Electrographic cue-reactivity co-localizes with accumbens deep brain stimulation in a case of opioid use disorder

Domain: neuroscience
Published: 2026-01-29
Editorial outcome: major_revision

Abstract
Opioid use disorder (OUD) is a significant public health concern, with over 30% of the affected population not responding to available treatments. Severe OUD is characterized by drug-cue reactivity that has been reported to predict treatment failure. We leveraged this pathophysiological feature to optimize deep brain stimulation (DBS) of the nucleus accumbens region (NAc) in a male patient with OUD. A personalized drug-cue-reactivity task was administered while recording NAc electrophysiology from a lead externalized for clinical purposes. We identified a drug-cue-evoked electrophysiological signal in the ventral NAc that was associated with an elevated craving state and attenuated with stimulation delivered to the same area. This electrophysiological biomarker, along with behavioral assessments, informed the re-programming of DBS to a more focal and effective stimulation site. This resulted in sustained suppression of drug-related cravings. This study represents a proof-of-principle for a personalized, biomarker-informed neuromodulation strategy in OUD.

Introduction
Substance use disorders (SUDs) are a growing public health concern of epidemic proportion, resulting in more than 600,000 deaths in the United States annually, with over 100,000 annual deaths in the United States from opioid use disorders (OUD) alone1. The greatest challenge to treatment success is preventing relapse, and relapse risk has been attributed at least in part to neural adaptations in the frontostriatal circuitry involving the nucleus accumbens region (NAc), an important component of the mesolimbic dopamine system2. Functional neuroimaging assays utilizing cue-reactivity paradigms to probe neural activity underlying drug desire (“craving”)3,4, compulsive drug seeking and drug-use behaviors have revealed hyperreactive responses to drug-related cues. These phenomena are associated with poorer treatment outcomes and increased relapse rates5,6. However, a well-recognized challenge with functional MRI is its limited spatial and temporal resolution, which precludes exquisite detailing of specific anatomophysiological involvement of the NAc region. Deep brain stimulation (DBS) is a long-term neuromodulatory treatment with the potential to rescue frontostriatal adaptations and reverse behavioral over-response to drug cues in severe cases7. However, the best way to engage the NAc network with DBS in OUD remains largely unknown8.

In this case study, we seized a serendipitous clinical opportunity to synchronize a personalized cue-reactive task to intracranial electroencephalographic (iEEG) recordings via an in-situ NAc DBS electrode. This afforded high spatial and temporal resolution to decipher the involvement of the NAc in drug-related cravings and behavior4,9. We report a proof-of-principle use of a specific drug-cue-reactive electrophysiological biomarker, alongside anatomical and behavioral correlates, to optimize DBS programming in a patient with OUD. Our work heralds the potential for personalizing DBS for OUD using an objective, physiologically grounded marker of drug-cue reactivity.

Results
The patient is a 25-year-old male with an existing NAc DBS to treat his severe OUD off-label (see Online Methods; Figs. 1a & S1a-c). DBS therapy improved his OUD and substance cravings, allowing him to abstain from drug use for several years, but required a prohibitively high dose of stimulation (14.5 mA) in a monopolar configuration that diffusely engaged a broad region including the ventral anterior limb of the internal capsule (vALIC) and adjacent dorsal NAc region (Fig. 1a). He suffered a wound erosion over the left subclavicular incision that warranted removal of the implantable pulse generator (IPG), which was followed by rapid recurrence of opioid cravings and drug-related thoughts (Fig. 1a). Due to suspicion of ongoing infection, the DBS system was externalized with an extension cable while cultures and antibiotic treatment ensued (Online Methods).

We hypothesized that prior high charge requirements were due to non-specific engagement of the NAc. To identify focal sites within the NAc region where cue-reactivity was most robust and specific to drug cues, three computer-based tasks were conducted for clinical purposes: a personalized drug-cue-reactivity task to assess for presence of drug-related cue-electroreactivity6 (Fig. 1b), the Monetary Incentive Delay, MID task10, and the Blink Suppression Task, BST11 (Online Methods). This battery of tasks allowed us to attempt to assess specificity of electrophysiologic drug-cue reactivity.

NAc low-frequency power is associated with high craving state
Presentation of personalized drug-related video cues evoked a robust elevation of low-frequency band (1–6 Hz) power in the iEEG recordings within the ventromedial-most region of the NAc, the presumed shell subregion12,13 (most ventral contacts 1-2) when compared to neutral cues (Fig. 1c). The signal was most prominent from 4.5 to 5.5 s post-cue onset, consistent with prior evidence that craving responses intensify with prolonged cue exposure3,14. This low-frequency power elevation was observed specifically during presentation of drug-related cues, in comparison to neutral cues, and was associated with an elevated craving state as assessed on a visual analog scale (VAS; range 1-9; task specific). It was also focally localized to the presumed NAc shell (Figs. 1d and S1d-e), consistent with anatomical specificity and known subregional physiology of the NAc15. Finally, this 1–6 Hz frequency power elevation was not elicited by non-drug incentive cues presented during the MID and BST tasks (Figs. S2 and S3). Rather, the MID task produced an elevation across a broader spectral power range (notably 3–21 Hz) during the anticipation window, and this was not anatomically specific to the NAc shell subregion (Fig. S2). The BST, which induces a strong urge to blink, showed no statistically significant low-frequency elevation during high-urge periods (Fig. S3). These well-validated tasks served as controls to assess possible domain specificity of the observed low-frequency signal. While they similarly engage reward anticipation and motor urgency, respectively, they differ from the cue-reactivity task in task design and phase of reward processing they probe. These differences limit direct comparability but reinforce the conclusion that the 1–6 Hz elevation observed during opioid cue exposure is at least not entirely attributable to general arousal, urge, or task engagement, and may represent drug-cue specific pathophysiology.

Stimulation modulates low-frequency power and lowers craving
Next, a systematic stimulation assessment was conducted across the DBS electrode contact pairs akin to standard clinical practice, but with additional focus on drug-related cravings in this subject (Table 1). The optimal stimulation site with the widest therapeutic window aligned with selection of the presumed NAc shell as the target, with reduction of cravings noted at the lowest dose required without adverse effects. Positive effects were also noted in more dorsal contacts in the vALIC (contacts 4+/5-) where he was previously stimulated, although a higher current amplitude (8 mA) was required to elicit positive effects during the evaluation. Sham-controlled trials suggested the acute effects of stimulation observed were not placebo effects.

We then investigated the effect of NAc shell bipolar stimulation during the drug-cue-reactivity task. As stimulation artifacts precluded monitoring iEEG signals simultaneous with stimulation, DBS was delivered for ten min (1-/2+, 6 mA, 90 µs, 130 Hz) prior to administration of the task to establish a physiologically “stimulated” state. For each trial, an approximately 15 s stimulation bout was delivered before each cue presentation followed by a brief pause in stimulation during cue presentation to enable artifact-free iEEG recordings (Fig. 1b). This interdigitated approach allowed for the observation of stimulated effects without introducing simultaneous artifacts. Acute NAc shell stimulation attenuated the drug-cue evoked elevation of 1–6 Hz low-frequency band power towards a power suppression (p = 0.032, two-tailed permutation test), as seen in neutral cues, and this was associated with >50% reduction in mean subjective rating of opioid-related cravings (6.57 vs. 3.09, p = 6.16e-04, two-tailed permutation test) (Fig. 1e-f).

Biomarker-optimized stimulation sustained long-term effects
Based on these clinical and electrophysiological findings, we personalized the chronic DBS parameters (1-/2+, 6 mA, 90 µs, 130 Hz) guided by convergent findings from behavioral testing and electrophysiological readouts evoked by the cue-reactivity task. The ventral NAc shell contacts exhibited both an electrophysiologic signal of craving and a robust acute behavioral and electrophysiological response to stimulation. This multi-modal approach enabled effective symptom control at substantially reduced current amplitude requirements compared to the original stimulation dose (14.5 mA). Six months following this personalization, the patient reported sustained reduction in drug cravings and continued abstinence from opioid use, meeting DSM-5 criteria for early remission (Fig. 2a). Notably, while the patient did not relapse to opioid use at any point, including during the DBS withdrawal phase, self-reported craving levels and inhibitory control improved progressively over the first several months following reactivation of DBS. These clinical improvements were specific to drug-related behavior, and did not affect his self-reported anxiety, mood and energy levels, as monitored via structured electronic questionnaires using VAS scales (range 0-10) during remote follow-up (Fig. S4). This progressive improvement suggests a possible cumulative or time-dependent effect of DBS on craving suppression and behavioral regulation (Fig. 2a). Such a trend may reflect ongoing plasticity within frontostriatal circuits or gradual behavioral integration of neuromodulatory effects. These observations underscore the value of longitudinal follow-up in future DBS studies to better characterize the trajectory and durability of therapeutic effects.

