Characterizing Deep Brain Stimulation Dual Device Beat Frequency Artifacts

Characterizing Deep Brain Stimulation Dual Device Beat Frequency Artifacts | 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 Characterizing Deep Brain Stimulation Dual Device Beat Frequency Artifacts View ORCID Profile Nabeel Diab , View ORCID Profile Kara Presbrey , View ORCID Profile Stephanie Cernera , View ORCID Profile Sameer Rajesh , Raphael Bechtold , View ORCID Profile Nisha Giridharan , View ORCID Profile Garrett Banks , View ORCID Profile Eric A. Storch , Doris D. Wang , View ORCID Profile Philip A. Starr , Wayne K. Goodman , View ORCID Profile Jeffrey A. Herron , View ORCID Profile Sameer A. Sheth , View ORCID Profile Nicole R. Provenza doi: https://doi.org/10.1101/2025.10.11.25337803 Nabeel Diab 1 Department of Neurosurgery, Baylor College of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nabeel Diab For correspondence: nabeel.diab{at}bcm.edu Kara Presbrey 2 Department of Neurological Surgery, University of California San Francisco Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kara Presbrey Stephanie Cernera 2 Department of Neurological Surgery, University of California San Francisco Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephanie Cernera Sameer Rajesh 1 Department of Neurosurgery, Baylor College of Medicine 3 Department of Neurological Surgery, University of Texas Southwestern Medical Center Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sameer Rajesh Raphael Bechtold 4 Department of Neurological Surgery, University of Washington Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nisha Giridharan 1 Department of Neurosurgery, Baylor College of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nisha Giridharan Garrett Banks 1 Department of Neurosurgery, Baylor College of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Garrett Banks Eric A. Storch 5 Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eric A. Storch Doris D. Wang 2 Department of Neurological Surgery, University of California San Francisco Find this author on Google Scholar Find this author on PubMed Search for this author on this site Philip A. Starr 2 Department of Neurological Surgery, University of California San Francisco Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philip A. Starr Wayne K. Goodman 5 Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jeffrey A. Herron 4 Department of Neurological Surgery, University of Washington Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jeffrey A. Herron Sameer A. Sheth 1 Department of Neurosurgery, Baylor College of Medicine 5 Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine 6 Department of Electrical and Computer Engineering, Rice University 7 Neuroengineering Initiative, Rice University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sameer A. Sheth Nicole R. Provenza 1 Department of Neurosurgery, Baylor College of Medicine 6 Department of Electrical and Computer Engineering, Rice University 7 Neuroengineering Initiative, Rice University 8 Department of Bioengineering, Rice University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nicole R. Provenza Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Introduction Recording-enabled deep brain stimulation devices are used to treat individuals with a variety of neurological disorders, but current systems are only able to connect to a maximum of two brain leads. Off-label implantation of additional leads for unique clinical research questions requires bilateral implantable pulse generators (IPGs), however, slight mismatches in clock rates across IPGs produce mismatched stimulation frequencies. This mismatch creates high amplitude beat frequency artifacts (BFA) that occur at regular intervals and contaminate underlying neural signals, presenting unique challenges for future adaptive DBS (aDBS) algorithm development utilizing dual IPGs. Method We quantified BFA intervals in local field potential (LFP) recordings from 26 patients implanted with two IPGs during both continuous (cDBS; n=21) and adaptive (aDBS; n=5) stimulation modes. Results BFAs occur in all patients at varying intervals and require both devices to be turned on. We found that BFAs result from mismatched stimulation frequencies, where smaller frequency differences result in longer intervals between BFAs. The range in interval length between artifacts was 112 seconds to 30 minutes across patients. Switching to adaptive DBS (aDBS) decreased this interval to 30 seconds in a single patient. Conclusion In a dual-device scenario, BFAs should be considered in LFP analysis or future aDBS algorithm design through implementation of mitigation strategies such as onset times and selection of minimally affected recording channels. Introduction With the commercial growth of recording-capable deep brain stimulation (DBS) devices, 1 researchers have begun to use on-device neural records to inform therapeutic stimulation delivery. 