{"paper_id":"b5e915d2-eab4-44fa-bd9f-aad42faccd15","body_text":"Global Increases in Brain Glucose Metabolism Following Acute N,N-Dimethyltryptamine and Harmine Administration in Healthy Volunteers: An [¹⁸F]FDG-PET Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Global Increases in Brain Glucose Metabolism Following Acute N,N-Dimethyltryptamine and Harmine Administration in Healthy Volunteers: An [¹⁸F]FDG-PET Study Klemens Egger, Robert Bozsak, Helena Aicher, Hasan Sari, Sandra Poetzsch, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7099164/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Classical psychedelics such N,N -dimethyltryptamine (DMT), psilocybin, and lysergic acid diethylamide (LSD) modulate consciousness via serotonergic receptor agonism, and are increasingly investigated for their psychotherapeutic potential. When combined with the monoamine oxidase A (MAO-A) inhibitor harmine—mimicking the pharmacological profile of ayahuasca—oral DMT induces a psychedelic experience lasting 4–5 hours. While neuroimaging studies have examined changes in brain activity, connectivity, and cerebral perfusion under psychedelics, their effects on cerebral glucose metabolism remain largely unexplored. Here, we used positron emission tomography with [ 18 F]fluorodeoxyglucose ([¹⁸F]FDG-PET) to assess the cerebral metabolic rate for glucose consumption (CMRglc) following buccal DMT + harmine (90 mg DMT, 120 mg harmine) versus placebo in a single-blind, placebo-controlled, crossover design in (n = 14) healthy males. Scans were acquired during peak drug effects, i.e., 100–170 min post-administration. Global CMRglc increased by 12% under DMT + harmine compared to placebo ( t = 2.57, p < 0.05), with relatively greater activation in the right hemisphere. Vertex- and network-wise analyses revealed widespread cortical increases, with localized effects in the default mode, frontoparietal, and attentional networks. Exploratory correlational analyses found a significant positive correlation between global CMRglc and harmine plasma levels (area under the curve (AUC); r = 0.61, p = 0.021) in the DMT + harmine condition, but not with DMT AUC, subjective intensity ratings, or regional serotonin-2A receptor (5-HT2AR) density derived from a publicly available PET atlas. These findings advance the mechanistic understanding of psychedelics by demonstrating that DMT + harmine increases cerebral glucose metabolism, particularly in higher-order networks, and augment pioneering work indicating increased brain glucose metabolism as a potential metabolic signature of the psychedelic state. Health sciences/Biomarkers/Predictive markers Biological sciences/Neuroscience Biological sciences/Biological techniques Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Serotonergic psychedelics—notably including the classical psychedelics psilocybin, lysergic acid diethylamide (LSD), mescaline, and N,N -dimethyltryptamine (DMT)—are known for their profound ability to alter emotional processing, perception, and self-experience [ 1 ]. There is a general consensus that these effects are primarily mediated by agonist activity at serotonin 2A receptors (5-HT2AR) in cerebral cortex, which play a central role in the modulation of cortical activity and subjective psychedelic experience [ 2 ]. Among the classical psychedelic substances, DMT stands out due to its intense but short-acting effects when administered intravenously [ 3 , 4 ], and is widely known for its traditional use in ayahuasca, a psychoactive decoction with a long history of ceremonial use among indigenous Amazonian cultures, which is drawing increasing attention beyond its traditional context [ 5 ]. Indeed, ayahuasca is showing promise in early clinical trials for the treatment of a range of mental health disorders, including depression, anxiety, post-traumatic stress disorder (PTSD), and substance use disorders, as an important facet of the broader revival of psychedelic-assisted therapies [ 5 – 10 ]. Ayahuasca entails coadministration of DMT with β-carboline monoamine oxidase A inhibitors (MAOIs) such as harmine, which reduce first pass DMT metabolism and thereby synergistically enhance its otherwise very limited oral bioavailability [ 11 , 12 ]. The composition of traditional ayahuasca, which entails a mixture of at least two plants separately containing DMT and MAOIs, has inspired the development of a novel formulation intended to emulate the psychedelic effects of ayahuasca in a controlled clinical setting [ 13 – 15 ], while minimizing the emetic effects and uncertain dosages associated with the plant-derived brew [ 16 ]. Despite the burgeoning clinical interest in ayahuasca and its analogues, there is scant documentation of their effects on brain function. In general, acute administration of psychedelics profoundly alters brain functional dynamics to functional magnetic resonance imaging (fMRI) and magneto- and electroencephalography (MEG and EEG) [ 17 – 20 ]. Such studies have consistently documented functional changes during acute psychedelic states, such as increased global connectivity as marked by greater signal complexity or entropy [ 21 – 25 ], and reduced modular segregation between functional networks [ 26 – 30 ]. These alterations are thought to underlie the dissolution of ego boundaries, vivid imagery, and heightened emotional states often reported during psychedelic experiences [ 17 ]. However, the application of molecular imaging techniques such as positron emission tomography (PET) to study the effects of psychedelics remains scarce. There are very few investigations of how these substances influence cerebral metabolism per se [ 31 ]. To date, only two human studies—both conducted nearly three decades ago with psilocybin—have examined cerebral glucose metabolism using PET with the glucose analogue [ 18 F]fluorodeoxyglucose (FDG). One study reported a global increase in the cerebral metabolic rate of glucose (CMRglc) during the acute psychedelic state [ 32 ], while the other study reported more regionally specific effects, with increases in the right anterior cingulate cortex and frontal operculum and decreases in the thalamus [ 33 ]. Nearly three decades on, there has been no replication study with psilocybin, and no generalization to other psychedelic substances such as DMT or ayahuasca. However, a few studies have investigated the effects of mescaline and ayahuasca on cerebral blood flow via single photon emission computer tomography (SPECT), a molecular imaging method that indirectly reflects neuronal activity and energy demand through perfusion measurements [ 34 – 36 ]. To advance our understanding of the metabolic underpinnings of psychedelic states, we conducted a single-blind, placebo-controlled, within-subject FDG-PET study to assess changes in brain glucose metabolism following administration of a novel oromucosal formulation of DMT combined with harmine [ 13 – 15 ]. Based on the prior findings with psilocybin and the known pharmacodynamic profile of DMT + harmine, we hypothesized a global increase in CMRglc under the active drug condition compared to placebo. In exploratory analyses, we further examined whether global CMRglc correlates with plasma drug concentrations and subjective intensity ratings. We also investigated whether specific cortical regions and resting-state networks exhibit distinct changes in glucose uptake. Lastly, we explored whether the strength of correlations between CMRglc and 5-HT2AR density differed between drug and placebo conditions, using human brain 5-HT2AR distribution maps from a publicly available dataset [ 37 ]. Methods This study was conducted in accordance with the Declaration of Helsinki and International Conference on Harmonization Guidelines in Good Clinical Practice and was approved by the Cantonal Ethics Committees of the Cantons of Bern and Zurich (BASEC-Nr. 2022 − 01515). We received an exemption from the Federal Office of Public Health (FOPH) for the administration of the controlled substance DMT. The study was registered at ClinicalTriails.gov (NCT06252506). All participants provided written informed consent. Participants Twenty healthy male volunteers were initially recruited for the study. Of these, three withdrew after the screening visit and an additional three withdrew after completing the first PET study session due to personal reasons or scheduling conflicts that prevented their participation on the designated study days. Fourteen participants completed both PET study sessions (placebo and verum), and were included in the final analysis (mean age: 31.6 ± 6.1 years; mean body mass index (BMI): 23.1 ± 2.6 kg/m²). Key inclusion criteria included age between 25–45 years and previous experience with psychedelic substances, excluding the preceding three months. Key participant characteristics are shown in Table 1 . Full eligibility (in- and exclusion) criteria are detailed in the supplementary material. No serious adverse events were reported during the study; however, two participants