Optimized NAc stimulation engaged SCG circuit and conserved charge
Structural connectivity analysis of the two stimulation sites revealed that the original stimulation setting had a much larger (25x) volume of tissue activation (VTA) (896 mm3 vs. 35.4 mm3), overlapping with the presumed NAc shell subregion, with additional, non-specific activation of other brain regions. Conversely, more apparently focal DBS delivery to the NAc shell appeared to engage a known circuit involving the subcallosal gyrus (SCG)12,13,16 (Fig. 2b-g). A basic modelling of charge requirement with this new stimulation paradigm predicted substantially improved battery lifespan (Energy Use Index 12.3 vs. 39.3) of the patient’s IPG, further emphasizing the value of precision targeting and personalization of DBS therapy. These results suggest that our personalized strategy delivered DBS with similar therapeutic efficacy, fewer off-target activations, and less device charge drainage.

Methods
Clinical history
At the time of the current presentation, the patient was a 25-year-old male with DBS implanted 4.5 years prior. He first developed an addiction to opioids from thirteen years old following rapid withdrawal of prescription opioid medication. He also had co-morbid depression, anxiety and attention deficit hyperactivity disorder, and was treated with multiple anti-depressants, mood stabilizers and anti-psychotic medications. He started intravenous use of opiates at age fifteen and experienced episodes of opioid overdoses. He also developed disabling tardive dyskinesia that disrupted all his activities of daily living and led to severe disability (Global Assessment of Functioning Score 40) and dropped out of school. In spite of chronic methadone maintenance therapy (maximum dose 320 mg/day), he had recurrent opioid cravings and required several inpatient hospitalizations for detoxification. After several years of medication optimization without success for his neuropsychiatric conditions, he was finally referred by his movement disorder specialist and psychiatrist for off-label bilateral DBS of the globus pallidus interna (GPi) to treat his severe tardive dyskinesia62. At the same time, DBS therapy for his severe, medication-refractory OUD was discussed. After discussion with the patient, with his parents and after careful consideration of potential risks and benefits, a consensus decision was made for bilateral GPi DBS (with right-sided IPG) to treat his tardive dyskinesia, as well as a unilateral left NAc DBS (left-sided IPG) targeted at treating his OUD (Figure S1). A unilateral NAc implant was chosen to mitigate the risk of this off-label surgery, recognizing an additional lead could be placed in the future if deemed necessary.

The DBS therapy was programmed based on standard clinical monopolar assessment used in movement disorders and psychiatric practice. Bilateral GPi DBS improved his dyskinetic movements significantly, which remained stable and managed at an outside facility with minimal adjustment required since initiation. The left NAc DBS electrode was activated based on in-clinic assessments of mood, anxiety and energy, but required a very high dose of stimulation (14.5 mA) between contacts 4 and 5, which were located in the vALIC (Fig. S1c). When stimulated at yet higher doses, he experienced diaphoresis and myoclonus. Unfortunately, he was unable to tolerate a rechargeable system, which destabilized his clinical condition whenever the IPGs were inadequately charged, leading to the team to recommend switching to non-rechargeable IPGs. The high dose requirement, however, led to rapid charge depletion of the IPG. Several months after clinical stabilization, he relocated to France, where a local psychiatrist managed his condition and DBS programming. Following DBS activation, methadone dose was tapered to 120 mg/day, and the patient remained abstinent for four years without requiring residential, intensive outpatient (IOP), or sober living programs. His clinical course suggested that DBS played a central role in suppressing craving and maintaining abstinence. In the interim, he developed episodes of hospital-acquired infections (pneumonia with bacteremia), complicated by infective endocarditis that required five cardiac surgeries for valve replacements. All these cardiac procedures occurred between ages 22–23, almost two years before his presentation to our team. There was no clinical or toxicologic evidence of intravenous drug use relapse during this period and abstinence was corroborated by urine toxicology and caregiver reports. He abstained from drug use for several years (sustained remission) while the DBS was on, and craving levels were minimal. He was able to regain function in life, completing high school education and was able to work.

Four months after his last cardiac surgery, he underwent replacement of his left IPG due to battery depletion. A subsequent wound erosion (eleven months later) over the left subclavicular incision warranted removal of the left DBS IPG (extension wires were retained) and wound debridement, which was followed by a rapid recurrence of opioid cravings and drug-related thoughts (Fig. 1a). Within the first several weeks of IPG explant, the patient condition destabilized, and he attended emergency department three times for symptoms of elevated cravings and fear of relapse. His medications were uptitrated and methadone had to be increased from 120 mg/day to 240 mg/day to cope with his symptoms. This led him to re-engage with addiction support services. Because no local surgeon with necessary expertise was available and given his complex medical history, his family and psychiatrist reached out to our institution to re-establish DBS therapy. Due to clinical suspicion for ongoing wound infection given ecchymosis at the subclavicular site, the retained extension wire was explanted and the DBS lead externalized with an extension cable while local and blood cultures and antibiotic treatment ensued. During this period, personalized computer-based cue-reactivity tasks were performed to identify and localize a potential electrophysiological biomarker of drug-cue-reactivity by recording brain activity from the DBS lead. We hypothesized that prior charge density requirements were due to non-specific engagement of the NAc region from vALIC stimulation site. To identify sites within the NAc region where cue-reactivity was most robust and specific to relevant drug cues, three computer-based tasks were conducted: a primary drug-cue-reactivity task (designed with his personalized drug use habits) to assess for presence of drug-related biomarker6 (Fig. 1b), the Monetary Incentive Delay, MID10 and the Blink Suppression Test, BST11 (Figs. S2 and S3). The two latter tasks were used as behavioral controls to assess specificity of electrophysiologic drug-cue reactivity.

There were several concerns in the management of this patient. First, a time-sensitive restoration of his DBS therapy was considered to be crucial, as he reported exacerbation in his cravings (though he had demonstrated sustained drug abstinence), a daily struggle for him. Second, the high charge requirement and intolerance of a rechargeable system was worrisome in view of his young age. Thirdly, it was critical to ensure complete eradication of the infective organism before re-implantation of the IPG due to risk to both the DBS device as well as his prosthetic heart valves. With these considerations in mind, the patient was hospitalized for treatment, with a motivation to restore his DBS therapy as soon as he was cleared by infectious disease and potentially optimize programming parameters. Due to suspicion of unresolved infection of the left IPG site, a wound exploration and swab were performed, while explanting the retained extension wire. This revealed persistence of Staphylococcus aureus. Upon consultation with infectious disease specialist, he was commenced on intravenous antibiotics and the DBS system was externalized percutaneously with an extension wire using previously reported surgical methods in an attempt to preserve the proximal system while antibiotic treatment ensued63. Using the externalized system64,65, we devised a plan to use personalized computer-based cue-reactivity tasks to attempt to identify potential electrophysiological biomarker of drug-cue-reactivity. The overarching purpose of this was to localize the most active node encompassed by the NAc electrode for clinical purposes. We hypothesized that prior high amplitude requirements were due to non-specific stimulation target engagement and that a more precise stimulation may help reduce charge requirement and extend device lifespan which would be critical in the clinical management of this patient. The patient provided informed consent to participate and to allow publication of relevant information for educational purposes. The DBS recordings were performed entirely for clinical purposes in this unique off-label case of a patient at imminent risk for relapse. The recording data collected added confidence to how the acute stimulation assessment was interpreted though did not change clinical practice. Moreover, the University of Pennsylvania maintains an Institutional Review Board-approved protocol for post-implantation recordings of electrophysiological data in DBS patients (IRB #851872).

