate three-minutes before (PRE), during (STIM), and after (POST) TES 15kHz -TI 1Hz stimulation and calculated the differences between time periods ( Figure 2A ). SWA was normalized (%SWA) to account for SWA differences between participants and night-to-night variability within participants (see Methods, hd-EEG Spectral Power Analyses). Since SWA changes were consistent on the first and last intervention nights ( Supplementary Figure 1 ), we averaged Δ%SWA across the two nights for each participant. We also included participants (N=5) with only one intervention night of usable data to increase statistical power. Download figure Open in new tab Figure 2. TES 15kHz -TI 1Hz effects on SWA. A . Across all channels, normalized SWA (%SWA) increased during and after TES 15kHz -TI 1Hz compared to before. %SWA did not change after stimulation compared to during stimulation. Two-tailed paired samples t-tests used to evaluate. Median shown as solid red line. B . Increases in %SWA during and after stimulation were global. No channels had statistically significant changes in %SWA after stimulation compared to during stimulation. Topography of T-statistic shown; channels with statistically significant changes are indicated by white dots. Approximate locations of stimulating electrodes are shown in blue (E 1 ) and yellow (E 2 ). See Supplementary Table 3 for details on figure statistics. Overall, we found a significant difference in the average SWA across PRE, STIM, and POST periods (N=21, df=2, F=30.97, p=7.48×10 -9 , one-way repeated measures ANOVA). We found a global (across all channels) increase in SWA during the stimulation as compared to before stimulation ( Figure 2A , STIM-PRE, N=21, t=6.64, p=1.81×10 -6 , d=1.45; two-tailed paired samples t-test). A global increase in SWA was also present after stimulation compared to before stimulation ( Figure 2A , POST-PRE, N=21, t=7.47, p=3.34×10 -7 , d=1.63; two-tailed paired samples t-test). There were no significant differences between the periods after (POST) stimulation and those during (STIM) stimulation in the average across all channels ( Figure 2A , POST-STIM, N=21, t=0.63, p=0.536, d=0.14, two-tailed paired samples t-test). To identify statistically significant topographic changes at the level of individual channels, we implemented a nonparametric cluster-based statistical approach using a suprathreshold cluster analysis to control for multiple comparisons. The results of this topographical analysis confirmed the results at the global level, i.e. SWA increased across all channels during stimulation and the increase persisted to a similar degree after, with no significant differences between the STIM and POST periods, despite a higher T-statistic in frontal regions in the POST period ( Figure 2B , STIM-PRE: 1 positive cluster, 185 significant electrodes, p̄=1.93×10 -5 ; POST-PRE: 1 positive cluster, 185 significant electrodes, p̄=6.87×10 -6 ; POST-STIM: 0 clusters, 0 significant electrodes, p̄=0.537). TES 15kHz -TI 1Hz effects on SWA differ from those of TES 15kHz We then performed a preliminary comparison of the effects on SWA of TES with amplitude modulation (TES 15kHz -TI 1Hz , 1 Hz difference) and without (TES 15kHz , 0 Hz difference) ( Figure 3A ). To do so, we directly compared normalized SWA (%SWA) in the STIM period between TES 15kHz -TI 1Hz and TES 15kHz . First, we used nonparametric cluster-based statistics to identify channels with significantly different SWA during stimulation between TES 15kHz -TI 1Hz and TES 15kHz (1 positive cluster, 107 significant electrodes, p̄=0.028), and then we calculated the average SWA across these channels. We found that, during STIM, SWA in TES 15kHz -TI 1Hz was greater than SWA in TES 15kHz ( Figure 3B , TES 15kHz -TI 1Hz N=21, TES 15kHz N=7, t=2.48, p=0.020, d=1.08, We also tested whether the effects on SWA changed throughout the STIM period and, if so, whether these changes occurred both with TES 15kHz -TI 1Hz and TES 15kHz . For this analysis, we compared the last 45 seconds (LATE) of the STIM period with the first 45 seconds (EARLY), including the 15-second ramping interval ( Figure 3C ). In the TES 15kHz -TI 1Hz group, SWA was higher in LATE compared to EARLY ( Figure 3D , N=21, t=3.25, p=4.02×10 -3 , d=0.71; two-tailed paired samples t-test). Increases in SWA in LATE were global and strongest in the frontal region ( Figure 3E , 1 positive cluster, 165 significant electrodes, p̄=8.83×10 -3 ). By contrast, in TES 15kHz , SWA did not change between LATE and EARLY ( Figure 3D , N=7, t=1.71, p=0.138, d=0.65; two-tailed paired samples t-test, 3E, 0 clusters, 0 significant electrodes, p̄=0.200). Download figure Open in new tab Figure 3. TES 15kHz -TI 1Hz versus TES 15kHz effects. A . In TES 15kHz -TI 1Hz , one pair of electrodes delivers high frequency alternating current with an offset (15,001 Hz) to create temporal interference (amplitude modulation) at 1 Hz. In TES 15kHz , both pairs of electrodes deliver the same high frequency alternating current, with no offset, i.e. there is no amplitude modulation (difference is 0 Hz). Normalized SWA (%SWA) during the stimulation period (STIM) was directly compared between TES 15kHz -TI 1Hz and TES 15kHz . B . Average %SWA across channels with statistically significant differences between TES 15kHz -TI 1Hz and TES 15kHz (1 positive cluster, 107 significant electrodes, p!