2 – 10 These neural signals of interest are easily obscured by artifacts like the stimulation itself, patient movement, and electrocardiographic signals. Identifying these sources of artifact is important to then minimize their effect through choices like device placement or electrode programming and data post-processing. 11 – 13 While all current approved aDBS therapies are only for single implantable pulse generators (IPGs), offering patients two IPGs may be therapeutically and scientifically beneficial. 5 , 6 , 14 – 19 First, dual-device implantation doubles the number of leads that can be used for both stimulation and local field potential (LFP) recording. Second, utilizing a second device also doubles the number of recording channels that can be spread across recording and stimulating electrodes. Although adding a second IPG increases the total amount of hardware implanted, this hardware can be distributed bilaterally to maximize patient comfort with all leads connecting to ipsilateral extensions along either side of the neck. 6 Finally, since each IPG only powers half the total stimulation delivered in the brain, battery recharge and replacements do not need to occur as often. 20 As these devices were designed to be used as single IPG systems, use of multiple IPGs can result in sub-optimal interactions. Since each device operates independently of the other, stimulation pulses are delivered at a frequency dictated by an internal clock on each device. These clocks count time based on the oscillation frequency of an embedded quartz crystal. Since no two crystals are identical, no two clocks operate at exactly the same rate. This slight mismatch in internal clocks across IPGs results in a similar mismatch between identically programmed stimulation frequencies across the two IPGs. As in any situation where two oscillators are producing different frequency inputs into a system, a lower frequency “beat” occurs with a frequency equal to the frequency difference between the two oscillators. We thus hypothesize that in patients with two DBS IPGs, the small stimulation frequency differences effects would result in beat frequency artifacts (BFAs) in the neural data stream. In 26 patients implanted with two DBS IPGs, we show that delivering stimulation from two devices produces BFAs in the recorded neural data. These BFAs are high amplitude deviations from biological activity that occur at regular intervals and contaminate recordings of neural activity. Left unaddressed, BFAs hinder LFP analyses and the identification of neural biomarkers needed for optimization of neuromodulation therapy and adaptive DBS (aDBS) strategies. Novel mitigation strategies such as strategic implementation of onset times and careful selection of minimally affected recording channels. Methods Study Participants Twenty-six patients at two institutions (Baylor College of Medicine [BCM] and University of California at San Francisco [UCSF]) were implanted with two Medtronic Summit RC + S devices with one IPG placed in each side of the patient’s chest. The summit RC + S is a non-commercial system produced by Medtronic solely to support clinical research studies as part of the NIH Brain Initiative. The local institutional review board at BCM (H-49155) and UCSF (18-24454 and 20-32847) approved all procedures. At BCM, we implanted five patients with treatment refractory obsessive compulsive disorder (OCD) with bilateral DBS leads in the ventral capsule/ventral striatum (VC/VS) and bilateral cortical surface electrodes in the form of electrocorticography (ECoG) strips over the orbitofrontal cortex (OFC) ( NCT04806516 ). At UCSF, we implanted 21 patients with Parkinson’s Disease (PD) with bilateral DBS leads in either the subthalamic nucleus (STN) or globus pallidus internus (GPi), in addition to ECoG strips over the primary motor cortex (M1) ( NCT03582891 and NCT04675398 ). Electrode and Stimulation Configuration Each Medtronic Summit RC + S electrode contains contacts numbered 0-3. We performed monopolar simulation bilaterally at one of the two middle contacts (contact 1 or 2) in either the VC/VS, STN, or GPi, across all patients. Of the four bipolar recording channels per IPG, two channels recorded activity from the stimulating depth electrode and the other two recorded from the ECoG strips. The two recording channels on the subcortical lead flanked the stimulating contact to reduce stimulation artifact when recording (contact pairs 0-2 when stimulating at contact 1) such that the fourth contact (contact 3) was unused. The two recording channels on each ECoG strip consisted of adjacent pairs (contact pairs 8-9, 10-11). We delivered stimulation in the continuous DBS (cDBS) mode both at home and in clinic except during experimental delivery of aDBS through the device’s onboard “Operative aDBS” mode in the clinic. Neural recordings all passed through an onboard 0.85 Hz high pass filter and 100 Hz low pass filter. Calculating BFAs We define the frequency difference (Δf) across device 1 (f 1 ) and device 2 (f 2 ) with the following calculation: Where the frequency of BFAs is equal to Δf. Therefore, the interval between BFAs is inversely related to Δf in any given system. Hence, higher frequency BFAs have a shorter BFA interval and a greater Δf. Experiment 1 - Frequency Modulation Testing To test the impact of varying stimulation frequency across the two IPGs on observed BFA periodicity in a single patient, we modified the frequency of DBS for one OCD DBS patient (bilateral VC/VS electrodes and OFC ECoG strips) receiving therapeutic stimulation at 5.5 mA, 150.6 Hz, and 120 µs bilaterally. Without changing left hemisphere stimulation parameters, we decreased the right side stimulation frequency to 146.2 Hz while keeping right side stimulation amplitude and pulse width constant, which we expected would result in a BFA with a frequency of 4.4 Hz equivalent to. After 217 seconds, we increased the right side stimulation frequency to 148.8 Hz, which we predicted would result in a BFA with a frequency of 1.8 Hz. We returned the right side stimulation frequency to 150.6 Hz after 300 seconds of total experiment time. Any BFA observed in this configuration would occur due to quartz-crystal variability with a very low (<0.1 Hz) Δf. Experiment 2 - Unilateral DBS OFF Testing To demonstrate that BFAs originate from stimulation interference across devices, we recorded LFPs in both devices while only one was stimulating at a time. Cessation of the artifact would confirm that the observed artifacts are BFAs rather than artifacts from another source. For all DBS off testing (n=3), we turned only one device off at a time. Since stimulation at 0 mA produces a stimulation artifact on recording channels, 8 , 21 we switched the devices to DBS off rather than decreasing stimulation amplitude to 0 mA. In Figure 1B , both devices continued to stimulate at clinical settings before we turned off stimulation in the right device for 160 seconds. We then turned right side stimulation back on to clinical settings for another 230 seconds before turning the left side stimulation off for 150 seconds. We turned left side stimulation back on and maintained clinical stimulation parameters until the experiment ended. Download figure Open in new tab Download figure Open in new tab Figure 1. Beat frequency artifacts occur when devices are set to slightly different stimulation frequencies. (A) DBS frequency and VC/VS LFP over time. Left IPG stimulation is held at 150.6 Hz over the 20 second window. Right IPG stimulation frequency is 146.2 Hz until 217 seconds which produces a 4.4 Hz BFA equivalent to the difference in stimulation frequencies across devices. The BFA frequency drops to 1.8 Hz when right IPG stimulation frequency increases to 148.8 Hz (n=1). (B) DBS amplitude, VC/VS LFP, and OFC LFP over time. Beat frequency artifacts are only present when both devices are actively stimulating. Data from 1 of 3 patients tested with one device off is shown. BFAs contaminate neural signals on both stimulating leads in the ventral capsule/ventral striatum (VC/VS) and cortical electrode recording channels in the orbitofrontal cortex (OFC). Long-term Recordings To evaluate the impact of BFAs on long-term recording and data collection across our entire patient cohort, we collected single LFP recordings exceeding 1000 seconds in length from either in-clinic behavioral tasks or passively at home from each patient across both institutions. We visually screened these recordings for BFAs to compare across patients. Additionally, these recordings contained “cap stack” artifacts which are unique to the Medtronic Summit RC + S device. Cap stack artifacts are a result of the DBS system optimizing the stimulation engine to ensure the stimulation engine is outputting accurate stimulation pulses as device battery level changes over time. These artifacts occur once every 300 seconds and last ~200 ms in duration. Cap stack artifacts are present in the LFP recording of a single device and do not present simultaneously in the LFP recording of another device. Cap stack artifacts were noted in neural recordings as 200 ms fixed, narrow amplitude deviations occurring every 300 seconds in all patients. Experiment 3 - Amplitude Ramping Testing We used repeated step-like increments and decrements in stimulation amplitude, termed “ramping,” to further investigate the behavior of BFAs during IPG on-off behavior. We performed ramping with the DBS device set to the “Operative aDBS” mode to allow for