Computer-based tasks
(a) Personalized cue-reactivity paradigm task3,6. The patient was clinically interviewed by an expert in cue-reactivity (A.R.C) for specific details related to his drug use habits and preferences prior to the task. The patient was presented with a series of 6 s-long videos in a quasi-random block design in two categories (drug cues and neutral cues). The video clips precisely reflected his paraphernalia, preferred opioid drug and injection preparation. This personalized approach seemed necessary to evoke a reliable electrophysiologic cue reactivity and an associated craving response in this patient, as he reported that standardized cue databases, developed primarily based on U.S. practice, did not reflect his experience and failed to elicit strong craving. Neutral cues consisted of emotionally neutral nature scenes to serve as a low-salience comparator. After each video presentation, the subject was asked to rate his level of craving from 1 (no craving) to 9 (extremely high craving), reflecting the patient’s momentary craving intensity in response to the preceding stimulus. Each session included six drug-related cues and six neutral (video) cues, presented in a quasi-randomized block design. The task was designed based on our team’s clinical judgement and cue-reactivity paradigm expertise of one of our senior authors (A.R.C) to balance the need for cue repetitions (for signal averaging) with risk of reduction of cue-induced responses with too many exposures (habituation or extinction effects). To help minimize the repeated, exposure-related reductions in responses, cue sessions can be less frequent or spaced widely apart. Therefore, in our design, the cue-reactivity tasks were conducted on alternate days, and tasks were not performed on days involving stimulation testing with craving assessment to allow sufficient stimulation washout. A total of four cue-reactivity task sessions were completed: two before any stimulation was initiated and two with active stimulation. Sham stimulation was not included in the cue-reactivity task to minimize exposure-related reductions in cue-triggered responding.

(b) Monetary Incentive Delay (MID) task10. The MID task is a validated and widely-used cognitive task to identify neural substrates of monetary reward anticipation and outcome, and has been used by this team to activate cue-reactivity within the NAc using both functional magnetic resonance imaging and electrophysiology28,66. The patient was presented with a visual shape cue that signals the possibility of a reward (potential gain), loss (potential loss) or neutral (no gain or loss) trial. Following the visual shape cue presentation of two seconds, a delay phase of two seconds is imposed before a target appears to prompt a response by the participant with a button press as quickly as possible to gain the reward or avoid a loss. Finally, in the feedback/outcome phase, the participant receives performance feedback with cumulative earnings. The MID task was performed using real monetary rewards, with immediate feedback and actual earnings delivered based on trial outcomes, to ensure ecologically valid engagement of reward anticipation circuits67. This task is one of the most widely validated paradigms for probing reward-related neural activity.

(c) Blink Suppression Task (BST)11. This task uses eyeblink suppression as a model for sensory-based urges, with blocks of free blinking (30 s) alternating with longer blink suppression periods (60 s). During the free blinking periods, the instruction ‘Normal’ is displayed and subject is allowed to blink as normal. During the blink suppression period, the instruction ‘Hold’ is displayed and the participant is told to withhold blinking as long as the instruction is present. After this block, there is a recovery period with instruction ‘Ok to blink’ where the participant is allowed to blink as much as he wished, and a visual-analog-scale (VAS) rating of the urge to blink during the ‘hold’ period is collected. Eight blocks of each period are presented.

Electrophysiological recording and signal analysis
After wound debridement and cultures, a Boston Scientific DBS extension wire (model NM-3138-55) was connected to the in-situ Boston Scientific DB-22018-ringed contacts DBS electrode, tunneled and externalized to allow intravenous antibiotic treatment of the wound infection and preservation of the remaining DBS system63. An in-house customized adapter was used to connect to the Boston Scientific DBS extension wire (Boston Scientific, Marlborough, MA) to the g.HIAMP PRO system (g.tec medical engineering GmbH, Austria) for continuous iEEG recordings of the NAc region during conduct of the computer-based tasks.

Raw iEEG data acquired at 1200 Hz sampling rate was exported post-hoc for offline analysis using custom MATLAB scripts, applying a high-pass filter (1 Hz) and notch filter at line noise frequency (58–62 Hz) and harmonics. A time-frequency analysis using multi-taper convolution methods was used to characterize spectral power components of the cue-reactive signals. The power was extracted by squaring the magnitude of the complex Fourier-spectra and transformed by the log function. The time window length was shortened (4 to 0.13 s) with increase in frequency (1–115 Hz) and timestep of 0.02 s. The frequency resolution was dense in the low frequencies starting from 1 Hz and more sparse in the high frequencies up to 115 Hz using logarithmic space. The power spectrum was corrected using a common baseline from the interval 0.1-1 s before the cue was presented. Difference in mean power spectrogram obtained from each condition epoch (e.g., drug-cue or blink suppression) was compared to a neutral condition epoch (e.g., neutral-cue or normal blink) which served as the control condition. To identify significant time-frequency regions of interest without predefining time epochs and frequency bands, we applied a data-driven procedure across the full time-frequency spectrogram (1–115 Hz, log-scaled). For each time-frequency sample, we computed a two-tailed paired-sample t-test, to compare responses to drug vs neutral cues across trials. Samples exceeding an uncorrected threshold of p < 0.05 with the same sign were grouped into connected sets, i.e., ‘clusters’, based on spectral and temporal adjacency68. Clusters were identified by size (i.e., the number of supra-threshold samples), and a permutation test was performed to test for significance for each of the cluster (10,000 permutations, alpha = 0.05). Within-pair sign-flipper permutations were used to construct a null distribution of the largest cluster size observed by chance (two-tailed). Each observed cluster was compared against the null distribution to determine whether the cluster was unlikely to occur by chance, and only clusters that passed this comparison were considered significant. No conclusions were drawn from individual time–frequency bins. This cluster-correction process resulted in statistically significant clusters in time and frequency that can be used to define a region of interest (ROI) for further hypothesis-driven analyses. To compare responses of stimulation (e.g., ROI mean power or craving ratings during drug cues with vs. without stimulation), a non-parametric two-tailed permutation test (10,000 permutations) was applied. For each comparison, within-pair sign-flipper permutations were performed to generate a null distribution of mean differences. The p-value was computed as the proportion of permuted differences greater than the observed difference (two-tailed). This analysis of the stimulation effect is distinct from the cluster-based permutation described above, and was chosen due to the small sample size and non-normal distribution of these data. Trials with excessive movement artifact or recording dropouts were excluded from analysis to ensure data quality.

Neuroimaging analyses
High resolution pre-operative MRI and post-operative computer tomography (CT) head were available for imaging analyses. T1, T2, and fast gray matter acquisition T1 inversion recovery (FGATIR) sequences were acquired at 1 mm thick slices on a 3T GE scanner (GE Healthcare, Chicago, IL), as well as multi-shell 64-direction diffusion weighted imaging for tractography. The post-operative CT was co-registered to the pre-operative MRI using a 2-step linear and non-linear registration using Advanced Normalization Tools69. Probabilistic tractography was performed using FMRIB Software Library (FSL)’s Probtrackx2 using the NAc as a seed and the subgenual cortex as a target to subsegment the NAc into shell and core regions using clustering of streamline density according to our previously published methods13.

Volume-of-tissue activation (VTA) analysis was performed using Lead-DBS software based on active DBS parameters using its in-built finite element modeling (FEM)70. Structural connectivity analysis with streamlines was performed using the DSI-Studio71 using the estimated VTAs as seeds in the whole brain tractography derived from patient’s diffusion MRI.

Acute anti-craving stimulation assessment
Using the Boston Scientific external stimulator, a systematic stimulation assessment was performed across adjacent electrode contact pairs in bipolar fashion. For each contact pair, stimulation amplitude was gradually increased in 0.5 mA stepwise increments, with each level maintained for at least 1 min to observe acute effects and monitor for side effects. Acute stimulation effects were recorded for therapeutic as well as side-effect thresholds. The patient’s mood, anxiety and affect were recorded per standard limbic stimulation assessment. In addition, opioid craving levels were recorded using a VAS scale compared to baseline level.