experienced transient nausea accompanied by emesis following DMT + harmine that resolved prior to the PET scan. Table 1 Participant demographics, injected FDG dose and blood glucose concentrations before and after PET scans. Values are presented either as mean + standard deviation or as otherwise specified. Participants N = 14 Age in years (mean (SD)) 31.6 (6.1) Sex Male 14 (100%) Gender Male 13 (93%) Other 1 (7%) Weight in kg (mean (SD)) 76.1 (8.3) BMI (mean (SD)) 23.1 (2.6) Injected FDG dose in MBq Mean (SD) 120 (4.7) Range 114–138 Blood glucose concentrations in mmol/L (mean (SD)) DMT + harmine before scan 4.88 (0.65) DMT + harmine after scan 5.19 (0.68) Placebo before scan 4.66 (0.43) Placebo after scan 4.94 (0.45) Highest education level (%) secondary school degree 1 (7%) high school degree 1 (7%) university degree 10 (72%) other school degree 2 (14%) Years of education (mean (SD)) 16.7 (4.8) Ethnicity White 14 (100%) Previous psychedelic experiences 1–5 3 (21%) 6–10 2 (14%) 11–20 2 (14%) 20–50 3 (21%) > 50 4 (28%) Study design and procedures We implemented a single-blind, placebo-controlled, randomized crossover design. Participants were randomly assigned to receive either DMT + harmine (verum) or placebo in the first session, followed by the alternate condition in the second session. Each participant completed two six-hour PET study days, preceded by a screening visit. Study days were separated by a washout period of at least one week, with most participants having two or more weeks between imaging sessions. Medical screenings were conducted at the University Hospital of Psychiatry Zurich. Study sessions took place at the University Hospital of Bern, in a quiet, dimly lit room adjacent to the PET imaging suite, designed to provide a relaxed and supportive environment. Participants arrived in a fasted state (minimum of four hours before PET scan) to stabilize baseline blood glucose levels. Upon arrival, drug abstinence was verified via a urine drug test (Drug-Screen Multi 12Q Test, Nal von Minden GmbH, Regensburg, Germany). A study physician and one experimenter were present throughout the study day. One of three standardized background music playlists was randomly selected and played during the pre- and post-scan periods on both study days. The study drug (DMT + harmine or placebo orodispersible tablets) was administered buccally in three equal dose increments of 30 mg DMT and 40 mg harmine, each spaced 20 minutes apart to ensure a gradual and smooth transition into the psychedelic state [ 15 ], starting between 09:30 and 11:00 AM. The active condition consisted of a total of 90 mg DMT and 120 mg harmine (both expressed as freebase weight). The formulation and preparation followed previously established protocols (refer to [ 15 ] and Supplementary Material for details). Vital signs (blood pressure and heart rate), venous blood samples for pharmacokinetic analysis of DMT, harmine, and their main metabolites (3-indole acetic acid (3-IAA), DMT- N- oxide (DMT-NO) and harmol), and subjective drug effect ratings (0–10 scale) were collected at multiple timepoints throughout the study day (see Fig. 1 for full schedule). Immediately before and after the PET scan, blood glucose levels were also measured (epoc® Blood Analysis System, Siemens Healthineers AG, Munich, Germany). There were no significant differences in blood glucose levels pre and post scan, or between drug conditions (refer to Table 1 ). Approximately 100 minutes after the first drug dose, participants were transferred to the PET scanner for a ~ 70-minute resting-state acquisition (eyes closed, no music). Participants remained lying on a mattress for most of the time before and after the scan. After the scan, they returned to the study room and were offered a light snack. Toward the end of the study day, participants completed standardized questionnaires assessing their acute psychedelic experience, including the Mystical Experience Questionnaire (MEQ) [ 38 ] and the 5-Dimensional Altered States of Consciousness Questionnaire (5D-ASC) [ 39 ], two questionnaires commonly used in psychedelic research. Discharge occurred approximately 90 to 150 minutes after completion of the PET scan. Imaging data acquisition and preprocessing A T1-weighted structural MR image was obtained at the medical screening visit in Zurich on a 3T MR scanner (Achieva 3.0T, Philips, Amsterdam, The Netherlands) equipped with a 32- channel receive head coil and MultiTransmit parallel radio frequency transmission was used. T1-weigthed MRI images were acquired employing a 3D multishot Turbo Field Echo (TFE) sequence with the following specifications: repetition time (TR) = 8.2 ms, echo time (TE) = 3.8 ms, flip angle = 8°, field-of-view (FoV) = 240×240 mm 2 , slices = 160, no interslice gap, voxel size = 1.0×1.0×1.0 mm 3 , acquisition time = 4.53 min. These images were used as an anatomical template to co-register PET scans to individual brains. PET-CT acquisitions Participants were scanned with a Biograph Vision Quadra (Siemens Healthineers Hoffman Estates, IL, USA) long axial field-of-view (LAFOV) PET scanner. Each subject first received a CT scan from the skull vertex to mid thighs in a single-bed position for PET data attenuation correction. Then, participants received a single intravenous bolus to the medial cubital vein of [ 18 F]-FDG radiotracer (120 ± 4.7 MBq, range: 114–138 MBq). List-mode PET emission data were acquired over 67 min, starting directly after tracer injection. CT images were reconstructed with a voxel size of 1.52×1.52×2.0 mm 3 , and CT-based µ-maps were generated using the bilinear relationship to convert Hounsfield units to voxel-wise attenuation correction factors. List-mode PET emission data was reconstructed into 23 frames (6×20 s, 6×60 s, 2×120 s, 5×300 s, and 4×450 s). PET images were reconstructed in high-sensitivity mode using a 3D OSEM algorithm using a point-spread function–time-of-flight reconstruction algorithm with 4 iterations and 5 subsets. The image matrix was set to 256×256×531 voxels with a voxel size of 1.42×1.42×2.0 mm 3 , and a post-reconstruction gaussian filter with a full width at half maximum of 1.5 mm was applied. Emission data were corrected for decay, randoms, attenuation, and scatter. Image-derived input function (IDIF) extraction To obtain an input function without arterial blood sampling, an image-derived input function (IDIF) was extracted from the aorta using the co-registered CT and PET data, much as in our prior LAFOV [ 18 F]-FDG-PET studies [ 40 , 41 ]. A deep learning-based segmentation method was used to automatically define a volume of interest (VOI) measuring 1 cm in width and 2 cm in height, centered on the descending aorta, using CT images [ 42 ]. The descending aorta mask was resampled and was then applied as a binary mask to the dynamic PET dataset to extract mean activity values within the aorta for each of the 23 reconstructed frames. The resulting IDIF reflects the time-activity curve (TAC) of the [¹⁸F]-FDG tracer concentration in arterial blood, with one value corresponding to each PET frame. No partial volume, motion, or spillover correction was necessary. Preprocessing of MRI and PET data Before preprocessing, all neuroimaging data were set to brain imaging data structure (BIDS) format with Dcm2Bids 3.1.1 [ 43 ]. Then, all images were anonymized using mri_reface 0.3.5 [ 44 , 45 ]. T1w MR image preprocessing was performed using the configurable sMRIPrep 0.17.0 [ 46 , 47 ] pipeline, which included intensity non-uniformity correction, skull-stripping, and spatial normalization to MNI152NLin2009cAsym, MNI305 , and fsaverage space. FDG-PET data were first motion-corrected with the petprep_hmc 0.0.9 [ 48 ] pipeline and then further preprocessed using the petprep_extract_tacs 0.0.5 pipeline [ 49 ]. This included co-registration and spatial normalization of dynamic PET data to fsaverage space. TACs were extracted in fsaverage space and averaged across predefined cortical and subcortical regions of interest (ROIs). Both volume- and surface-based data were smoothed with a 6 mm full-width at half-maximum Gaussian kernel. For the ROI-based TAC extraction, partial volume correction was applied using an adapted geometric transfer matrix (aGTM) method with a starting point-spread function assumption of 3 mm instead of smoothing. Kinetic modeling Kinetic modelling of [ 18 F]FDG-PET data was performed in R (v. 4.2.2, R Foundation, Vienna, Austria) using the kinfitr package (v. 0.8.0) [ 50 ]. We segmented the TACs for brain ROIs using petprep_extract_tacs , and then estimated the magnitude of the unidirectional blood brain clearance ( K in ; ml hg − 1 min − 1 ) by Gjedde-Patlak linear graphic analysis [ 51 ]. Based on a visual inspection on the diagnostic plots generated by kinfitr’s Patlak_tstar function, we used the final ten frames (10–67 min post injection) for the linearization. We excluded the blood volume fraction (vB) parameter as its inclusion did not improve the model fits or change