=0.028) are shown. %SWA was higher during TES 15kHz -TI 1Hz as compared to TES 15kHz (two-tailed independent samples t-test). Median shown as solid red line. C . Within the three-minute period during stimulation, early and late forty-five second intervals were considered; the difference in %SWA between the late and early stimulation intervals was compared for both TES 15kHz -TI 1Hz and TES 15kHz . D . Averaged across all channels, %SWA was greater in the late interval compared to the early interval in TES 15kHz -TI 1Hz but not in TES 15kHz . Two-tailed paired samples t-tests were used. E . (Left) In TES 15kHz -TI 1Hz , increases in %SWA later in stimulation were global. (Right) In TES 15kHz , no channels had statistically significant changes in %SWA between late and early intervals. Topography of T-statistic shown; channels with statistically significant changes are indicated by white dots. Approximate locations of stimulating electrodes are shown in blue (E 1 ) and yellow (E 2 ). See Supplementary Table 3 for details on figure statistics. TES 15kHz -TI 1Hz effects on other frequency bands Next, we tested whether TES 15kHz -TI 1Hz had additional effects on the EEG power spectrum. PSD values were averaged for each PRE, STIM, and POST period, then across electrodes, then across first and last intervention night, to yield one spectrum per participant (N=21). Spectra were then averaged across participants ( Figure 4 ). For each frequency bin (N=240, 0.167 Hz steps), the STIM-PRE difference, as well as the POST-PRE difference, were calculated. For statistical comparisons, for each canonical band (e.g., theta, alpha), PSD values were averaged across the frequencies in that band for each participant, then STIM-PRE and POST-PRE comparisons were performed using paired samples t-tests with the Benjamini-Yekutieli procedure to correct for multiple comparisons ( Supplementary Table 2 ). For selected significant topographical analyses, as was done with SWA, band power was normalized (% band power) by the average band power during NREM throughout the entire night to give a relative change from the mean overnight band power. Then, the difference between time periods (STIM-PRE, POST-PRE) was evaluated with nonparametric cluster-based statistics. Download figure Open in new tab Figure 4. TES 15kHz -TI 1Hz effects on other frequency bands. A . Differences in power spectral density (PSD) during (STIM) compared to before (PRE) stimulation for individual frequencies (N=240, 0.167 Hz steps). PSD was averaged across channels then across participants (dark line) with the standard error of the mean across participants (light lines) shown. In addition to SWA ( Figure 2 ), increased theta and decreased beta power were found during stimulation. Two-tailed paired samples t-tests were used with correction for multiple comparisons across bands using the Benjamini-Yekutieli procedure. (Top Right, Inset) PSD for individual frequencies before (PRE) and during (STIM) stimulation. B . Changes in normalized theta and normalized beta power were global. Topography of T-statistic shown; channels with statistically significant changes are indicated by white dots. Approximate locations of stimulating electrodes are shown in blue (E 1 ) and yellow (E 2 ). C . Differences in PSD after (POST) compared to before (PRE) stimulation. In addition to SWA ( Figure 2 ), increased theta and decreased sigma power were found after stimulation. (Top Right, Inset) PSD for individual frequencies before (PRE) and after (POST) stimulation. D . Changes in normalized theta and normalized sigma power were global. See Supplementary Table 3 for details on figure statistics. We found that, as compared to before, during stimulation, TES 15kHz -TI 1Hz increased power in the theta (4–8 Hz) range and decreased power in the beta (16–25 Hz) range ( Figure 4A , STIM-PRE, N=21, theta: t=4.29, p=3.58×10 -4 , d=0.94; beta: t= -3.29, p=3.68×10 -3 , d= -0.72; two-tailed paired samples t-tests). Both changes were global ( Figure 4B , theta: 1 positive cluster, 185 significant electrodes, p̄=9.07×10 -4 ; beta: 1 negative cluster, 173 significant electrodes, p̄=7.59×10 -3 ). As compared to before stimulation, after TES 15kHz -TI 1Hz , we found an increase in power in the theta range and a decrease in the sigma (12–16 Hz) range ( Figure 4C , POST-PRE, N=21, theta: t=3.90, p=8.92×10 -4 , d=0.85; sigma: t= -3.95, p=7.95×10 -4 , d= -0.86; two-tailed paired samples t-tests). All these changes were also global ( Figure 4D , theta: 1 positive cluster, 185 significant electrodes, p̄=1.46×10 -3 ; sigma: 1 negative cluster, 185 significant electrodes, p̄=4.47×10 -3 ). When the analysis was repeated for TES 15kHz , there were no differences in any frequency band for either the STIM-PRE or POST-PRE comparisons (N=7, STIM-PRE: SWA: t=1.34, p=0.229; theta: t=0.98, p=0.366; alpha: t= -0.75, p=0.482; low sigma: t= -0.98, p=0.364; high sigma: t= -1.42, p=0.205; beta: t= -0.46, p=0.660; gamma: t= -0.97, p=0.367; POST-PRE: SWA: t=1.23, p=0.264; theta: t=1.98, p=0.095; alpha: t= -0.74, p=0.487; low sigma: t= -0.93, p=0.387; high sigma: t= -2.67, p=0.037 (adjusted p=1.676); beta: t= -0.79, p=0.459; gamma: t= -1.55, p=0.172). Changes in SWA during TES 15kHz -TI 1Hz are associated with improved sleep quality ratings following the TES 15kHz -TI 1Hz intervention Finally, we examined potential relationships between stimulation-related changes in normalized SWA (%SWA) and participants’ perceptions of how they slept. For each participant in the TES 15kHz -TI 1Hz group, we compared the difference in SWA during (STIM-PRE) and after (POST-PRE) TES 15kHz -TI 1Hz with subjective sleep quality ratings from the Restorative Sleep Questionnaire (REST-Q) 29 , 30 , assessed the morning after each intervention night. In line with Robbins and colleagues 29 , an overall REST-Q score was calculated for each participant and each intervention night. When evaluating first and last night separately, using Spearman’s correlations (r s ), we found no correlations between sleep quality ratings and SWA during or after TES 15kHz -TI 1Hz on either night (STIM-PRE, N=16; First, r s =0.038, p=0.888; Last, r s =0.159, p=0.555; POST-PRE, N=16; First, r s =0.176, p=0.515; Last, r s = -0.218, p=0.416). We then considered how SWA and sleep quality ratings might change over the course of the intervention, from first to last night, in the context of inter-individual variability. We found that a greater increase in SWA during TES 15kHz -TI 1Hz (STIM-PRE) on the last night compared to the first night was associated with higher