To confirm anti-craving effects of stimulation, a short sham-controlled (patient and assessor blinded) stimulation session was also conducted to verify the effects of active stimulation. Sham stimulation was delivered using a dedicated and separate stimulation box that generated auditory outputs similar to active stimulation (e.g., clicks and device engagement) but did not deliver electrical current to the brain. This approach preserved sensory cues associated with stimulation while avoiding neuromodulatory effects. The patient was informed that some blocks would include stimulation and others would not, but was blinded to specific condition assignment during each trial. For the stimulation parameters notation, the configuration “C+/4- (60%) 5- (40%)” refers to the case acting as the anode, with current split between contacts 4 and 5 (60% and 40%, respectively) as cathodes.

Clinical outcomes
A composite self-report electronic questionnaire was administered at regular intervals throughout the follow-up period. This included items adapted from the Obsessive Compulsive Drug Use Scale (OCDUS), Brief Substance Craving Scale (BSCS) and Brief Addiction Monitor (BAM) questionnaires as well as VAS for anxiety, mood and energy72. VAS ratings of craving frequency and severity range from 0 to 10 unless otherwise noted. The composite was used to monitor the patient’s subjective levels of craving, substance use, mood and behavior over time. Scores from this tool are presented in Figs. 2a and S4 as part of the longitudinal assessment of clinical outcomes.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript entitled, “Localizing electrophysiologic cue-reactivity within the nucleus accumbens guides deep brain stimulation for opioid use disorder” presents the interesting case of a single participant who was previously treated for tardive dyskinesia (bilateral globus pallidus interna DBS, IPG placed in right chest) along with unilateral (left) lead placement in the nucleus accumbens (left chest IPG placement) to treat opioid use disorder. With very high dose (14.5mA) stimulation between contacts 4 and 5 (in the ventral anterior limb of the internal capsule) the patient had achieved sustained remission from opioid use (almost 4 years). The high stimulation led to low battery life but a rechargeable was not deemed feasible (clinical destabilization when charge was too low). Recurrent infections led to multiple surgeries and then wound erosion/infection necessitated removal of the left IPG and explanation/externalization of the nucleus accumbens extension wire/lead. This provided an opportunity to administer a personalized cue-craving while recording from the lead contacts. The investigators report convincing results, demonstrating increased power in the 1-7hz range during cue vs. neutral which was attenuated following short periods of bipolar stimulation at 1-2+ at 4-6mA, pulse width of 90 and frequency of 130. There are few examples of direct recording from the nucleus accumbens in the realm of addiction – the results, which are consistent with this group’s prior work in binge eating disorder, are quite interesting and if replicated would be a major advance toward adaptive/closed loop stimulation approaches in this population However, several questions/concerns remain.\n1) From a programming standpoint, what is the advantage of the recordings? It appears that the same stimulation location was found (a) anatomically (shell of the nucleus accumbens) and (b) through standard programming procedures (exploration with patient subjective response). In opposition to what the paper title/body text suggest, did the recordings actually contribute to guiding the programming for this patient? It would seem that without the cue craving paradigm and recordings, that 1-2+ would have been selected from Table 1 (and it appears that initial monopolar programming at the outside hospital did not assess craving – online methods line 317). Of course, the identification of a marker of craving is, in itself, a significant contribution but clarification regarding how this was essential in guiding programming in this case is not clear.\n2) It is interesting that unilateral stimulation of the left nucleus accumbens was adequate in this patient to achieve sustained remission. Although space is limited, a comment on this would be helpful (even if in online materials). I understand the rationale was a second lead could be placed if needed and this decision was made at an outside hospital. To my knowledge generally bilateral nucleus accumbens DBS has be utilized for addiction phenotypes. Why might unilateral be thought to be adequate? Would right nucleus accumbens reactivity somehow be treated via the left nucleus accumbens stimulation?\n3) There is a lack of information regarding other substance use disorder treatment received by this patient during the time on DBS (before and after the in-hospital recordings). When was he on methadone/MAT and at what doses? Did he receive other care (IOP, sober living, residential, 12 step, etc) and if so when? It sounds like he was medical quite ill – in some instances such concerns can lead to motivation toward abstinence. Better outlining potential confounds which may have contributed to his good OUD outcome would be helpful - though the effects of DBS appear evident. In addition, what was the source of the recurrent infections (they are described as hospital acquired)? One wonders immediately about injection drug use, but it appears the patient was abstinent for many years. Could more information be shared here?\n4) Was the personalized cue craving task shown repeatedly? Any concern regarding habituation? The image shown suggests the neutral cue was of nature images. In some instances, efforts are made to pair the neutral images based on content (e.g., hands, tools, faces, lighting; Ekhtiari et al., 2020) and standardized images are employed (as opposed to personalized). Can more be said about the choice to use an individualized set of images and how/why the control images were selected? The individualized approach based on expert interview make it less standardized or exportable to other labs for use. Online methods Computer based tasks (line 367) - Regier et al 2021 is used as one of 2 references for the personalized cue craving task but a quick review of that paper does not suggest a personalized cue craving task was used there. And that paper appears not to have focused on opioids.\n5) Online methods lines 414-416 “This was systematically performed for different frequency bands ranging from 1-115Hz, with different size ranges…” – can the authors state the number of tests completed and adequacy of thresholding? A cluster-based permutation test was utilized but just to double check, did thresholding account for all comparisons (e.g., number of frequency bands, number of contacts, number paradigms examined). Figure 1 remains quite convincing.\n(6) Figure 2.b suggests that effects of stimulation may have a time effect (e.g., improvement in clinical outcomes with greater time) with the greatest effects seen at 3-6 months, though no drugs use was seen throughout. Can the authors comment on this?\n(7) Minor\nFigure 1 - “Intravenous drug abuse initiated” – abuse is an older term. Might update to be consistent with DSM-5 or to simply replace abuse with use. “Reduced Craving Instilled Abstinence” – the term “instilled” might be revised. This suggests an understanding of the mechanism vs. an observed association.\nUFAT (line 139) is not previously defined.\nThe supplemental file video S.5 include a still image of the patient’s face that appears potentially identifiable. The videos themselves appear appropriately blurred.\nEkhtiari H, Kuplicki R, Pruthi A, Paulus M. Methamphetamine and Opioid Cue Database (MOCD): development and validation. Drug and alcohol dependence. 2020 Apr 1;209:107941.\nRegier PS, Jagannathan K, Franklin TR, Wetherill RR, Langleben DD, Gawyrsiak M, Kampman KM, Childress AR. Sustained brain response to repeated drug cues is associated with poor drug‐use outcomes. Addiction biology. 2021 Sep;26(5):e13028.", "reviewer_2": "The authors presenting an intriguing case study with important implications for the biological underpinnings and treatment potential of opioid use disorder. The report is well-written and includes important within-subject controls to give confidence in their conclusions for this individual. The ‘serendipity’ and invasiveness of the assays and intervention make a “n of 1” reasonable, with the caveats that apply. Specifically, the authors need to be careful about over-generalizing. While the results may generalize across substance use disorders, that cannot be known from this data. Thus, the abstract should be constrained to this disorder (OUD). Further, the title should include “case study”. Overall, I think the manuscript has high potential impact, but I think could benefit from addressing the following points:\n- Line 133: “Positively correlated” implies a statistical comparison. Please comment on whether/how this was done, or if simply descriptive finding.\n- Figure 1: Why is there a focus on 4.5-6 seconds? I see the pattern in c which I suspect led to the analysis in e and f, but please comment on the reason for the delay, or why this was analyzed rather than 0-1 sec which would likely be significant in the opposite direction. Moreover, why was permutation not used for this comparison? Any reason sham results were not included in this figure?\n- Line 161: Were personalized parameters based on biomarker responses? Behavioral responses? Please describe in detail this personalization since the durable response is an important aspect of the potential impact of this study.\n- Fig S4/line 167: Based on VAS? Were any validated psychometric scales obtained?