K in estimates. To obtain the cerebral metabolic rate for glucose consumption (CMRglc; µmols glucose hg − 1 min − 1 ) for the ROI- and surface-based analyses, K in values were multiplied by the average of blood glucose concentrations measured before and after each PET recordings, with no lumped constant correction. For surface-based analyses, time-activity curves were fitted using a custom Gjedde-Patlak modeling function implemented in Python. This approach applied the same parameters as used in the ROI-based modeling with kinfitr and was performed for each vertex on the fsaverage surface maps for each individual scan. To obtain network-wise CMRglc values, the resulting vertex-wise CMRglc maps were spatially averaged within each of the seven resting-state networks defined by Yeo et al. [ 52 ]. Additionally, as a complementary analysis, we fitted the TACs from the same ROIs using kinfitr’s twotcm_irr function, which implements the two-tissue compartment model (2TCM) with irreversible binding relative to the IDIF, to estimate the microparameters for unidirectional blood-brain clearance (K₁; ml g − 1 min − 1 ), brain washout fractional rate constant (k₂; min − 1 ), and relative hexokinase activity, i.e., irreversible trapping fractional rate constant (k₃; min − 1 ). Psychometry Acute subjective drug effects were monitored throughout the study days (for time points, see Fig. 1 ) through two single-item based questionnaire versions: 1) a short version assessing “intensity of drug effects” and “challenging drug effects” (i.e., if the content or the quality of the experience difficult to handle or navigate) and 2) a long version, additionally assessing “liking”, “arousal”, “emotionality”, and “visual alterations”. All items were verbally rated on a visual analog scale (VAS) from 0–10 (0 = no effect; 10 = maximal effect). For correlational analyses with global CMRglc, the mean intensity rating across timepoints corresponding to the PET acquisition window (100–180 minutes post-administration) was calculated. Blood sample collections and biochemical plasma analysis Venous blood samples were collected at seven timepoints of each session via a peripheral venous catheter (BD Venflon™ Pro Safety 18G, Becton Dickinson GmbH, Heidelberg, Germany) placed in the median cubital vein, with baseline sample collection just prior to the first drug administration (either placebo or verum), and at 20, 40, 60, 80, 100, and 180 min after first administration (Fig. 1 ). Two additional 2 mL blood samples were collected immediately before and after PET scan start to measure blood glucose concentration for the calculation of CMRglc. The final plasma sample was collected after completion of the PET scan and could therefore not always be obtained at exactly 180 minutes after the first dose (range: 172–245 min; mean: 189 min; median: 186 min): deviations of two minutes per time point were tolerated, but any blood withdrawals exceeding this tolerance range were discarded from analysis (except for the 180-minute final timepoint). Plasma concentrations of DMT, harmine, and their primary metabolites—3-IAA, DMT-NO, and harmol—and serotonin were quantified using an ultra-high-performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) method adapted from an earlier study [ 53 ]. Serotonin levels were included to evaluate the potential MAO-A inhibiting effects of harmine. The Supplementary Material provides detailed information on sample processing and analytical procedures. Pharmacokinetic analysis Given our recent pharmacokinetic/pharmacodynamic (PK/PD) characterization of the DMT + harmine formulation [ 15 ], and given the constraints of blood sampling in the setting of the PET examination, we confined our PK analysis to the calculation of the area under the concentration-time curves from the first to the last measured timepoint (AUC last ) for DMT and harmine for exploratory correlational purposes with global CMRglc values from the DMT + harmine PET scans. We calculated AUC last by non-compartmental analysis in R (v.4.4.0) with the ncappc package (v.0.3.0), as described in our previous publication [ 15 ]. Statistical analyses The primary hypothesis—that global CMRglc would be lower in the drug condition compared to placebo—was tested using a one-sided paired t -test ( p < 0.05). Exploratory Pearson’s correlations were conducted between global CMRglc and AUC last of DMT and harmine, as well as mean subjective intensity during the PET acquisition window, in the DMT + harmine condition ( p < 0.05, uncorrected). Secondary exploratory analyses of regional CMRglc differences were performed using two-sided paired t -tests (uncorrected). Surface-based analyses were conducted using the SLM function for surface-based linear models (BrainStat 0.4.2, [ 54 ]), applying cluster-forming thresholds of p RFT <0.05 and p RFT <0.01 across the whole cortical surface. CMRglc differences between conditions within each of the seven Yeo networks were further assessed by applying a significance threshold of q FDR <0.05. Further exploratory analyses correlated network-based CMRglc with publicly available 5-HT2AR density maps available in fsaverage [ 37 ] by averaging the CMRglc and 5-HT2AR density per network for each scan and then using network-wise Pearson’s correlation for both DMT + harmine and placebo conditions. Differences in correlation coefficients ( r ) were compared using paired t -tests ( q FDR <0.05). All statistical analyses were performed in Python (v.3.12.2) [ 55 ]or R (v.4.4.0) [ 56 ]. Results Global change in CMRglc and associations with plasma drug concentrations and subjective intensity Global CMRglc was significantly higher in the DMT + harmine condition compared to placebo ( t = 2.57, df = 13, p < 0.05, one-sided paired t -test, Cohen’s d ( z -standardized) = 0.64) (Fig. 2 , panel A ). Individual data points and paired lines indicate a consistent increase across participants. There was a 12% global increase in the active condition (CMRglc [µmol hg -1 min -1 ] DMT + harmine = 16.3, placebo = 14.5, mean difference = 1.8). Individual plasma concentration curves of DMT, harmine, their main metabolites 3-IAA, DMT-NO, and harmol, as well as serotonin are shown in Supplement ( Fig. S1 ). DMT, harmine, and metabolite concentrations follow a very similar pattern as reported in [ 15 ], serotonin plasma concentration increases at the last timepoint (180 min) compared to previous timepoints ( Fig. S1 ). Mean subjective acute effect curves are also shown in the supplement ( Fig. S2 ). Correlations between whole-brain CMRglc and pharmacokinetic (DMT and harmine AUC last ) as well as subjective intensity (mean intensity between 100–180 min post drug administration) under DMT + harmine are shown in Fig. 2 , panels B–D . There was a significant positive correlation between global CMRglc and harmine AUC last ( r = 0.62, p = 0.019). Positive but non-significant correlations were found for DMT AUC last ( r = 0.35, p = 0.216), and for mean subjective intensity ratings while participants were in the scanner (i.e., 100–180 min after first DMT + harmine administration ( r = 0.39, p = 0.166). Vertex- and network-wise changes in CMRglc Vertex and network-wise analysis of CMRglc differences between the active and placebo scan conditions revealed significantly increased CMRglc across large parts of the cerebral cortex at p RFT < 0. 05, and in specific regions belonging to the default mode (DMN), frontoparietal (FPN) and salience (SAL) networks, persisting with the more stringent threshold p RFT <0.01 (Fig. 3 , panel A ). Corresponding network-wise analysis of the surface data indicated increased CMRglc in attentional (i.e., dorsal attention (DAN) and SAL networks) and higher-level transmodal networks (i.e., FPN and DMN) (Fig. 3 , panel B ). Associations between network-wise CMRglc and 5-HT2AR density We calculated correlations between CMRglc and 5-HT2AR density (from publicly available maps [ 37 ]) per Yeo network and drug condition. There were no significant differences in correlation scores between placebo and active condition in any network (Fig. 4 ). Discussion In this single-blind, placebo-controlled within-subject FDG-PET study, we investigated the acute effects of a novel oromucosal formulation containing DMT and harmine on cerebral glucose metabolism in healthy participants. This ayahuasca-inspired combination was previously uncharacterized using molecular imaging, and our study provides first-in-human evidence for its metabolic impact. Using Gjedde-Patlak linear graphic analysis of [¹⁸F]FDG uptake with individual IDIFs, we found a significant global increase in CMRglc under DMT + harmine compared to placebo. Complementary surface-based and network-level analyses revealed widespread metabolic increases, particularly within attention and transmodal association cortices of the SAL, FPN, and DMN. These findings suggest that the acute psychedelic state induced by DMT + harmine is associated with globally heightened cerebral energy demand, especially