overall REST-Q scores on the last night compared to the first night ( Figure 5A , Left, N=16, r s =0.563, p=0.023). This association was strongest for channels over frontal regions ( Figure 5A , Right, 2 positive clusters, 73 significant electrodes, p̄=0.016). To examine the specificity of the association found for SWA, we also tested the relationship between the change in normalized sigma power and the change in overall REST-Q scores from first to last intervention night, and found no significant association for either low or high sigma power ( Figure 5B , Low Sigma: 9–12 Hz, N=16, r s =0.152, p=0.573; Right: 0 clusters, 0 significant electrodes, p̄=0.549; Figure 5C , High Sigma: 12–16 Hz, N=16, r s = -0.229, p=0.393; Right: 0 clusters, 0 significant electrodes, p̄=0.597). Download figure Open in new tab Figure 5. Relationship between changes in SWA during TES 15kHz -TI 1Hz and changes in sleep quality following TES 15kHz -TI 1Hz intervention. Greater increases in normalized SWA (%SWA), averaged across all channels, were related to improved sleep quality in TES 15kHz -TI 1Hz . A . (Left) Difference in %SWA during stimulation (STIM-PRE, y-axis) was positively correlated with the difference in overall REST-Q scores on the last intervention night as compared to the first intervention night (x-axis). (Right) Strongest correlations were present in frontal areas. Topography of Spearman’s correlation coefficients are shown; channels with statistically significant changes are indicated by white dots. Approximate locations of stimulating electrodes are shown in blue (E 1 ) and yellow (E 2 ). B . (Left) Difference in normalized 9–12 Hz (low sigma) power during stimulation (STIM-PRE, y-axis) was not correlated with the difference in overall REST-Q scores on the last intervention night as compared to the first intervention night (x-axis). (Right) No channels had statistically significant correlations. C . (Left) Difference in normalized 12–16 Hz (high sigma) power during stimulation (STIM-PRE, y-axis) was not correlated with the difference in overall REST-Q scores on the last intervention night as compared to the first intervention night (x-axis). (Right) No channels had statistically significant correlations. See Supplementary Table 3 for details on figure statistics. Discussion We performed simultaneous hd-EEG recording and Transcranial Electrical Stimulation with Temporal Interference (TES-TI, 15 kHz, 1 Hz difference) during overnight sleep in a laboratory setting in healthy humans with the goal of enhancing SWA during NREM sleep. The results show that TES 15kHz -TI 1Hz targeting left ventromedial prefrontal cortical regions increased SWA during the stimulation period, and the effect outlasted the stimulation. TES 15kHz -TI 1Hz during the stimulation period also increased SWA compared to TES 15kHz without TI (15 kHz, 0 Hz difference), indicating that the effect is specifically attributable to TI. Moreover, increases in SWA were greater in the late part of the stimulation interval with TES 15kHz -TI 1Hz stimulation but not with TES 15kHz . Effects on spectral power in other frequency bands were also found, pointing to an overall increase in low frequencies (SWA and theta range) and a decrease in higher frequencies (sigma and beta range) during and/or after stimulation. Lastly, participants who showed the largest incremental effects on SWA between the first and the last intervention night of the 4-week protocol also showed the largest improvement in restorative sleep ratings. Together, these findings illustrate the ability of TES 15kHz -TI 1Hz to enhance SWA and, more generally, the potential for TES-TI as an intervention to improve sleep. TES-TI offers several potential advantages compared to other techniques for neuromodulation during sleep. While acoustic stimulation increases SWA 31 , 32 , the closed-loop titration of the intensity of stimuli to effectively trigger slow waves without awakening subjects can be problematic. This is likely because the triggering of slow waves is mediated by the non-lemniscal auditory pathway, which is associated with the ascending reticular activating system (ARAS) in the brainstem 10 . By directly modulating slow wave generation within their primary cortical origination sites, TES-TI circumvents unintended ARAS activation. Indeed, when comparing baseline, first and last intervention nights, we found that TES 15kHz -TI 1Hz did not affect the general sleep architecture and, more specifically, did not increase the number of awakenings and the time spent awake after sleep onset ( Table 3 ). Notably, the percent of time spent in slow wave sleep (Stage N3) and SWA (both throughout the entire night and in the first four hours of sleep) did not change across baseline, first, and last intervention nights for TES-TI participants. This is in line with several studies using an ON/OFF block design to enhance SWA with other methods 14 . It is possible that more numerous stimulation protocols and/or continuous stimulation might expand the increases in SWA observed in the immediate 3-minutes during and after TES-TI to a larger timescale, over several hours. Also consistent with the preservation of sleep architecture was that the power spectra before, during, and after stimulation all maintained a profile consistent with NREM sleep, including a large, broad peak in the low frequencies corresponding to SWA and a smaller peak in the higher frequencies corresponding to sigma power. Conventional TES, which typically employs frequencies below 1 kHz, has also been used to increase sleep SWA 12,33,34 . However, conventional TES is not well suited for reaching deep brain regions and to do so focally, it produces scalp discomfort and potential awakenings at relatively low stimulation intensities (e.g., 1.5 mA). Also, electrical artifacts prevent the analysis of concurrent EEG recordings 14 . By contrast, TES 15kHz -TI 1Hz as performed in this study, can reach deep brain regions with