\n- Fig 2c-f: Could probabilistic tractography prospectively lead to the same results (efficacy with lower amperage) as the reported 1-7Hz frequency band changes? Please comment on the likelihood of this alternative imaging (only)-based guidance producing the same results (which would not require recording electrodes). If the authors think tractography could not derive the same result, does this imply that DBS for depression (as an example), might require electrophysiological guidance beyond the tractography approach? Please speculate on this, as the authors point out, this case study could have broad implications, but it is important to establish to the best of their knowledge whether these results could be arrived at through less invasive means.\n- What are the potential effects of effective long-term DBS and the electrophysiologic signature? Were references 16 and 17 of patients who were previously well-managed on DBS? Could this provide insight into the similarities (theta) and differences (alpha and gamma) with the prior findings?\n- Line 235: please add that it may not even be generalizable to other OUD patients, as pointed out in discussion, even other heroin addicts had different signatures, and it cannot be known what the effect of prior treatment was, the age of onset, the means of onset, and many other clinical variables, which we can see in the large variability in whether a person responds to treatment or not.\n- Since the authors frequently interchange SUD with OUD, can they state whether any other studies outside of OUD have shown physiologic signatures and how they compare with OUD? If none, please add this information so that readers understand that the electrophysiologic relationship between OUD and SUD is unknown.\n- Please describe how sham was performed, particularly since the patient reports being able to tell he was getting sham. Similarly, with Figure 2a: Why is there no anatomic region listed for sham? [I am reading this as alternating sham and active x2 in the section under sham – please also clarify this section as it is not clear what each line represents]. More clarity on the procedure might clarify this. In figure 1 e-f, is “without stim” the sham? Please describe how the blocks of stim and no stim were arranged, specifically in regards to order effects. If sham = “no stim”, please describe how this was used as a sham – for example, was patient told they were receiving stim?\n- Fig 2 shows improved mood and anxiety in shell stim, in contrast with text and supp figure. Please clarify (I think this refers to real-time vs follow-up?).\n- Why are analysis in Fig S1 (bottom panels) different from S2?\n- Figure 1(e): what does the shaded area represent? In Figure 1(f), what do the error bars indicate? Please clarify whether these reflect SD, SE, or 95% confidence intervals.\n- [Online Methods] Figure 2(b) includes OCDUS scores, yet this is not mentioned in \"6. Clinical Outcomes\" section. Please address this omission.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The manuscript entitled “Localizing electrophysiologic cue-reactivity within the nucleus accumbens guides deep brain stimulation for opioid use disorder” by Qui et al shows that personalized drug-related cues elicited a specific rise of low-frequency micro-EEG power at 1-7 Hz in the most ventral region of the NAc that was strongly associated with a subjective feeling of desire for drug use in a patient with in situ DBS electrode implanted unilaterally in the ALIC-NAc region. High-frequency DBS of the same region delivered acutely with clinically relevant parameters in a preexposure mode normalized the electrophysiological signal and suppressed the drug craving. Chronic stimulation at remarkably low intensity in a focally selected subregion within the NAc yielded successful abstinence of drug use for over 6 months without noticeable adverse effect while improving mood and reducing anxiety.\nThe authors did a great work by taking advantage of well-established cognitive tasks, especially the cue-reactivity test that has conventionally been using in functional imaging studies, together with electrophysiological recording from previously implanted electrode in the NAc in a patient with SUD. Simultaneous measurement of intracranial EEG and drug craving in response to precisely selected drug cues provided an opportunity to find a specific electrophysiological biomarker of craving that was subsequently targeted by clinical DBS.\nFor closing the loop on SUD, we already knew that: (1) the best characterized region to be targeted was the NAc; (2) there was a need for a biomarker of craving; (3) it should be of electrophysiological nature because the stimulation is electrical; (4) it should be in the form of local field potential; (5) ideally it should be recorded from the same region where the stimulation is given; (6) it should be specific for drug reward and not occurring in response to natural rewards; and finally (7) we knew from the work of Wu et al. PNAS 2018: pnas.1712214114 that it should be around delta frequency band. In my opinion, this case study put nearly all together and even though it only dealt with programming, but it provides us with clues to resolve the historical need for a reliable biomarker of craving that can be used for adaptive on-demand delivery of DBS for treatment of intractable SUD.\nThere are some concerns though that should be addressed by the authors to improve the manuscript:\nThe authors had better consider revising the title. It is inferred from the title that electrophysiological response to drug cue can guide DBS treatment of addiction. Although the concept is correct and is supported by the presented data (the evoked signal was more prominent in the ventral NAc and stimulation was more effective in the same region), the electrophysiological response was not directly used for DBS programming of the patients as it was the behavioral response (subjective craving) that finally guided programming (Fig 2a). This issue should be taken into account to soften the claim made in Lines 215-216.\nLine 117: Stimulation had originally been given in the vALIC. More caution should be taken when stating this because the stimulation mode was monopolar (C+/4-(60%)5-(40%)) that is known to provide more widespread stimulation (than bipolar) and contact 4 was located in the NAc core based on the image shown in Fig S1c. In addition, Fig 2f shows spread of the stimulation in the NAc as well as the vALIC.\nFig 1f and lines 155-159: Student’s t-test was used. It seems that a two-way repeated measures ANOVA have to be used. Please mention the tests in figure legends.\nLines 133-134 and 219-221: There was positive correlation between the elevated power at frequencies 1-7 and the subjective drug craving. No statistical test supported the claim. Although craving and power were simultaneously plotted in Fig 1f, there have to be a correlation test (Pearson, Spearman, or …) between these variables as well as fitting a regression line.\nFig 1b, e, f: There is no indication of the timing and specification of stimulation relative to both baseline and evoked electrophysiological response.\nLines 138-139: The 1-7 Hz frequency power elevation was not elicited by non-drug incentive cues (Fig S2a-b and Fig S3). At least in Fig 2b (Monetary incentive delay (MID) test) it is obvious that there is an increase in the power at frequencies 3-21 Hz, which encompasses part of the response elicited by drug cue presentation (1-7 Hz). Therefore, there is a concern regarding the specificity of the electrophysiological response (elevated low-frequency band power) to drug-cue. Therefore, 3-7 Hz power elevation occurred in response to both drug-cue and a surrogate natural reward. It also seems that electrophysiological response happening at the time of cue presentation and fixation (first 4 s) in the MID test cannot be conceptually comparable with that happening during drug cue presentation in cue-reactivity task because the former only represents reward expectation while the latter represents recollection of personalized drug-related cues. This concern gets even more serious when there is no indication in the manuscript that MID test was performed with a hypothetical or real reward because previous studies have shown that these two types of MIDs may have different effects on the brain activation profile (Xu et al. Scientific Reports 2018: 10.1038/s41598-018-21820-0). In this sense, including more realistic cues of natural reward (food, sex, …) similar to those used by Regier et al, Addiction biology 2021: 10.1111/adb.13028 would better capture the problem.\nHow the stimulation parameters mentioned in Fig 2a were first chosen? Was there a stepwise increase in the stimulation intensity to reach the therapeutic effect?\nLines 207-208: Please emphasize that preclinical studies also highlighted stimulation in the shell subregion of the NAc for SUD.\nPreclinical studies suggest that an extinguished drug seeking behavior can be reinstated by drug cue/context, drug priming, or stress. Human SUD patients may also experience withdrawal-based and cue-induced craving (people, places, paraphernalia, etc.). This study emphasized the presence of an electrophysiological biomarker of cue-induced carving in the NAc shell and its modulation by focally targeted DBS. Previous studies (Mantsch et al. Neuropsychopharmacology 2016: 10.1038/npp.2015.142) also implicated NAc in the stress-induced reinstatement of drug seeking. This was not seen in the present manuscript. Does the elevated low-frequency band power also signal stress-induced craving? Is that modulated by DBS in the NAc shell?\nLine 52: please use “in a patient” instead of “in an adult”.\nLine 113: The expression “… an objective read-out of at-risk pathophysiology” is ambiguous and difficult to understand at least in the place it appeared in the text before giving any data. Please consider using a clearer statement.\nLine 236: Please correct “Based our findings, …”. “On” is missing.