in higher-order cortical networks, and extend prior fMRI observations of DMT and ayahuasca by providing a direct index of neurometabolic activity. The cortical regions showing specifically increased CMRglc (Fig. 2 , panel A , last row) correspond to brain areas that already show the highest CMR glc at rest [ 57 ]. The magnitude of global metabolic enhancement (~ 12%) in this study is comparable to, though slightly lower than, that reported in the only FDG-PET study with psilocybin (~ 20%) during resting state scans, further corroborating the conserved neurometabolic signature of serotonergic psychedelics [ 32 ]. Similar results (global CMRglc increased by ~ 20%, greater increase in frontal regions) have been obtained in an FDG-PET study with the N- methyl-D-aspartate (NMDA) receptor antagonist ketamine, which is often referred to be an “atypical” psychedelic [ 58 ]. Cortical metabolic hyperfrontality was proposed both within these psilocybin and ketamine studies and an earlier SPECT study measuring cerebral blood flow under mescaline that was more pronounced in the right hemisphere in both cases [ 32 , 36 , 58 ]. A similar right-lateralized hypermetabolic frontal pattern was also observed in a SPECT study of healthy individuals following ayahuasca administration [ 34 ], as recapitulated in the present DMT + harmine dataset (ref. Figure 2 , panel A , last two rows). This right-hemisphere predominance aligns with recent theoretical accounts, which suggests a psychedelic-induced loosening of interhemispheric hierarchy and a release of right-hemispheric processes often suppressed during normal waking consciousness [ 59 ]. Given the right hemisphere’s established role in handling cognitive novelty and context-independent behavior [ 60 ], this lateralized pattern may reflect the brain’s engagement with the psychedelic state as a subjectively novel and complex cognitive-emotional landscape. However, such a frontal hypermetabolic pattern was not evident in depressed patients after ayahuasca administration [ 34 ], suggesting that it may be specific to healthy individuals. An earlier autoradiographic study showed dose-dependent decreases in CMRglc in rats treated with either 5-methoxy- N,N -dimethyltryptamine (5-MeO-DMT) or LSD [ 61 ], perhaps reflecting species differences, or differing serotonin receptor selectivities of LSD, 5-MeO-DMT, and DMT [ 62 ]. In a pilot PET study, we did not see any significant effect of low doses of DMT and/or harmine on FDG-uptake in brain of rats [ 63 ], thus further highlighting inconsistencies between pre-clinical and clinical studies. Present findings with DMT + harmine concur with the earlier human studies with psilocybin and ketamine in showing a substantial and global activation of CMRglc relative to the placebo condition [ 32 , 58 ]. In the simplest interpretation, an elevation of CMRglc reflects increased energy metabolism, i.e., neuronal activity. Alternately, it could also arise in relation to a shift in metabolic coupling [ 64 ]. Indeed, DMT altered the expression of mitochondrial membrane-associated proteins in the brain of Alzheimer’s disease model transgenic mice, and altered the physical association of mitochondria with endoplasmic reticulum in vitro, along with restorative effects on oxidative phosphorylation and ATP synthase [ 65 ]. The authors attributed these effects to an action of DMT at intracellular sigma-1 receptors, which might present a mechanism for the present observation of globally increased CMRglc (but might not explain the increases seen earlier with psilocybin). In general, increased glycolysis (i.e, CMRglc to FDG-PET) without a proportional increase in mitochondrial oxidation—known as uncoupling—should lead to elevated lactate production, as occurs during certain sensory stimulation paradigms [ 64 ], which might conceptually also apply to the acute effects of psychedelics. This alternative interpretation, suggestive of altered oxidative stoichiometry (i.e., a reduced oxygen-to-glucose ratio), could be explored in future studies using MR spectroscopy to assess lactate levels and metabolic flux directly, and [ 15 O]-oxygen PET studies to measure the metabolic rate for oxygen. We speculate that the observed increase in glucose metabolism in the DMT + harmine condition may reflect a shift toward a higher-entropy brain state. In thermodynamic terms, increased energy consumption—indexed here by elevated CMRglc—can support a larger number of accessible microstates, reflecting a more disordered, flexible, and less hierarchically constrained neural configuration. Notably, preclinical and in vitro studies have shown that psychedelic compounds can acutely increase neuronal firing rates and cortical excitability, offering a potential mechanism for this elevated metabolic demand [ 66 ]. Psychedelic states are consistently associated with a breakdown of structured functional networks, increased global integration, and greater signal complexity—patterns that have been interpreted as hallmarks of elevated brain entropy [ 21 – 23 , 26 – 30 , 67 ]. These features have been integrated into recent models of psychedelic action, which propose that psychedelics transiently relax the influence of top-down beliefs, allowing for more flexible, bottom-up processing and unusual combinations of percepts, thoughts, and emotions [ 68 ]. CMRglc may index the energetic cost of this transient functional reorganization. Rather than efficient, segregated processing, the brain under psychedelics may shift into a state of widespread, metabolically demanding communication across networks. This reorganization could help disrupt rigid cognitive and emotional patterns, opening the way for novel perspectives and insights [ 67 ]. Crucially, such entropic brain states may not only explain the altered conscious experience but also underpin therapeutic effects, by expanding the brain’s dynamic range and weakening entrenched activity patterns—especially in conditions marked by cognitive or emotional rigidity [ 69 ]. In an exploratory analysis, we examined how normative binding potential maps (BP ND ) for the 5-HT2AR agonist ligand [ 11 C]Cimbi-36 from an independent data set [ 37 ] might relate to CMRglc patterns across Yeo networks, notwithstanding caveats arising from such a comparison [ 70 ]. Nonetheless, CMRglc showed a moderate correlation with [ 11 C]Cimbi-36 BP ND in both the placebo and DMT + harmine conditions; the similarity in correlation coefficients across conditions suggests that receptor distribution alone does not explain the acute cerebrometabolic effects of the drug administration. The local 5-HT2AR density may possibly serve as a proxy for broader structural properties such as cortical thickness, which co-varies with both neuroreceptor expression and metabolic rate [ 71 , 72 ]. This could explain why frontal and transmodal areas—characterized by both high 5-HT2AR density and cortical thickness—exhibited stronger metabolic effects in the DMT + harmine condition. Based on our previous pharmacokinetic study with this formulation, we had selected an intermediate DMT + harmine dose and the 100–180 min post-administration window for PET acquisition, a time corresponding to peak plasma concentrations and subjective effects at the administered dose [ 15 ]. Plasma and subjective intensity curves from the current participant group (see Supplement) support this timing. However, both DMT and harmine showed slightly lower plasma concentrations and faster clearance compared to our earlier findings, potentially due to the all-male sample in this study, in consideration that males typically exhibit faster first pass drug metabolism and hepatic clearance (e.g., via CYP450 and CYP2D6 enzymes) [ 11 , 73 ]. Notably, we observed a ~ 50% increase in plasma serotonin concentrations three hours after DMT + harmine administration relative to earlier timepoints, doubtless reflecting the inhibition of MAO-A in peripheral tissues. Given preclinical findings with reversible MAO-A inhibitors [ 74 ], and behavioral associations of plasma serotonin levels [ 75 ], we can infer that the present treatment likely also increased brain serotonin levels. This suggests a model wherein psychedelic effects of exogenous DMT (as in ayahuasca) occur in conjunction with a potentiation of serotonergic signaling due to inhibition of brain MAO-A, as distinct from the potentiation of DMT brain uptake via inhibition of peripheral MAO-A. Global CMRglc under DMT + harmine correlated significantly with the AUC for harmine, but (unexpectedly) not for the AUCs for DMT or subjective intensity. While this finding might suggest a primary role for harmine in modulating glucose metabolism, we believe these correlation findings should be interpreted with caution. It does not follow necessarily from the observed correlations that harmine is the driver for the observed increase in brain metabolism. In a recent study using this same drug formulation, we observed strong correlations between the individual DMT and