relatively high focality and field intensity, as indicated by electric field modeling for individual subjects. Even at the relatively high intensities employed in this study (5 mA), TES 15kHz -TI 1Hz did not produce any scalp sensation and did not awaken subjects. In addition, given the high carrier frequency (15 kHz, much higher than the EEG signal) and the use of dedicated hardware filters, it was possible to conduct simultaneous TES 15kHz -TI 1Hz and hd-EEG recordings avoiding stimulation artifacts (there were no narrow peaks at 1Hz or its harmonics superimposed on the smooth power spectra typical of NREM sleep). TES 15kHz -TI 1Hz was intentionally targeted to a deep brain region—left ventromedial prefrontal cortical regions that we had previously identified as a “hot spot” for the origination of slow waves through source modeling of all-night hd-EEG recordings 27 . Because slow waves travel across the cortical surface 35 we nevertheless suspected that the effects of targeted neuromodulation might extend to broader cortical areas. In fact, the increases in SWA during and after TES 15kHz -TI 1Hz were global, extending across all channels. An interesting aspect of the present results is that the increase in SWA produced by TES 15kHz -TI 1Hz was incremental—increasing from the beginning to the end of the 3-minute stimulation period. It also outlasted the stimulation, extending to the subsequent 3 minutes. Because of the experimental design, we could not properly assess how long the enhancement may have lasted. Nevertheless, these findings suggest that TES 15kHz -TI 1Hz at 1 Hz may not simply boost, synchronize, or entrain neuronal slow oscillations, but may produce plastic changes that underlie a cumulative effect. These findings clearly warrant further investigation of stimulation protocols that may maximize the effectiveness of SWA enhancement by lengthening stimulation periods, increasing the number of stimulation periods, and/or repeating them over subsequent nights. An important part of this study was the direct comparison of TES 15kHz -TI 1Hz with TES 15kHz . A separate group of participants (N=7) received high frequency stimulation (15 kHz) with the same intensity and duration as the TES 15kHz -TI 1Hz group, but with 0 Hz frequency difference between the two stimulation channels (TES 15kHz ). High frequency TES is often assumed to produce minimal effects on the brain because of dampening of the field intensity by the skull, resulting in subthreshold intensities, but this assumption has not yet been tested fully 22 , 36 . The present results showed that TES 15kHz -TI 1Hz , compared to TES 15kHz , enhanced SWA during the stimulation period. Moreover, SWA was higher in the last 45 seconds of stimulation compared to the first 45 seconds during TES 15kHz -TI 1Hz and no different during TES 15kHz . Although there were fewer TES 15kHz participants, these initial findings support the notion that the focal amplitude modulation at 1 Hz obtained through TES 15kHz -TI 1Hz has specific effects on brain activity that are not observed by mere high frequency TES 15kHz , consistent with data from intracranial recordings 22 . Besides enhancing sleep SWA, the TES 15kHz -TI 1Hz intervention employed here produced changes in the sleep power spectrum that were consistent with an overall deepening of sleep. In addition to increased power in the SWA (delta) band, we observed a global increase in theta power during and after TES 15kHz -TI 1Hz stimulation. Sleep onset is typically accompanied by an increase in both delta and theta power 37 , 38 and recovery sleep is characterized by increases in low frequency power that extend into the theta band 39 . Similarly, the global decrease in beta power concomitant with increased SWA during TES 15kHz -TI 1Hz was consistent with the inverse relationship between beta and delta power and the higher delta:beta ratio associated with reduced arousal levels 38 , 40 . The decrease in power in the sigma range (12–16 Hz) after TES 15kHz -TI 1Hz (and a trend during TES 15kHz -TI 1Hz ) may also reflect a deepening of sleep. In general, a reduction in spindles is seen with the transition to slow wave sleep (Stage N3), in recovery sleep after total sleep deprivation 39 and, in particular, fast spindle power has dynamics that run in the opposite direction of those of SWA 41 , 42 . A promising indication of this study was the improvement in the restorative quality of sleep when comparing the first and last intervention nights of TES 15kHz -TI 1Hz , as reported by subjects through REST-Q scores. Specifically, participants with greater increases in SWA during TES 15kHz -TI 1Hz from the first to the last night also reported better overall restorative sleep ratings. This relationship was specific to sleep SWA, as it did not extend to the sigma range. The ability of TES 15kHz -TI 1Hz to produce effects on neuronal activity during sleep that outlast the stimulation and may be associated with plastic changes, as identified here, will be further investigated in the STRENGTHEN study by assessing long-lasting modifications in brain anatomical and functional connectivity, as well as in behavioral indicators of cognitive flexibility and emotional regulation 43 , 44 . This first study of TES-TI during sleep had several limitations. We targeted left ventromedial prefrontal cortex using a standardized montage for TES 15kHz -TI 1Hz . Future work may determine whether more precise, individualized TES-TI targeting may offer superior efficacy 45 . Our initial analyses focused on the effects of TES 15kHz -TI 1Hz during NREM sleep on first and last intervention nights. Future work within the STRENGHTEN framework will evaluate additional intervention nights as well as the effects of TES 15kHz -TI 6Hz during REM sleep. Further enrollment will increase the number of subjects receiving TES 15kHz and possibly add a within-participant component. On the other hand, few clinical trials of TES-TI have provided