\nSupp videos: Please provide subtitles for non-native English readers.", "reviewer_5": "This manuscript presents a case study of a personalized approach to deep brain stimulation (DBS) for severe opioid use disorder (OUD). The authors report using an externalized nucleus accumbens (NAc) DBS lead (available due to a temporary clinical necessity) to record intracranial EEG during a personalized drug cue-reactivity task. They identified a specific low-frequency (1–7 Hz) oscillatory “biomarker” in the ventromedial NAc (presumed NAc shell region) that was robustly elevated by opioid-related cues (and correlated with subjective craving levels) but not by neutral cues or non-drug control tasks. Using this information, they reprogrammed the patient’s DBS to specifically target the NAc contact showing cue-reactivity, at a much lower stimulation amplitude than previously used. Over six months of follow-up, the patient experienced sustained suppression of drug cravings and continued abstinence from opioid use (meeting DSM-5 criteria for early remission), with no adverse effects and improved efficiency of the DBS therapy.\nThis should be the first reported instance of using intracranial electrophysiological cue-reactivity data to guide DBS programming in addiction. The work leverages a unique clinical opportunity to gain insight into the neural underpinnings of opioid craving at high spatiotemporal resolution to guide programming. Rather than trial-and-error methods, one could use a patient’s own neural response to drug cues to “personalize” the DBS target and settings. This approach could increase efficacy (as seen by the craving reduction here) and minimize side effects (by reducing unnecessary volume of stimulation). Given the ongoing opioid overdose epidemic and the lack of effective treatments for severe, treatment-refractory OUD, this report is timely and could be of high interest to the neurotherapy, addiction psychiatry, and broader neuroscience communities.\nMajor comments:\n1. While the findings are intriguing, the manuscript’s scope as a single-case study may raise questions about broad interest and translatability for a journal like Nature Communications. The authors should more explicitly frame how this case provides a “proof-of-principle” for a generalizable approach. For example, could this personalized biomarker-guided DBS strategy be implemented in other patients or tested in a clinical trial? In particular, since individualizing cues to elicit sufficient craving is a challenge in this field, how they designed their individualized cue tasks so would have been important to include. The discussion would benefit from highlighting a path toward broader application – e.g., using sensing-enabled DBS devices or temporary externalization in a larger cohort – to convince readers that this is not an isolated anecdote but rather a demonstration of a scalable therapeutic concept. The authors do note that this is the first such use of electrophysiological target engagement in SUD and propose the cue-induced low-frequency signal as a candidate biomarker for future studies.\n2. Statistical Validation: The authors used a cluster-based permutation test to find the time-frequency region of significant difference between drug and neutral cue trials, which is appropriate. However, when quantifying the effect, they report statistical significance with simple t-tests (e.g., drug vs neutral power without stimulation, p=0.018; drug without vs with stimulation, p=0.039; craving ratings difference p=4.32×10^–7). Given that these tests are performed on multiple trials from a single subject (e.g., 7 drug versus 10 neutral trials, as noted in Fig.1f), the independence assumption is arguable – essentially, the subject is the only experimental unit. It would be wise to clarify the statistical approach in the Methods. For instance, are these unpaired two-sample t-tests treating each trial’s averaged power as an observation? If so, a non-parametric test (e.g., permutation test on condition labels or Wilcoxon rank-sum) might be more robust for small N. In any case, the authors should caution that p-values in an N=1 context reflect within-subject reliability of the effect rather than population-level significance. The extremely low p-value for craving reduction does convey a very large effect size, which is convincing, but a brief discussion of statistical limitations would enhance rigor.\n3. Correlation with Craving Ratings: The text notes that the low-frequency power increase was “positively correlated with the patient’s ratings of drug cravings on a visual analog scale,” but no correlation coefficient or statistical significance for this correlation is provided. To substantiate this important point, the authors should report the correlation analysis (e.g., Spearman or Pearson r between trial-by-trial 1–7 Hz power and craving rating for drug-cue trials). If this analysis was performed, including the correlation value and p-value (even if just in the supplement) would bolster the claim that the neural signal tracks subjective craving in real time.\n4. Specificity of the Signal: The authors did a good job including control tasks (the Monetary Incentive Delay and the “Urge-For-Action” blink suppression task) to test whether the 1–7 Hz power increase was specific to drug cues. The manuscript states that no such low-frequency elevation was elicited by non-drug incentive cues in the MID or the blink task (and Fig. S2, S3 presumably show this). It would be helpful if the authors could summarize these negative findings in the Results text with a bit more detail. For example, confirming that only the drug cues (and not generic reward anticipation or urge to blink) produced the significant power increase underscores the pathophysiological specificity of the biomarker. This helps rule out alternative explanations like general arousal, stress, or motion artifact causing the low-frequency activity. Furthermore, their findings would have been strengthened if it included individualized cues that cause other types of craving or pleasureable feelings.\n5. The stimulation testing involved an interdigitated approach, where “DBS pretreatment was delivered for ten minutes (1-/2+, 6mA, 90µs, 130Hz) prior to administration of the task, and an additional 15s stimulation period was delivered before each cue presentation,” but the washout period between these stimulation periods was not specified, which could have carryover effects. Furthermore, “opioid craving levels were recorded using a VAS scale compared to baseline level,” which, if I understand correctly, was comparing every craving level in response to stimulation to the level of craving before stimulation began. If that is the case, there could be several confounders, such as carryover effects, patient fatigue, extinction to the cues, etc.\n6. The manuscript includes a structural connectivity analysis comparing the original vs. new stimulation sites (Fig.2c–h). In particular, they indicate that stimulation at the NAc shell was efficacious. Providing a comparison of NAc shell vs. the core would enhance the discussion section. The new, focal stimulation (NAc shell) primarily engaged a circuit involving the subcallosal gyrus (SCg), whereas the original broader stimulation of vALIC had a much larger volume of tissue activation, presumably recruiting additional regions. This finding is notable as the subcallosal gyrus (Brodmann area 25 region) is more commonly known in the context of mood disorders (a common target for depression DBS). Why might connectivity to the subcallosal area be beneficial for opioid craving? Currently, the text simply notes the difference; but readers would benefit from understanding how the connectivity results inform the mechanism of clinical improvement. If there are other notable differences in connectivity (e.g., did the original stimulation engage motor or cognitive networks that the new one spares?) mentioning those would add insight.\nMinor Comments\n- It would be important to note his medications and whether they were changed during postoperatively, especially opioid agonist therapies.\n- There is some inconsistency in naming the blink suppression task. In the Methods it is called the Blink Suppression Task (BST), while earlier it was referred to as the “Urge-For-Action Task (UFAT)”. For clarity, pick one name and acronym and use it throughout (I would recommend “Blink Suppression Task (BST)” since it’s descriptive; or mention both if UFAT is a known term from the literature). Currently, the use of “UFAT” in the Results section without definition could confuse readers. Unifying this will also help ensure the reader understands that the BST is one of the control tasks used to test specificity.\n-In Figure 1, panel (e) and (f) present the power with and without stimulation. The caption notes “red dotted box denotes time period when power level was significantly reduced with stimulation (p=0.039)”\nIt might help to indicate in the figure or caption how that p-value was derived (likely a t-test on 1–7 Hz power during the cue window, stim vs no-stim). Also, in panel (f), the bar plot uses n=7,10,12,12 for conditions – I assume these are the number of trials in each condition. Since the Methods mention 18 trials per condition were planned, clarifying why fewer were analyzed (were some excluded or was this after certain artifact removal?) would be useful. It may be simply that at the time of stimulation condition, more trials were done; if so, stating “(number of trials)” in the caption would avoid any confusion that these are sample sizes.\nFigure 2 caption: The stimulation parameters are listed, e.g., “(1-/2+, 6mA, 90µs, 130Hz)” for the new DBS. However, in the text it appears as “90s” and “70s” for pulse width, presumably a formatting issue where “µs” became “s”. Please correct “90s” to “90 µs” and “70s” to “70 µs” to denote microseconds pulse width. Also, I suggest adding a brief note to the caption or Methods explaining the notation “C+/4-(60%) 5-(40%), 14.5mA…” for the original DBS settings – I interpret this as the case (C) as anode, contacts 4 and 5 as cathodes with 60/40 current split. Not all readers will be familiar with DBS programming notation, so a one-line explanation (perhaps in Online Methods where programming is described) would be helpful.