manual control of stimulation amplitude. We completed this test in a single patient and switched from cDBS to aDBS before ramping amplitude down, turning DBS off, and then ramping amplitude back up. Results Beat frequency artifacts are a result of mismatched DBS frequencies across two IPGs To generate a BFA in vivo, we intentionally mismatched DBS frequency temporarily across both IPGs as described in Experiment 1 . We found that BFAs occur at predictable frequencies equal to the Δf between the two stimulating devices ( Fig. 1A ). First, we set the stimulation frequencies for the left and right hemispheres to 150.6 Hz and 146.2 Hz, respectively. At this Δf of 4.4 Hz, we found that BFAs occurred approximately 4.4 times per second. We then changed the stimulation frequency in the right hemisphere to 149.8 Hz, reducing the Δf to 1.8 Hz, and the BFAs occurred less frequently, at a rate of 1.8 times per second. Even when two IPGs are set to the same stimulation frequencies, BFAs arise due to slight mismatches in the clock rate across each device. We found a BFA interval of 112 seconds when stimulating at 150.6 Hz on both IPGs in one patient ( Fig. 1B ). To demonstrate BFAs arise from two simultaneously stimulating devices, we turned off one IPG at a time, as described in Experiment 2 . When stimulation was turned off on either device, the BFA disappeared. Therefore, we concluded that this periodic signal was most likely a BFA. BFAs were present in long-term recordings across all 26 participants Next, we sought to investigate the presence of BFAs in a large, multi-institutional cohort of patients with two recording-capable IPGs. With both IPGs delivering therapeutic stimulation at identically programmed frequencies, we observed BFAs in all patients (n=26) at variable intervals ( Fig. 2 ). The shortest interval between BFAs observed was 112 seconds, and each of these BFAs lasted under 10 seconds. The longest interval between BFAs observed was over 30 minutes with each artifact lasting about 200 seconds. Download figure Open in new tab Figure 2. Longer intervals between beat frequency artifacts lead to longer artifact durations. LFP recordings over time in a subset of 8 patients (P01-P08). Recordings were performed on DBS electrode recording channels except in P02 where a cortical channel was used. Patients are arranged by ascending BFA interval lengths from top to bottom and all recordings are during cDBS with equivalent stimulating frequencies. All BFAs are highlighted in orange except for callouts showing BFA examples in P01, P05, and P08 in red, blue, and green, respectively. Cap stack artifacts appear at 300 second intervals as sharp, narrow peaks in the LFP recordings of P03, and P05-P08 above. Adaptive DBS reduces the BFA interval In a single patient, we investigated the impact of aDBS on BFA frequency and duration as described in Experiment 3 ( Fig. S1 ). To recreate the cDBS BFA shown in Figures 1 and 2 , we conducted a baseline recording during which left and right devices were set to an amplitude of 5.5 and 5.7 mA respectively and frequency of 150.6 Hz bilaterally. During this baseline recording, we observed 3 BFAs at the expected interval of 112 seconds. After 12 minutes of baseline recording, we switched the right hemisphere device to “Operative aDBS” mode in order to automatically control state changes that guide DBS parameters. We decremented stimulation amplitude to 0 mA at a rate of 1 mA per minute. After reaching 0 mA, we turned DBS off for one minute, to avoid 0 mA artifact, before incrementing back to 6 mA at a rate of 3 mA per minute. While the right IPG was in aDBS mode, BFA intervals across both hemispheres decreased to ~30 seconds during ramping of amplitude. This interval was not impacted by ramp rate. The amplitude of the BFA on the left was dependent on the stimulation amplitude of the right IPG. These higher-frequency BFAs took the place of the lower-frequency BFAs seen during baseline. As expected, BFAs disappeared in both hemispheres when one IPG was turned off. Download figure Open in new tab Figure S1. Decreased intervals between beat frequency artifacts in aDBS mode do not depend on ramp rate. After switching the right IPG from cDBS to aDBS and decrementing stimulation amplitude at 750 seconds, BFA intervals decrease from the 112 seconds during cDBS to 26.5 seconds and the artifact shortens in duration. Discussion We demonstrate the presence of BFAs across all patients investigated (n=26) with two IPGs. These artifacts are unavoidable due to cross device interference when stimulating simultaneously. Each IPG relies on an internal clock time aligned with the oscillation of an internal quartz crystal. Due to minute differences, no two quartz crystals oscillate at exactly the same rate. Consequently, the duration of one second differs slightly across devices. When two devices are each set to a clinical stimulation frequency of 150.6 Hz, their actual stimulation frequencies will not match exactly. To the degree of precision of each IPGs internal timestamp, these differences are negligible, but they are evidenced by reproducible and predictable BFAs. When switching to aDBS, the IPG stimulation engine updates more frequently to adjust for frequent stimulation changes which could explain the decreased BFA interval seen in Fig. S1 . To accommodate BFAs in future aDBS algorithms, it may be possible to ignore the high amplitude deviations of BFAs with the strategic implementation of onset times. 