a direct comparison with TES 22 , as was done here. SWA varies with age, and the age range in this study is relatively wide (19–49 years). However, we assessed the effect of TES 15kHz -TI 1Hz with an intra-subject design, by comparing adjacent 3-minute periods and normalizing power to the entire night 38 , 46 - 48 . While fluctuations of SWA during NREM episodes can present a challenge, the use of multiple stimulation blocks (repeated ten times per night) primarily throughout the first four hours of NREM sleep was intended to avoid spurious effects associated with infra-slow oscillations or the natural deepening of sleep. Future studies will aim at identifying the optimal parameters for TES-TI stimulation, including duration and timing, informed by increasing knowledge about its mechanism of action 18 , 25 , 49 . Moreover, the ability of TES-TI to modulate other features of sleep, from sleep spindles to sawtooth waves to sharp-wave ripples, are also ready for exploration, with potential applications in health and disease. Methods Study Design Data used in this study were acquired as part of the STRENGTHEN clinical trial ( NCT06267521 ), a single-blind, longitudinal study aimed at enhancing cognitive flexibility and emotion regulation. The study design includes Transcranial Electrical Stimulation with Temporal Interference (TES-TI) delivered during sleep in the lab as well as brief, daily meditation training. Healthy participants (ages 18–50) were assigned to 1 of 4 groups: (Group 1) TES 15kHz (0 Hz difference frequency) 2 night per week and meditation; (Group 2) TES 15kHz -TI 1Hz (1 Hz difference frequency) 2 nights per week and meditation; (Group 3) TES 15kHz -TI 1Hz 1 night per week and meditation; (Group 4) TES 15kHz -TI 1Hz 2 nights per week and sham meditation. For all participants, interventions during sleep in the lab occurred over four consecutive weeks. Participants were assigned to their treatment group following baseline assessment, with efforts made to ensure group equivalence on key demographic variables, particularly age and sex. This matching approach mitigated potential confounds related to sex and aged-based differences in sleep. Participants were blind to experimental group, i.e. they did not know whether they were receiving TES 15kHz (Group 1) or TES 15kHz -TI 1Hz (Groups 2,3,4). Participants were reimbursed for their study participation. All aspects of the study were approved by University of Wisconsin-Madison Institutional Review Board. The current study reflects a mechanistic sub-study within the broader STRENGTHEN trial; it focuses on the effects of TES 15kHz -TI 1Hz during NREM sleep using data collected from the first five cohorts (out of nine) of the STRENGTHEN trial. The analyses presented here were not pre-specified in the formal clinical trial registration; however, they were part of the planned exploratory aims within the Institutional Review Board-approved protocol and were designed prior to data collection from the analyzed cohorts. Primary and secondary endpoints of the trial related to cognitive flexibility and emotion regulation will be the focus of later work. The analyses performed here were initiated after interim inspection of data from the first two cohorts showed large effect sizes. Specifically, analyses, as in Figure 2 , performed after the first two cohorts (N=10 TES 15kHz -TI 1Hz ) indicated effect sizes ranging 0.947–1.146 (Cohen’s d) suggesting 5–9 subjects would be sufficient to obtain statistical power of 0.8 (calculated using MATLAB sampsizepwr for a one-tailed paired samples t-test). To maximize statistical power, the current study used data from the first five cohorts to examine the effects of TES 15kHz -TI 1Hz on NREM sleep (N=21, 1 Hz, targeting left ventromedial prefrontal cortex during NREM sleep episodes in the first half of the night) by comparing the baseline night with the first and last intervention nights regardless of the group. A preliminary comparison between TES 15kHz -TI 1Hz and TES 15kHz (i.e., directly assessing the potential effects of amplitude modulation) was also performed utilizing Group 1 participants from the first five cohorts (N=7). Another goal of the STRENGTHEN clinical trial, to be reported in a subsequent publication, was the assessment of TES 15kHz -TI 6Hz (6 Hz, targeting retrosplenial cortex) during REM sleep episodes later in the night. Participants In total, twenty-eight participants (30.5±9.9 years, 61.0% Female) from the first five cohorts of the STRENGTHEN study were included in the current study. All participants were medically healthy and free of sleep disorders. Participants had a baseline magnetic resonance imaging (MRI) scan and sleep night in the lab. The baseline sleep night did not include overnight stimulation. Participants were excluded if they had any neuroradiologist-identified structural abnormalities of the brain. Participant recruitment began with an online survey. Potential participants were then further screened by phone call to see if they met eligibility criteria. They then underwent additional screening and an informed consent discussion in person prior to enrollment in the study. All participants gave written informed consent in accordance with the University of Wisconsin-Madison Institutional Review Board. Datasets Included Baseline, first, and last intervention nights were utilized here; a future publication will report on all the intervention nights. In total, twenty-two first nights and twenty-seven last nights were included in subsequent analyses that evaluated effects during stimulation. Three first nights were excluded due to technical issues and two first nights were excluded due to poor sleep that precluded experiment completion (e.g., fewer than five out of ten intended stimulation protocols were completed). For two additional nights (one first and one last), hd-EEG recordings during the stimulation were excluded due to technical issues. However, these two nights were