\n- In Fig.2b, the authors show clinical outcome measures (craving frequency/intensity over six months). It would be good to specify what the y-axis and error bars represent (are these self-reported ratings averaged per week? And the error bars variability or range?). Since it’s a single patient, perhaps they tracked craving on a regular basis – describing this in the caption or results ensures readers appreciate the improvement quantitatively.", "reviewer_6": "There is no code available."}, "author_response": "Thank you very much for the opportunity to revise our manuscript. We greatly appreciate the careful reading of our manuscript by the reviewers, and for the constructive feedback given to help us improve the clarity, methodological rigor and overall impact of our work.\nThere were two central themes that emerged across multiple reviewers we would first like to highlight, which have now been addressed in detail in our responses and in the revised manuscript.\nFirst, three reviewers noted our original use of the term “correlation”, which may have inadvertently implied a predictive or causal relationship between neural activity and drug cravings. We would like to clarify that our aim in this case study was to report the observation of an elevated low-frequency power in the nucleus accumbens (NAc) shell specific to drug cue exposure, which was associated with a high craving state and could be effectively modulated with stimulation. It was not our intent to develop a predictive model nor suggest real-time decoding utility, and it would be inappropriate to suggest a formal statistical correlation in this N-of-1 study. We have carefully revised the manuscript to clarify our findings to avoid overinterpretation, and this will also be discussed in detail in the respective responses.\nSecond, we agree with the reviewers on the need for a non-parametric statistical test in our analyses due to small sample size and non-normal distribution of data. In the revision process, we also identified a methodological limitation related to the time window selected in our original time-frequency analysis, which may have constrained the resolution of the low-frequency signal of interest. We conducted a sensitivity analysis and determined that extending the upper bound of the time window (from 3 to 4 seconds) improved the frequency resolution in the low frequency range of interest. This is because we employed a multi-taper wavelet convolution approach where the time window length is inversely related to frequency. We have thus adjusted the analysis parameters accordingly using the new time window, and more accurately identified our time-frequency signal of interest as 1-6Hz range, in the 4.5-5.5 seconds (instead of 1-7Hz, 4.5-6 seconds) post cue presentation based on cluster-corrected permutation testing. We provide the details of these changes in our responses below.\nThese revisions are now reflected throughout the rest of this response, as well as in the updated figures and manuscript. Importantly, these revisions do not alter our original scientific conclusions.\nThe updated analyses support our original findings with improved methodological rigor. We also acknowledge the limitations of our analyses in the revised manuscript.\nIn addition, based on reviewer feedback regarding potential overinterpretation of and the need for clarity, we have revised the title of our manuscript to: \"Localized accumbens cue-electroreactivity informs a case of deep brain stimulation for opioid use disorder\". This revised title aims to reflect the case study nature of our findings more precisely, while staying within the 15-word limit per Nature Communications guidelines. We hope this revision better aligns with the intent of our study and the reviewers' comments. Of course, we remain happy to adjust the title further should you or the reviewers have any additional suggestions.\nBelow, we provide a point-by-point response to each reviewer’s comment. Reviewer comments appear in blue italics, with our responses provided directly below in plain text. All changes to the manuscript are highlighted in yellow."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed my critiques/concerns.", "reviewer_2": "My early career researcher colleague and I thank the authors for their thorough responses to our review. We are satisfied with their responses.\nWe have additional several minor issues we'd appreciate if they could address, and have some formatting edits to suggest.\n1.[Online Methods, Electrophysiological Recording and Signal Analysis]\nThe manuscript states that a “cluster-based permutation test” was applied across the full time–frequency spectrogram, which yielded a statistically significant cluster. However, the description of the statistical procedure is underspecified. In particular, it is not entirely clear whether the test statistics at each time–frequency bin were derived from t-tests (or another metric), and how exactly these values were aggregated to form clusters prior to permutation. Because “cluster-based permutation test” is a general framework that can be implemented in slightly different ways, it would be helpful if the authors could clarify the specific procedure. For example, did they perform pointwise t-tests at each time–frequency bin and then cluster contiguous significant bins, or was a different clustering criterion used? The authors also state that a permutation test was used to analyze the data presented in Figure 1(f). It would also be essential to provide a detailed description of the specific permutation procedure applied in this analysis. This approach differs from the cluster-based permutation test used for spectrogram analysis, and clarifying the distinction will help readers better understand the statistical methodology.\nIn addition, we have the following formatting suggestions:\n3. [Results, Lines 120, 145, 161, 166, 176, 181, 196, 197, 347, 363, 389, 390, Figure 1(e)(f), Figure 2(a)(b), 411, 425, 432, 524, 525, 529, 531, 535, 544] The manuscript currently lacks formatting consistency. For example, sometimes a space is placed between a number and its unit, while in other places it is not. Like Nature’s formatting guide, “units should have a single space between the number and the unit.”\n4. [Results, Lines 174, 196, 201, 355, 367, 371] Please use “vs.” instead of “vs” like line 539.\n5. [Results, Lines 174, 371] Please check the formatting guide and use a consistent style for “p” and “p-value” (either with or without a space, but consistently applied).\n6. [Results, Line 199] When citing references, please list the lower number first (i.e., change 16,12,13 to 12,13,16).\n7. [Discussion, Line 218] While the abbreviation vALIC is explained, ALIC is not. Please provide the full term at its first mention.\n8. [Discussion, Line 229] Please use a comma after “i.e.”\n9. [Figure 1, Line 341] Change “~15-seconds” to “~15-second”.\n10. [Figure 1(b)] Change “10 min stimulation” to “10-minute stimulation”.\n11. [Figure 2(a)] Figure 2(a) might be better presented as a table rather than a figure.\n12. [Figure 2(a)] Why is only “ ‘Definitely feels like a sham’ ” placed in quotation marks?\n13. [Online Methods, Line 426] The abbreviation “vALIC” was already defined in the Results section.\n14. [Online Methods, Line 508] Change “30-seconds” to “30 seconds”.\n15. [Online Methods, Line 522] The abbreviation “iEEG” was already defined in the Introduction section.\n16. [Online Methods, Line 543] Please define the abbreviation “CT” at its first mention.\n17. [Online Methods, Line 545] Change “64-directions” to “64-direction”.\n18. [Online Methods, Line 575] The abbreviation “VAS” was already defined earlier in the manuscript.\n19. [Figure S1(d)(e), S2(b)(c)(d)(f), S3(b)] Please add labels to the color bars to indicate their meaning, as shown in Figure 1(c).\n20. [Figure S1(e)] The label “Time (s)” is cut off. Please improve the figure quality.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I appreciate the authors providing the revised version of the manuscript and the greatly organized response to reviewers’ comments. I believe that the manuscript was improved to a great extent. My previous concerns were addressed appropriately. However, there remain some minor concerns, as follows, that should be addressed to reach an ideal refinement:\nI suggest, but not insist, reordering the figures as they appeared in the text. They are sometimes difficult to follow and may cause confusion.\nIn Fig 1e and 1f, please clearly mention which contact points the average power was calculated form. Was the power, here, averaged across recording sites or across the bouts of stimulation-recording only in contacts 1-2? Please also specify on the figure that the stimulation was focused on contacts 1→2 (or NAc shell). Is there any electrophysiologic data about the effect of stimulation in other sites on the changes in the PSD from contacts 1-2 (similar to what reported about craving in Fig 2)?\nWas the reduction in the low-frequency range (1-6 Hz) the only response occurred by DBS? Any alteration in the power of higher-frequency ranges?\nThe authors did a great job adopting a pre-task, followed by a within-task intermittent stimulation of the NAc. However, why they did not include an interval between the instruction and the cue presentation phase and apply the stimulation during the interval (just before the cue presentation, not after it). In the current design, DBS was on during the time when the patient was rating his desire for the drug. How it would be ruled out that the stimulation affected the rating itself? (I mean rating may not exactly equal craving as the former is a cognitive process and the latter is an emotional/affective process). I am bringing up this concern because previous studies in rodent models indicated an antidromic alteration of neural activity in the orbitofrontal and medial prefrontal cortex during NAc DBS [DOI:10.1523/JNEUROSCI.3750-07.2007] [DOI:10.1523/JNEUROSCI.4804-12.2013]. These areas are essential for determining the subjective value of rewards [DOI: 10.1111/j.1749-6632.2011.06262.x] [DOI: 10.1093/braincomms/fcaa196]. The results of this study also confirmed structural connectivity with SCG (part of the medial frontal cortex).