harmine AUCs, and saw similar temporal patterns for the plasma drug concentrations and the subjective effects [ 15 ]. Examination of Fig. 2 suggests that the present study was underpowered to detect such a significant correlation for DMT. Alternately, we note that our study protocol was primarily optimized to assess CMRglc rather than to capture with high precision the full pharmacokinetic profiles of DMT and harmine. Moreover, our own pilot FDG-PET study in rats found only a small change in glucose metabolism after low-dose harmine administration in the thalamus compared to placebo [ 63 ], and the broader literature remains sparse regarding direct metabolic effects of harmine on the brain. Given that harmine’s primary pharmacological role in this context is presumably to inhibit MAO-A (although it may have other actions in the context of ayahuasca [ 11 ]) and thereby enable oral DMT bioavailability [ 15 ], we consider it unlikely that harmine alone contributes importantly to the observed global CMRglc increase. This holds especially in consideration that harmine alone does not induce psychedelic effects, but possesses a distinct psychoactive profile with different or even opposed characteristics to those typically observed with serotonergic psychedelics [ 76 ]. Limitations We employed a single-blind, within-subject, placebo-controlled design, providing strong sensitivity and robustness for detecting the hypothesized drug-induced CMRglc changes. However, we note several limitations of the study. While the sample size sufficed to detect global and regional CMRglc changes, it was relatively small for the exploratory correlational analyses, especially those involving pharmacokinetics, which were further affected by high interindividual variability in drug disposition, as previously reported [ 15 ]. Additionally, the study design was not optimized for detailed pharmacokinetic profiling, as the blood sampling schedule lacked sufficient resolution to capture complete AUCs (i.e., during the PET recordings). The sample consisted exclusively of healthy, white, male participants, which limits the generalizability of our findings. Blinding efficacy was limited; most participants correctly identified their treatment condition by the second study day, reflecting a common challenge in psychedelic research [ 77 , 78 ]. We opted for an inert placebo to enhance neuroimaging contrasts, at the expense of effective blinding. Finally, we used an IDIF instead of the more conventional arterial input function (AIF) for CMRglc quantification, which might have biased the evaluation of CMRglc. However, in a recent study, there was a considerable degree of concordance between IDIF- and AIF-based analyses of CMRglc [ 79 ]. In effect, the advent of LAFOV PET scanners enable recovery of the FDG signal from large vascular structures, such as the aorta in the present study, without penalty in accuracy due to spillover effects of heart motion [ 40 , 41 ]. Conclusion Our findings demonstrate that acute administration of a novel oromucosal DMT + harmine formulation induces a robust global increase in cerebral glucose metabolism, with particularly strong effects in attentional and higher-order transmodal networks. These metabolic changes may reflect a distinct brain state characterized by globally heightened glucose metabolism, which is generally held to reflect increased neuronal activity [ 80 ]. However, we cannot exclude the possibility that the increased CMRglc reflects mitochondrial uncoupling, rather than increased metabolic activity per se. On the other hand, the spatial pattern of CMRglc increases under DMT + harmine appears consistent with a shift toward a more entropic and less hierarchically constrained brain state. Such a configuration may support the breakdown of entrenched patterns of neural activity, promoting cognitive and emotional flexibility. The right hemisphere predominance of the increased CMRglc may be in accord with a recent model of psychedelic action involving a change in hemispheric hierarchy. Future studies should aim to establish the causal mechanism whereby this drug formation stimulates brain glucose metabolism, and to establish better the contribution of 5-HT2AR agonism to the cerebrometabolic and subjective effects of DMT + harmine. Declarations Clinical trial registry name and URL incl. registration number: Molecular Imaging Study of Harmine/DMT: a Basic Research Approach (HaD-PET) https://clinicaltrials.gov/study/NCT06252506 Acknowledgements The authors thank study physicians Jovin Müller and Sarah Njoh for their medical support and screening of participants, the medical imaging personnel at the study site in Bern, namely Marco Viscione, Ângela Mendes, Ângelo Felgosa Cardoso, and Janneke Henniphoffor conducting the PET scans, Céline Birrer and Franziska Strunz for their administrative support, Robin von Rotz for coordinating with the database provider, and John Smallridge for sharing analysis scripts for pharmacokinetic analyses. Funding This work was supported by the Swiss National Science Foundation (Grant Number 320030-204978) awarded to Professor Cumming. Author Contributions Klemens Egger: Conceptualization, Data curation, Formal analysis, Investigation, Project administration, Visualization, Writing – Original draft, Writing – Review and editing. Robert Bozsak: Investigation, Project administration, Writing – Review and editing. Helena D. Aicher: Investigation, Writing – Review and editing. Hasan Sari: Resources, Methodology, Writing – Review and editing. Sandra N. Poetzsch: Formal analysis, Writing – Review and editing. Axel Rominger: Resources, Writing – Review and editing. Chantal Martin-Soelch: Conceptualization. Dario Dornbierer: Resources. Boris B. Quednow: Conceptualization, Writing – Review and editing. Milan Scheidegger: Conceptualization, Writing – Review and editing. Paul Cumming: Conceptualization, Funding acquisition, Methodology, Writing – Review and editing. Conflicts of Interests KE, RB, HAD, HS, SNP, AR, CM-S, BBQ, MS, PC have nothing to declare. DD and MS declare that they co-founded Reconnect Labs AG, an academic spin-off at the University of Zurich, focused on the development of psychedelic medicines for mental health. Data statement Imaging data related to this project will be made available in an online repository. Additional information is available upon reasonable request. References Nichols DE. Psychedelics. Pharmacol Rev. 2016;68:264–355. Vollenweider FX, Kometer M. The neurobiology of psychedelic drugs: implications for the treatment of mood disorders. Nat Rev Neurosci. 2010;11:642–651. Vogt SB, Ley L, Erne L, Straumann I, Becker AM, Klaiber A, et al. Acute effects of intravenous DMT in a randomized placebo-controlled study in healthy participants. Transl Psychiatry. 2023;13:172. Luan LX, Eckernäs E, Ashton M, Rosas FE, Uthaug M V, Bartha A, et al. Psychological and physiological effects of extended DMT. Journal of Psychopharmacology. 2023. 28 October 2023. https://doi.org/10.1177/02698811231196877 . 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DD and MS declare that they co-founded Reconnect Labs AG, an academic spin-off at the University of Zurich, focused on the development of psychedelic medicines for mental health. Supplementary Files HDPSupplement.pdf Supplementary material Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Poetzsch\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sandra\",\"middleName\":\"\",\"lastName\":\"Poetzsch\",\"suffix\":\"\"},{\"id\":488262887,\"identity\":\"eee705ed-b4fd-47fb-96dc-7e9303005471\",\"order_by\":5,\"name\":\"Axel Rominger\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-1954-736X\",\"institution\":\"Inselspital, University of Bern\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Axel\",\"middleName\":\"\",\"lastName\":\"Rominger\",\"suffix\":\"\"},{\"id\":488262888,\"identity\":\"318495d5-9ca5-4e3f-b783-27468fe166a3\",\"order_by\":6,\"name\":\"Chantal Martin-Soelch\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-3859-9023\",\"institution\":\"University of Fribourg\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chantal\",\"middleName\":\"\",\"lastName\":\"Martin-Soelch\",\"suffix\":\"\"},{\"id\":488262889,\"identity\":\"909622ae-3bfe-4ed2-8bb8-3a901b64d661\",\"order_by\":7,\"name\":\"Dario Dornbierer\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Dario\",\"middleName\":\"\",\"lastName\":\"Dornbierer\",\"suffix\":\"\"},{\"id\":488262890,\"identity\":\"6a3d05dd-294c-4093-a5b3-d85097e52a6d\",\"order_by\":8,\"name\":\"Boris Quednow\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0001-7933-2865\",\"institution\":\"University of Zurich\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Boris\",\"middleName\":\"\",\"lastName\":\"Quednow\",\"suffix\":\"\"},{\"id\":488262891,\"identity\":\"e0d527b1-b770-433c-bc46-219a36861e2e\",\"order_by\":9,\"name\":\"Milan