included in subsequent analyses that evaluated effects before or after stimulation. Analyses comparing baseline, first, and/or last intervention nights were restricted to participants with usable data across all nights. Procedure Participants spent overnights in the sleep lab at the Wisconsin Sleep Center, where they were outfitted with a 256-channel hd-EEG net for recording brain activity and additional channels for recording polysomnography (PSG) data (electrooculography adjacent to lateral canthi, submental electromyography, and electrocardiography). Channels for performing TES-TI were embedded in the hd-EEG net, placed in lieu of recording channels. While the participant was awake, short stimulation protocols (15-seconds with 15-second ramping up/down periods) were performed with increasing current amplitudes (up to 5 mA peak to peak) to determine if a participant began to feel sensation on their scalp. The current amplitude was then lowered systematically and checked again such that the final current value of 5 mA chosen did not elicit any sensation. Then, the participant went to sleep, and a sleep technician performed stimulation protocols based on sleep features visible on the hd-EEG and PSG recordings. Specifically, when NREM sleep was identified and stable (≥3 minutes), stimulation was delivered in 3-minute protocols with 15-second ramping up/down periods. Each protocol was separated by at least 6 minutes and repeated approximately ten times, in most cases confined to NREM sleep episodes during the first four hours after sleep onset. Participants were monitored throughout the night from a control room and a sleep technician was available for communication at any time. Sleep Quality Surveys After each overnight in the sleep lab, participants completed the 9-question Restorative Sleep Questionnaire (REST-Q) survey 29 , 30 . They rated to what extent they felt tired, sleepy, in a good mood, rested, refreshed, ready to start the day, energetic, mentally alert, and grouchy on scales of 1 to 5 (1=Not at all, 2=A little bit, 3=Some, 4=Very much, 5=Completely). An overall REST-Q score was calculated in line with 29 . Overall scores from after the first and last intervention nights were included in the analyses. Potential associations between scores and changes in SWA on either the first or last intervention night were evaluated using Spearman’s correlations. Then, associations between the change in scores and the change in band power from first to last intervention night were also evaluated using Spearman’s correlations. Specifically, the difference between overall REST-Q scores on the last intervention night compared to the first intervention night was computed. The difference between SWA (0.5–4 Hz) and sigma (low 9–12 Hz, high 12–16 Hz) band power on the last intervention night compared to the first was computed. Potential relationships between these differences were then assessed with Spearman’s correlations. TES-TI Targeting A standardized channel montage was used for all participants (channel 1: E20–E34, channel 2: E70–E95 of the hd-EEG net) targeting left ventromedial prefrontal cortical regions. Channel placement was first approximated using the Temporal Interference Planning Tool (TIP v1.0, IT’IS Foundation, Switzerland), in which an optimal channel configuration was chosen from the 10:10 system to target the left ventromedial prefrontal cortex with maximal intensity and minimal off-target stimulation 50 , 51 . TIP simulations were performed using the comprehensive MIDA head model, which details the electrical properties for different tissues in a twenty-nine-year-old healthy female volunteer 52 . For further confirmation, targeting was then verified using SimNIBS software (v4.1.0) that provided tools for electric field modeling with additional details including: an individual’s MRI, and channel material, geometry, and position in the 256 hd-EEG system 53 , 54 . Modeling began with segmentation of an individual participant’s T1 and T2 scans to extract electrical properties of different tissues. Segmentation and meshing were performed using the charm function. The participant’s head model was then aligned to a template 256-channel hd-EEG net. Next, electric fields were simulated accounting for channel properties (see TES-TI Stimulation ), a current value of 5 mA peak to peak, and location in the 256-channel hd-EEG net extrapolated from the 10:10 system locations used in TIP simulations. Simulation of the temporal interference field (V/m) was implemented using the function get_maxTI that calculates the maximal modulation amplitude of the difference frequency using the equation described in 18 . The field values obtained in grey matter were then extracted and plotted over the individual’s T1 scan as well as over a standardized atlas (MNI152) for visualization using SimNIBS and AFNI (Analysis of Functional NeuroImages) software 55 , 56 . Overall, simulations performed in SimNIBS with individualized head models agreed with those performed in TIP using the standardized MIDA head model. Based on these simulations using individualized head models, across all TES 15kHz -TI 1Hz participants (N=21), maximal intensity values in left ventromedial prefrontal cortex grey matter were 0.82±0.09 V/m, ranging 0.62–0.99 V/m and average intensity values were 0.46±0.05 V/m, ranging 0.36–0.52 V/m. Of note, the channel configuration used for two TES 15kHz -TI 1Hz participants differed by one channel (E94 instead of E95); however, their data was included given that electric field simulations for both configurations were highly similar. TES-TI Four silver-silver chloride stimulating channels (12.6 mm outer diameter, 8.0 mm inner diameter, 2.2 mm width) with 1.5-meter cable length were embedded into the hd-EEG net, attached to the participant’s scalp with conductive paste (Ten20), and then connected to the TI Brain Stimulator for Research (TIBS-R v3.0, TI Solutions AG, Switzerland). Electrode impedances