\nStimulation could also be given during the rating phase of BST!\nFig S3. VAS rating for urge-to-blink was recorded in the task but there is no report of it and its correlation with iEEG power.\nI am not totally convinced with the authors’ reasoning about the differences in the timing of the electrophysiological signal (between the cue-reactivity task that gave rise to an increase in 1-6 Hz power and MID that caused an increase in 3-21 Hz band power because they are essentially different task with different designs. However, please include in the discussion a brief paraphrased format of the explanation about the differences in power alteration in cue-reactivity and MID tasks to emphasize the specificity of the electrophysiologic response for drug-related cues (under the title “Temporal and contextual distinction” in response to reviewer file). Maybe it is better to elaborate on this from other aspects also (all reasons indicating the specificity of the response).\nI think that the title still does not reflect the importance of the study. I SUGGEST something like this: “Region-specific accumbens electro-cue-reactivity in a case of opioid use disorder: [followed by one of these:] potential implication to personalized DBS programming / a biomarker (or indicator) of drug craving / alteration (or normalization) by DBS”. (also consider comments by other reviewers)", "reviewer_6": "Summary\nThis manuscript reports a single‑patient case study combining intracranial electrophysiology (iEEG) with a personalized cue‑reactivity paradigm to localize a low‑frequency (≈1–6 Hz) signal in the ventral nucleus accumbens (NAc, putative shell) associated with drug‑cue exposure and self‑reported craving. The authors further show that stimulation targeted to this subregion attenuates both the biomarker and trial‑level craving, reporting sustained clinical improvement.\nSignificance & relation to prior work\nThe opportunity to record NAc activity during personalized drug‑cue exposure in a human with OUD is rare and scientifically valuable. Using such recordings to inform DBS programming is novel and potentially impactful, extending invasive human electrophysiology and biomarker‑guided neuromodulation concepts that have matured in movement disorders and are emerging in psychiatric indications. As a proof‑of‑principle case, this work is promising, especially for the patient population of addiction which requires novel interventions urgently. However, claims should remain appropriately circumscribed to the single‑patient context and framed as hypothesis‑generating for future cohorts.\nOverall assessment of soundness\nThe core approach (personalized cues, iEEG time–frequency analysis, permutation‑based statistics, and targeted stimulation) is generally sound. However, several clarifications and additions are necessary to meet transparency and reproducibility standards and to align claims with the presented evidence.\nComment 1\nWhat was the rationale behind the cue-reactivity design? Was an a priori power calculation completed beforehand for the number of cues to present?\n“Each session included six drug-related cues and six neutral cues, presented in a quasi-randomized block design. To mitigate habituation and ensure adequate washout between sessions, tasks were conducted on alternate days, and no cue-reactivity tasks were performed on days involving stimulation testing. A total of four task sessions were completed: two before any stimulation was initiated and two with active stimulation. Sham stimulation was not included in the cue-reactivity task due to concerns about cue habituation with repeated exposures, which has been shown to reduce neural and behavioral responses over time.”\nComment 2\nPlease provide the craving scores for each of the trials (both drugs and neutral) while also specifying which trials were excluded from the analysis.\nComment 3\nPlease detail further the rapid return of cravings. Did the patient disclose potential etiologies? For instance, the stress and discomfort of 5 cardiac surgeries?\nComment 4\nIt would be helpful to include how long the patient went without stimulation to provide a better understand of the therapeutic effect of the DBS.\nComment 5\nPlease consider updating the duration of clinical outcomes if available.\nComment 6\nPlease clarify whether the patient used opioids in only the USA, Paris, or both as these two statements appear potentially conflicting:\n“Several months after clinical stabilization, he relocated to France, where a local psychiatrist managed his condition and DBS programming.”\n“We were very fortunate in this regard: our patient had always used the same ‘vending machine’ injection paraphernalia (widely available in Paris). The ‘harm-reduction’ videos featuring these paraphernalia and demonstrating safe injection technique, were an exact match to his personal history. They were highly evocative for him despite four years since his last opioid use, and this helped to sustain reactivity across the cue sessions. In contrast, standardized cue sets were less evocative for him, especially because they did not resemble his habits in Paris.”\nComment 7\nThe phrase “multi-taper wavelet convolution” is ambiguous and potentially confusing. Multitaper spectral analysis and wavelet convolution are distinct techniques for time-frequency decomposition. As written, it’s not clear if the authors used a Morlet wavelet transform, a multitaper short-time Fourier transform, or some hybrid approach.\nComment 8\nThe methods section does not report important implementation details of the cluster-based permutation testing, such as the initial cluster-forming threshold or whether the test was one- or two-tailed. These parameters can materially affect the outcome, and their omission makes the analysis difficult to evaluate. Because the entire spectrogram was searched without predefined frequency bands, the analysis is exploratory. While this is acceptable, it increases the importance of clearly stating that significance was assessed at the cluster level only.\nComment 9\nWhy is there a discrepancy the VAS rating scale? It is stated that the “VAS ratings of craving frequency and severity range from 0–10 unless otherwise noted.” But elsewhere it is also stated that “This low-frequency power elevation was observed specifically during presentation of drug-related cues, in comparison to neutral cues, and was associated with an elevated craving state as assessed on a visual analog scale (VAS; range 1-9) (Fig 1f).”\nComment 10\nThe second sentence from the following quotation from manuscript requires a reference:\n“Based on results of personalized tractographic structural connectivity analysis (Fig 2c-h), we found that the re-programmed stimulation site within the NAc shell exhibited strong structural connectivity with the SCG. This suggests the new stimulation site may preferentially engage affective networks involved in interoception, emotional valuation, and compulsive urge regulation.”\nComment 11\nThe term \"cue-electroreactivity” is unfamiliar and confusing in the title: “Localized accumbens cue-electroreactivity informs a case of deep brain stimulation for opioid use disorder.”\nComment 12\nPlease consider supporting the claim of the scalability: “This approach mirrors established paradigms in movement disorders and supports the development of scalable, individualized neuromodulation strategies in future clinical trials, involving lead externalization as utilized here or sensing-enabled devices.”\nComment 13\nPlease consider revising the following point as the article it references does not suggest compulsivity but impulsivity:\n“Notably, there appears to be shared low-frequency, electrographic representation of compulsion within the NAc transdiagnostically that appears to be conserved across species24.”\nComment 14\nFigure S1 and S3 would benefit from showing the time-frequency and multiple permutation plots for all the contact pairs similar to S2.\nComment 15\nFigure 1c. The cluster-based permutation test figures corresponding to drug and neutral cues could be the same to avoid confusion.\nComment 16\nFigure 1d. The contrast between colours could be improved.\nComment 17\nFix typo “Unites States” in the following sentence: “Substance use disorders (SUDs) are a growing public health concern of epidemic proportion, resulting in more than 600,000 deaths in the Unites States annually, with over 100,000 annual deaths in the United States from opioid use disorders (OUD) alone1.”"}, "author_response": "We thank the editor and reviewers for the opportunity to revise our manuscript. We have carefully considered all the comments and have undertaken substantial revisions to address the concerns raised. In our revised submission, we have also removed the supplemental video files as requested, and ensured full compliance with the editorial guidelines, including the use of tracked changes and reference to line numbers (on the original manuscript) throughout the response. We think that the revisions have strengthened the manuscript and look forward to your further recommendation."}, {"round": 3, "reviewer_comments": {"reviewer_4": "I appreciate the authors for providing the revised version of the manuscript. All of my concerns were addressed appropriately.", "reviewer_6": "Thank you for the opportunity to review this work. No further comments from me. Take care."}, "author_response": ""}]

Decision letter