Scheidegger\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-1313-2208\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Milan\",\"middleName\":\"\",\"lastName\":\"Scheidegger\",\"suffix\":\"\"},{\"id\":488262892,\"identity\":\"6a63ddb8-1d95-43c7-b573-a43a04333ff7\",\"order_by\":10,\"name\":\"Paul Cumming\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-0257-9621\",\"institution\":\"Queensland University of Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Paul\",\"middleName\":\"\",\"lastName\":\"Cumming\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-07-11 07:55:31\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7099164/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7099164/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":87665518,\"identity\":\"1bce1e8a-4c4d-497b-8803-c56d894a2d5b\",\"added_by\":\"auto\",\"created_at\":\"2025-07-27 11:04:20\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":148729,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eStudy procedures on each study day. Before the first drug administration, baseline vital signs (BP/HR=blood pressure / heart rate), blood samples and psychometrics were assessed. The study drug was administered in three tablets of same strength at 20 minutes intervals. Vital signs, and psychometry were assessed in regular intervals with the last time 5 hours after first administration. Blood samples were drawn at 20-minute intervals before start of the PET scan and once more after the PET scan, to a total of seven blood samples per study day.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7099164/v1/7be50d99be0b51614bf39365.png\"},{\"id\":87665517,\"identity\":\"31fad9f7-bbb1-4eb8-bf11-d70f300d39c7\",\"added_by\":\"auto\",\"created_at\":\"2025-07-27 11:04:20\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":978454,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eWhole-brain CMRglc increases under DMT+harmine, and associations with subjective and blood plasma concentrations of DMT and harmine. A|\\u003c/strong\\u003e Violin plot showing whole-brain CMRglc (µmol/100g/min) for each subject under placebo (gray) and DMT+harmine (yellow) conditions. Individual data points and paired lines indicate within-subject changes. A one-tailed paired \\u003cem\\u003et\\u003c/em\\u003e-test shows a statistically significant increase in whole-brain CMRglc in the DMT+harmine condition compared to placebo (\\u003cem\\u003et\\u003c/em\\u003e=2.57, \\u003cem\\u003edf\\u003c/em\\u003e=13, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.05). \\u003cstrong\\u003eB–D|\\u003c/strong\\u003e Scatter plots showing correlations between whole-brain CMRglc in the DMT+harmine condition and (B) mean subjective intensity ratings during the PET scan (i.e., 100–180 min after first administration), (C) DMT exposure (AUC\\u003csub\\u003elast\\u003c/sub\\u003e), and (D) harmine exposure (AUC\\u003csub\\u003elast\\u003c/sub\\u003e). A significant positive correlation is observed with harmine AUC\\u003csub\\u003elast\\u003c/sub\\u003e (\\u003cem\\u003er\\u003c/em\\u003e=0.62, \\u003cem\\u003ep\\u003c/em\\u003e=0.019), while associations with subjective intensity (\\u003cem\\u003er\\u003c/em\\u003e=0.39, \\u003cem\\u003ep\\u003c/em\\u003e=0.166) and DMT AUC\\u003csub\\u003elast\\u003c/sub\\u003e (\\u003cem\\u003er\\u003c/em\\u003e=0.35, \\u003cem\\u003ep\\u003c/em\\u003e=0.216) are positive but not statistically significant. Shaded areas represent 95% confidence intervals. AUC\\u003csub\\u003elast\\u003c/sub\\u003e indicates the area under the time-concentration curve from the first to the last collected blood plasma sample.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7099164/v1/b166e6d02744a238779d1606.png\"},{\"id\":87664102,\"identity\":\"7802a579-13ad-4bc4-87fa-29ee0681e4bc\",\"added_by\":\"auto\",\"created_at\":\"2025-07-27 10:56:20\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2224491,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSurface-based differences in CMRglc between the DMT+harmine and placebo conditions. A|\\u003c/strong\\u003e CMRglc mapped onto the \\u003cem\\u003efsaverage\\u003c/em\\u003e brain surface for the DMT+harmine (top row) and placebo (second row) conditions. Statistical maps showing \\u003cem\\u003et\\u003c/em\\u003e-values from two-tailed paired \\u003cem\\u003et\\u003c/em\\u003e-tests (DMT+harmine vs. placebo) are presented at two significance thresholds: third row, \\u003cem\\u003ep\\u003c/em\\u003e\\u003csub\\u003eRFT\\u003c/sub\\u003e\\u0026lt;0.05; fourth row, \\u003cem\\u003ep\\u003c/em\\u003e\\u003csub\\u003eRFT\\u003c/sub\\u003e\\u0026lt;0.01. At \\u003cem\\u003ep\\u003c/em\\u003e\\u003csub\\u003eRFT\\u003c/sub\\u003e\\u0026lt;0.05, widespread cortical increases in CMRglc are observed under the active condition, particularly in regions associated with attentional and higher-order cognitive networks. At the more stringent threshold of \\u003cem\\u003ep\\u003c/em\\u003e\\u003csub\\u003eRFT\\u003c/sub\\u003e\\u0026lt;0.01, significant increases are mainly localized within the DMN, FPN and SAL. \\u003cstrong\\u003eB|\\u003c/strong\\u003e Network-wise averaged comparisons confirm the vertex-wise results, showing significantly increased CMRglc (\\u003cem\\u003eq\\u003c/em\\u003e\\u003csub\\u003eFDR\\u003c/sub\\u003e\\u0026lt;0.05) in the DAN, SAL, FPN, and DMN during the DMT+harmine scans. \\u003cstrong\\u003eC|\\u003c/strong\\u003e Histograms illustrating the vertex-wise distribution of CMRglc values for DMT+harmine (yellow) and placebo (gray) conditions. Dashed vertical lines represent the mean value for each condition. Abbreviations: VIS – visual network, SMN –\\u003cem\\u003e \\u003c/em\\u003esensorimotor network, DAN – dorsal attention network, SAL – salience network, FPN – frontoparietal network, DMN – default mode network.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7099164/v1/9d40581f1123a40b519534d1.png\"},{\"id\":87664100,\"identity\":\"9baf461d-b77f-4636-be70-762fc8be5ed7\",\"added_by\":\"auto\",\"created_at\":\"2025-07-27 10:56:20\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2276157,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAssociations between network-wise CMRglc and serotonin 5-HT2A receptor density. \\u003c/strong\\u003eViolin plots showing the distribution of Pearson correlation coefficients (\\u003cem\\u003er\\u003c/em\\u003e) between CMRglc and 5-HT2A receptor density across the seven Yeo networks for each participant under placebo (gray) and DMT+harmine (yellow) conditions. The regional 5-HT2A receptor densities derive from an atlas for heathy human brain [37]. Individual data points and connecting lines represent within-subject differences. No significant differences in correlation strength were observed between conditions in any of the networks (all \\u003cem\\u003eq\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eFDR\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026gt;0.05). Abbreviations: VIS – visual network, SMN – sensorimotor network, DAN – dorsal attention network, SAL – salience network, LIM – limbic network, FPN – frontoparietal network, DMN – default mode network.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7099164/v1/806eb3c21b7c9ccd55d1fda5.png\"},{\"id\":96917757,\"identity\":\"d9d21a4d-e5fd-43a8-bcde-735277b3018b\",\"added_by\":\"auto\",\"created_at\":\"2025-11-27 14:10:31\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":6611220,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7099164/v1/927e21b2-b78d-406a-b668-12498dfb0a58.pdf\"},{\"id\":87666150,\"identity\":\"5bd730dd-2bc3-4235-81b0-c5cb26364ba1\",\"added_by\":\"auto\",\"created_at\":\"2025-07-27 11:12:20\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1261777,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplementary material\",\"description\":\"\",\"filename\":\"HDPSupplement.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7099164/v1/b73f7f9de43ff6eec833a809.pdf\"}],\"financialInterests\":\"\\u003cb\\u003eYes\\u003c/b\\u003e\\nKE, RB, HAD, HS, SNP, AR, CM-S, BBQ, MS, PC have nothing to declare. DD and MS declare that they co-founded Reconnect Labs AG, an academic spin-off at the University of Zurich, focused on the development of psychedelic medicines for mental health.