were monitored throughout the experiment and served as an indicator for the need to adjust electrodes; substantial increases in impedance indicated poor positioning and a need for adjustment. Average impedances were 4590±110 Ohm for channel 1 and 5020±600 Ohm for channel 2. TES-TI protocols during NREM sleep had the following parameters: 15,000 Hz carrier frequency, 1 Hz difference frequency, sinusoidal waveform, 5 mA peak to peak, 3-minute duration with 15-second ramping up/down periods. Channel 1 used a 15,000 Hz frequency and channel 2 used a 15,001 Hz frequency. Actual, average current delivered was 5.21±0.21 mA for channel 1 and 5.06±0.19 mA for channel 2 as reported by the TIBS-R device. Programming of the TIBS-R device was performed using its Python software. hd-EEG Data Acquisition The hd-EEG system from MagStim Electrical Geodesics, Inc. (EGI) consisted of a hd-EEG net, an amplifier, and a physiological input box. Each net had 256 silver-silver chloride electrodes and was appropriately sized to each individual participant. In cases where the stimulating electrodes were very close to the hd-EEG recording electrodes, e.g., small net sizes, surrounding recording electrodes were not gelled to avoid the possibility of bridging between stimulating and recording electrodes. Nets were then connected to EGI amplifiers that interface with EGI’s Net Station Acquisition software (v5.4.2) for real-time data visualization. The physiological input box was used to collect polysomnography data (electrooculography, electromyography, and electrocardiography). In addition, data from the TIBS-R trigger was input into the physiological input box. The optical trigger box output a 5 V square wave signal with the onset and offset of stimulation and, when synchronized to hd-EEG recordings, allowed for offline identification of stimulation intervals. All data were collected at a 500 Hz sampling rate. Hardware filters specifically designed for the TIBS-R device allowed for simultaneous TES-TI and hd-EEG recording. A low-pass filter (7th order, 100 Hz cutoff) was placed in the path of the hd-EEG amplifier input such that signals from the hd-EEG net were filtered before they reached the amplifier. A high-pass filter (4th order, 250 Hz cutoff) was placed in the path of the TIBS-R output such that intermodulation products and low-frequency noise generated by the TIBS-R were filtered before they reached the stimulating electrodes. Both hardware filters were used to allow for simultaneous TES-TI stimulation and hd-EEG recording. MRI Data Acquisition MRI data were collected using a 3 Tesla MAGNUS (Microstructure Anatomy Gradient for Neuroimaging with Ultrafast Scanning, GE Healthcare) head-only MRI scanner. Structural images were acquired using T1- and T2-weighted images, with 0.8 mm isotropic voxels, and the following parameters. T1-weighted: sequence = MP-RAGE (Magnetization-Prepared Rapid Gradient-Echo), repetition time (TR) = 2000 ms, echo time (TE) = 3 ms, inverse time (TI) = 1100 ms, flip angle = 8 degrees, field of view (FOV) = 256 x 256 mm 2 , matrix size = 320 x 320 pixels, resolution = 0.8 mm x 0.8 mm x 0.8 mm, number of slices = 240, acquisition time = 4 min. T2-weighted: sequence = CUBE-T2, TR = 2500 ms, TE = 90 ms, echo train length (ETL) = 120, FOV = 256 x 256 mm 2 , matrix size = 320 x 320 pixels, resolution = 0.8 mm x 0.8 mm x 0.8 mm, number of slices = 240, acquisition time = 4 min. The structural MRI data were converted into NIfTI format using the open-source tool dcm2niix . Both T1- and T2-weighted images were defaced for all participants using the open-source Python-based defacing utility tool, pydeface . Sleep Staging Sleep staging was performed manually in accordance with the American Academy of Sleep Medicine guidelines by a registered polysomnographic technician 57 . Electrooculography, electromyography and 6 hd-EEG channels that approximate 10:20 locations F3, F4, C3, C4, O1, O2 were used for staging. Hd-EEG data were filtered using MATLAB’s filtfilt function with design: 4 th order infinite impulse response filter band-passed 1–30 Hz. Electromyography data were filtered using MATLAB’s filtfilt function with design: 4 th order infinite impulse response filter band-passed 1–100 Hz followed by a notch filter: 4 th order infinite impulse response filter stop band 59-61 Hz. CountingSheepPSG (v1.4.5) MATLAB software was then used to perform staging in 30-second epochs and calculate sleep metrics such as Total Sleep Time, Sleep Onset Latency (to Stage 1), Wake After Sleep Onset, and others. Nonparametric statistical tests were used to evaluate for potential changes in these metrics across participant groups (Wilcoxon rank sum) and within participants, across intervention nights (Friedman test). hd-EEG Preprocessing hd-EEG data preprocessing was performed using MATLAB (v2023b) and EEGLAB (v2025.0.0) 58 . hd-EEG data were band-pass filtered 0.5–40 Hz using EEGLAB’s finite impulse response filter function, pop_eegfiltnew . Only NREM epochs staged 2 or 3 were included in spectral power analyses. hd-EEG Artifact Rejection Semi-automated artifact rejection was performed to remove channels and epochs containing artifactual activity (e.g., high-frequency noise, interrupted contact with the scalp) 59 - 61 . Specifically, a Fast Fourier Transform (FFT) was used to calculate spectral power in low (0.5–4 Hz) and high (20–30 Hz) frequency ranges for 6-second NREM epochs. For each channel, automatic thresholds were calculated at the 99th percentile and then plotted and visually inspected. If epochs with substantially greater low- or high-frequency power did not exceed the automatic threshold, the threshold was lowered. All epochs exceeding the threshold were then removed. Channels in which artifacts affected most of the recording were removed. Additional spectral- and topographic-based procedures were used to identify and remove channels with distinctly greater power relative to neighboring channels. On