5 Onset times are a Summit-defined embedded algorithm parameter which defines a minimum amount of time an adaptive state change threshold must be crossed before the stimulation change is implemented. Onset times longer than the BFA duration will allow aDBS algorithms to respond only to biological signals. This approach does however limit the responsiveness of aDBS algorithms. Importantly, future work is required to characterize the impact of BFA artifacts onboard the recently available commercial Medtronic Percept aDBS system. 22 The impact of the BFAs on aDBS performance should be taken into consideration when deciding to leverage a dual-device approach, and BFAs specific to each patient should be characterized before analysis and adaptive algorithm development. Most patients we investigated had at least one recording channel with a very weak BFA, characterized by minimal amplitude deviation that could be chosen for aDBS implementation. Identifying which channels are least affected for each patient and adapting identified biomarker calculations accordingly can help researchers avoid the lengthening onset times while minimizing the presence of BFAs on aDBS algorithm inputs. Conclusion This study establishes BFAs as a consistent and predictable phenomenon arising from simultaneous stimulation by dual IPGs in DBS therapy. Through controlled mismatches in stimulation frequency and long-term recordings across a multi-institutional patient cohort, we demonstrate that BFAs are an inherent consequence of minute discrepancies in internal clock rates between devices despite identical programming. We also show that aDBS modulates BFA characteristics, as ramping stimulation amplitudes led to shorter BFA intervals. Given the dynamic nature of BFAs and their risk for interference with biomarker detection and adaptive algorithm performance, strategic implementation of onset times and careful selection of minimally-affected recording channels should be considered as future mitigation strategies. Ultimately, understanding and managing BFAs is necessary for implementing adaptive neuromodulation in patients with dual IPG configurations. Data Availability All data produced in the present study are available upon reasonable request to the authors. Conflicts of Interest W.K.G: Received donated devices from Medtronic. Consulting agreements with Biohaven Pharmaceuticals. S.A.S.: Consulting agreements with Boston Scientific, NeuroPace, Abbott, and Zimmer Biomet. Co-founder of Motif Neurotech. P.A.S.: consulting for InBrain Neuroelectronics Inc. and has grant funding (to support fellowships) from Medtronic Inc. and Boston Scientific Inc. D.D.W. consulting for Medtronic Inc., Boston Scientific Inc., and Iota Biosciences. Dr. Storch reports receiving research funding to his institution from the Ream Foundation, International OCD Foundation, and NIH. He was a consultant for Brainsway and Biohaven Pharmaceuticals in the past 24 months. He owns stock less than $5000 in NView (for distribution of the Y-BOCS and CY-BOCS). He receives book royalties from Elsevier, Wiley, Oxford, American Psychological Association, Guildford, Springer, Routledge, and Jessica Kingsley. Acknowledgements The research was supported by the National Institutes of Health (NIH) NINDS BRAIN Initiative via contracts UH3NS100549 (S.A.S., W.K.G., N.R.P., J.A.H.) and UH3NS100544 (P.A.S.), NIH NINDS via contract R01NS130183 (D.D.W.), the McNair Foundation (N.R.P., S.A.S), Burroughs-Wellcome Trust CAMS Award (D.D.W), Chen Scholar (D.D.W.), and Michael J Fox Foundation (D.D.W.). Summit RC + S research systems were donated by Medtronic as part of the BRAIN Initiative Public-Private Partnership Program. We thank Scott Stanslaski for providing technical expertise regarding the Medtronic DBS system. This work relied heavily on the community expertise and resources made available by the Open Mind Consortium ( https://openmind-consortium.github.io/ ). References 1. ↵ Frey , J. et al. Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches . Front Neurol 13 , 825178 ( 2022 ). OpenUrl CrossRef PubMed 2. ↵ Stanslaski , S. et al. 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