\",\"formattedTitle\":\"Global Increases in Brain Glucose Metabolism Following Acute N,N-Dimethyltryptamine and Harmine Administration in Healthy Volunteers: An [¹⁸F]FDG-PET Study\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eSerotonergic psychedelics—notably including the classical psychedelics psilocybin, lysergic acid diethylamide (LSD), mescaline, and \\u003cem\\u003eN,N\\u003c/em\\u003e-dimethyltryptamine (DMT)—are known for their profound ability to alter emotional processing, perception, and self-experience [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. There is a general consensus that these effects are primarily mediated by agonist activity at serotonin 2A receptors (5-HT2AR) in cerebral cortex, which play a central role in the modulation of cortical activity and subjective psychedelic experience [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Among the classical psychedelic substances, DMT stands out due to its intense but short-acting effects when administered intravenously [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e], and is widely known for its traditional use in ayahuasca, a psychoactive decoction with a long history of ceremonial use among indigenous Amazonian cultures, which is drawing increasing attention beyond its traditional context [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Indeed, ayahuasca is showing promise in early clinical trials for the treatment of a range of mental health disorders, including depression, anxiety, post-traumatic stress disorder (PTSD), and substance use disorders, as an important facet of the broader revival of psychedelic-assisted therapies [\\u003cspan additionalcitationids=\\\"CR6 CR7 CR8 CR9\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eAyahuasca entails coadministration of DMT with β-carboline monoamine oxidase A inhibitors (MAOIs) such as harmine, which reduce first pass DMT metabolism and thereby synergistically enhance its otherwise very limited oral bioavailability [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. The composition of traditional ayahuasca, which entails a mixture of at least two plants separately containing DMT and MAOIs, has inspired the development of a novel formulation intended to emulate the psychedelic effects of ayahuasca in a controlled clinical setting [\\u003cspan additionalcitationids=\\\"CR14\\\" citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], while minimizing the emetic effects and uncertain dosages associated with the plant-derived brew [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eDespite the burgeoning clinical interest in ayahuasca and its analogues, there is scant documentation of their effects on brain function. In general, acute administration of psychedelics profoundly alters brain functional dynamics to functional magnetic resonance imaging (fMRI) and magneto- and electroencephalography (MEG and EEG) [\\u003cspan additionalcitationids=\\\"CR18 CR19\\\" citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Such studies have consistently documented functional changes during acute psychedelic states, such as increased global connectivity as marked by greater signal complexity or entropy [\\u003cspan additionalcitationids=\\\"CR22 CR23 CR24\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e], and reduced modular segregation between functional networks [\\u003cspan additionalcitationids=\\\"CR27 CR28 CR29\\\" citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. These alterations are thought to underlie the dissolution of ego boundaries, vivid imagery, and heightened emotional states often reported during psychedelic experiences [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. However, the application of molecular imaging techniques such as positron emission tomography (PET) to study the effects of psychedelics remains scarce. There are very few investigations of how these substances influence cerebral metabolism per se [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. To date, only two human studies—both conducted nearly three decades ago with psilocybin—have examined cerebral glucose metabolism using PET with the glucose analogue [\\u003csup\\u003e18\\u003c/sup\\u003eF]fluorodeoxyglucose (FDG). One study reported a global increase in the cerebral metabolic rate of glucose (CMRglc) during the acute psychedelic state [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e], while the other study reported more regionally specific effects, with increases in the right anterior cingulate cortex and frontal operculum and decreases in the thalamus [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Nearly three decades on, there has been no replication study with psilocybin, and no generalization to other psychedelic substances such as DMT or ayahuasca. However, a few studies have investigated the effects of mescaline and ayahuasca on cerebral blood flow via single photon emission computer tomography (SPECT), a molecular imaging method that indirectly reflects neuronal activity and energy demand through perfusion measurements [\\u003cspan additionalcitationids=\\\"CR35\\\" citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eTo advance our understanding of the metabolic underpinnings of psychedelic states, we conducted a single-blind, placebo-controlled, within-subject FDG-PET study to assess changes in brain glucose metabolism following administration of a novel oromucosal formulation of DMT combined with harmine [\\u003cspan additionalcitationids=\\\"CR14\\\" citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Based on the prior findings with psilocybin and the known pharmacodynamic profile of DMT + harmine, we hypothesized a global increase in CMRglc under the active drug condition compared to placebo. In exploratory analyses, we further examined whether global CMRglc correlates with plasma drug concentrations and subjective intensity ratings. We also investigated whether specific cortical regions and resting-state networks exhibit distinct changes in glucose uptake. Lastly, we explored whether the strength of correlations between CMRglc and 5-HT2AR density differed between drug and placebo conditions, using human brain 5-HT2AR distribution maps from a publicly available dataset [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e].\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e This study was conducted in accordance with the Declaration of Helsinki and International Conference on Harmonization Guidelines in Good Clinical Practice and was approved by the Cantonal Ethics Committees of the Cantons of Bern and Zurich (BASEC-Nr. 2022 − 01515). We received an exemption from the Federal Office of Public Health (FOPH) for the administration of the controlled substance DMT. The study was registered at ClinicalTriails.gov (NCT06252506). All participants provided written informed consent.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eParticipants\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eTwenty healthy male volunteers were initially recruited for the study. Of these, three withdrew after the screening visit and an additional three withdrew after completing the first PET study session due to personal reasons or scheduling conflicts that prevented their participation on the designated study days. Fourteen participants completed both PET study sessions (placebo and verum), and were included in the final analysis (mean age: 31.6 ± 6.1 years; mean body mass index (BMI): 23.1 ± 2.6 kg/m²). Key inclusion criteria included age between 25–45 years and previous experience with psychedelic substances, excluding the preceding three months. Key participant characteristics are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Full eligibility (in- and exclusion) criteria are detailed in the supplementary material. No serious adverse events were reported during the study; however, two participants experienced transient nausea accompanied by emesis following DMT + harmine that resolved prior to the PET scan.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eParticipant demographics, injected FDG dose and blood glucose concentrations before and after PET scans.\\u003c/b\\u003e Values are presented either as mean + standard deviation or as otherwise specified.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"2\\\"\\u003e\\u003c/colgroup\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eParticipants\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eN = 14\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eAge in years (mean (SD))\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e31.6 (6.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eSex\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14 (100%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eGender\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e13 (93%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOther\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1 (7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eWeight in kg (mean (SD))\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e76.1 (8.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eBMI (mean (SD))\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e23.1 (2.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInjected FDG dose in MBq\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMean (SD)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e120 (4.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRange\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e114–138\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eBlood glucose concentrations in mmol/L (mean (SD))\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDMT + harmine before scan\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.88 (0.65)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDMT + harmine after scan\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.19 (0.68)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePlacebo before scan\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.66 (0.43)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePlacebo after scan\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.94 (0.