average, 74% (189±23) of the 256-hd EEG channels remained on intervention nights. A final visual review of the hd-EEG data was performed to remove any remaining channels and epochs with artifactual activity. To increase the signal-to-noise ratio, analyses were restricted to 185 scalp channels (excluding neck and face channels). Any removed channels, including channels that were not gelled, within the scalp 185 channels were interpolated using spherical spline interpolation via the eeg_interp function. All channel data was then re-referenced to the average of the final 185 channels. For visualization only, topographies were plotted using EEGLAB’s topoplot function with a decreased radius (plotrad = 0.51); all 185 channels were included in analyses. hd-EEG Alignment Timing of TES-TI throughout the night was determined based on hardware trigger signals in agreement with timestamps recorded at the start and end of individual stimulation protocols. Time periods included a baseline PRE stimulation period 3-minutes prior to the start of stimulation, a STIM period including a 15-second ramp up, 3-minute stimulation, and 15-second ramp down, and a POST stimulation period 3-minutes after the ramp down. Analyses comparing STIM and PRE or POST intervals excluded the ramping interval to allow for equal 3-minutes comparisons. hd-EEG Spectral Power Analyses For each hd-EEG channel, power spectral density (PSD) was calculated with Welch’s method using 6-second windows with 90% overlap for a 0.167 Hz step size. PSD values were then averaged over frequencies to calculate band power (e.g., SWA, 0.5–4 Hz). Band power was normalized to account for differences between participants and night-to-night variability within participants 48 . For each participant and each night, the band power of interest (e.g., SWA) for each channel in PRE, STIM, and POST periods was divided by the average band power (e.g., SWA) during NREM sleep throughout the entire night to obtain a relative change (shown as a percent) from the mean overnight band power 48 . Canonical frequency bands were defined as follows, SWA: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–12 Hz, low sigma: 9–12 Hz, high sigma: 12–16 Hz, beta: 16–25 Hz, gamma: 25–40 Hz. Statistics In general, data were evaluated for normality using the Shapiro-Wilk test prior to performing any statistical tests. If data distributions were normal, parametric tests were used; if data distributions were not normal, nonparametric tests were used. Tests were two-tailed unless otherwise specified. Assessment of statistical significance in the context of multiple comparisons was performed using the false discovery rate, particularly the Benjamini-Yekutieli procedure. All results are reported as mean ± standard deviation and all reported p-values are unadjusted unless otherwise specified. For topographic comparisons, a nonparametric cluster-based statistical approach using a suprathreshold cluster analysis to control for multiple comparisons was implemented 62 - 64 . For each permutation (N=5000), new datasets were generated with randomly relabeled conditions from the original data. Then, a selected statistical test (e.g., t-test or Spearman correlation) was performed and clusters were determined based on neighboring channels exceeding the critical value for a given two-tailed test distribution, degrees of freedom, and alpha of 0.05. The maximal size of resulting clusters was used to build a cluster size distribution across all permutations. The critical cluster size threshold was defined as the 95 th percentile of this distribution. For the empirical comparison, only channels that exceeded the critical value and were located within a cluster larger than the critical cluster size threshold were considered significant 62 , 63 . The number of significant clusters, total number of significant electrodes, and mean p-value (p̄) of those significant electrodes are reported; channels with significant responses are shown as white dots in figures. Data Availability The data underlying this article will be shared on reasonable request to the corresponding authors. Code Availability Customized codes used to create the main figures and tables have been made publicly available on GitHub, at https://github.com/eschaeffer-0/Schaeffer_et_al_2025 . Author Contributions Conceptualization – G.T., R.J.D., S.G.J., R.I.G., C.C., M.B., E.L.S., I.H.; Data Curation – E.L.S., I.H., Z.F., S.Br., B.M., R.S., S.G.J., L.A., M.B.; Formal Analysis – E.L.S., S.Br., I.H., Z.F., L.N.; Funding Acquisition – G.T., R.J.D., S.G.J., R.I.G.; Investigation – E.L.S., I.H., B.M., T.A., G.V., R.S., L.N.; Methodology – G.T., S.G.J., M.B., E.L.S., I.H., Z.F., S.Br., L.A., F.M., A.W., P.A., S.Be., M.C., E.N., N.K.; Project Administration – S.G.J., E.L.S., B.M., R.S., M.B.; Resources – P.A., S.Be., M.C., E.N., N.K.; Software – E.L.S., I.H., Z.F., S.Br., P.A., S.Be., M.C., E.N., N.K.; Visualization – E.L.S., S.Br., I.H., Z.F.; Writing (Original Draft) – E.L.S., C.C., G.T.; Writing (Review & Editing) – all authors. Competing Interests The authors declare no competing financial or non-financial interests, with the following exceptions: G.T. is Chair of Board and has a financial interest in Intrinsic Powers Inc., N.K. and E.N. are Board Members and have a financial interest in TI Solutions AG, and R.J.D. is the founder, president, and serves on the board of directors for the nonprofit organization, Healthy Minds Innovations, Inc. Acknowledgements We thank Tricia Denman, Emily Peterson, and Kate Riordan for their efforts in participant recruitment, Everett Carstens, Claire Doucet, Paul Ladefoged, Poorang Nori, and Ana Maria Vascan for their efforts in data collection, Cameron Brace and Dillon Scott for their assistance in data processing. This project is funded by the Defense Advanced Research Projects Agency (DARPA) under cooperative agreement No. HR00112320033 (to G.T., R.J.D.). 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