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eHighest education level (%)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003esecondary school degree\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1 (7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ehigh school degree\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1 (7%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003euniversity degree\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10 (72%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eother school degree\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2 (14%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eYears of education (mean (SD))\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e16.7 (4.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eEthnicity\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWhite\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14 (100%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePrevious psychedelic experiences\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1–5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3 (21%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e6–10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2 (14%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e11–20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2 (14%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e20–50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3 (21%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026gt; 50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4 (28%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eStudy design and procedures\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eWe implemented a single-blind, placebo-controlled, randomized crossover design. Participants were randomly assigned to receive either DMT + harmine (verum) or placebo in the first session, followed by the alternate condition in the second session. Each participant completed two six-hour PET study days, preceded by a screening visit. Study days were separated by a washout period of at least one week, with most participants having two or more weeks between imaging sessions.\\u003c/p\\u003e\\u003cp\\u003eMedical screenings were conducted at the University Hospital of Psychiatry Zurich. Study sessions took place at the University Hospital of Bern, in a quiet, dimly lit room adjacent to the PET imaging suite, designed to provide a relaxed and supportive environment. Participants arrived in a fasted state (minimum of four hours before PET scan) to stabilize baseline blood glucose levels. Upon arrival, drug abstinence was verified via a urine drug test (Drug-Screen Multi 12Q Test, Nal von Minden GmbH, Regensburg, Germany). A study physician and one experimenter were present throughout the study day. One of three standardized background music playlists was randomly selected and played during the pre- and post-scan periods on both study days.\\u003c/p\\u003e\\u003cp\\u003eThe study drug (DMT + harmine or placebo orodispersible tablets) was administered buccally in three equal dose increments of 30 mg DMT and 40 mg harmine, each spaced 20 minutes apart to ensure a gradual and smooth transition into the psychedelic state [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], starting between 09:30 and 11:00 AM. The active condition consisted of a total of 90 mg DMT and 120 mg harmine (both expressed as freebase weight). The formulation and preparation followed previously established protocols (refer to [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e] and Supplementary Material for details).\\u003c/p\\u003e\\u003cp\\u003eVital signs (blood pressure and heart rate), venous blood samples for pharmacokinetic analysis of DMT, harmine, and their main metabolites (3-indole acetic acid (3-IAA), DMT-\\u003cem\\u003eN-\\u003c/em\\u003eoxide (DMT-NO) and harmol), and subjective drug effect ratings (0–10 scale) were collected at multiple timepoints throughout the study day (see Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e for full schedule). Immediately before and after the PET scan, blood glucose levels were also measured (epoc® Blood Analysis System, Siemens Healthineers AG, Munich, Germany). There were no significant differences in blood glucose levels pre and post scan, or between drug conditions (refer to Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Approximately 100 minutes after the first drug dose, participants were transferred to the PET scanner for a ~ 70-minute resting-state acquisition (eyes closed, no music). Participants remained lying on a mattress for most of the time before and after the scan. After the scan, they returned to the study room and were offered a light snack. Toward the end of the study day, participants completed standardized questionnaires assessing their acute psychedelic experience, including the Mystical Experience Questionnaire (MEQ) [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e] and the 5-Dimensional Altered States of Consciousness Questionnaire (5D-ASC) [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e], two questionnaires commonly used in psychedelic research. Discharge occurred approximately 90 to 150 minutes after completion of the PET scan.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eImaging data acquisition and preprocessing\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eA T1-weighted structural MR image was obtained at the medical screening visit in Zurich on a 3T MR scanner (Achieva 3.0T, Philips, Amsterdam, The Netherlands) equipped with a 32- channel receive head coil and MultiTransmit parallel radio frequency transmission was used. T1-weigthed MRI images were acquired employing a 3D multishot Turbo Field Echo (TFE) sequence with the following specifications: repetition time (TR) = 8.2 ms, echo time (TE) = 3.8 ms, flip angle = 8°, field-of-view (FoV) = 240×240 mm\\u003csup\\u003e2\\u003c/sup\\u003e, slices = 160, no interslice gap, voxel size = 1.0×1.0×1.0 mm\\u003csup\\u003e3\\u003c/sup\\u003e, acquisition time = 4.53 min. These images were used as an anatomical template to co-register PET scans to individual brains.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003ePET-CT acquisitions\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eParticipants were scanned with a Biograph Vision Quadra (Siemens Healthineers Hoffman Estates, IL, USA) long axial field-of-view (LAFOV) PET scanner. Each subject first received a CT scan from the skull vertex to mid thighs in a single-bed position for PET data attenuation correction. Then, participants received a single intravenous bolus to the medial cubital vein of [\\u003csup\\u003e18\\u003c/sup\\u003eF]-FDG radiotracer (120 ± 4.7 MBq, range: 114–138 MBq). List-mode PET emission data were acquired over 67 min, starting directly after tracer injection.\\u003c/p\\u003e\\u003cp\\u003eCT images were reconstructed with a voxel size of 1.52×1.52×2.0 mm\\u003csup\\u003e3\\u003c/sup\\u003e, and CT-based µ-maps were generated using the bilinear relationship to convert Hounsfield units to voxel-wise attenuation correction factors. List-mode PET emission data was reconstructed into 23 frames (6×20 s, 6×60 s, 2×120 s, 5×300 s, and 4×450 s). PET images were reconstructed in high-sensitivity mode using a 3D OSEM algorithm using a point-spread function–time-of-flight reconstruction algorithm with 4 iterations and 5 subsets. The image matrix was set to 256×256×531 voxels with a voxel size of 1.42×1.42×2.0 mm\\u003csup\\u003e3\\u003c/sup\\u003e, and a post-reconstruction gaussian filter with a full width at half maximum of 1.5 mm was applied. Emission data were corrected for decay, randoms, attenuation, and scatter.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eImage-derived input function (IDIF) extraction\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eTo obtain an input function without arterial blood sampling, an image-derived input function (IDIF) was extracted from the aorta using the co-registered CT and PET data, much as in our prior LAFOV [\\u003csup\\u003e18\\u003c/sup\\u003eF]-FDG-PET studies [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. A deep learning-based segmentation method was used to automatically define a volume of interest (VOI) measuring 1 cm in width and 2 cm in height, centered on the descending aorta, using CT images [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. The descending aorta mask was resampled and was then applied as a binary mask to the dynamic PET dataset to extract mean activity values within the aorta for each of the 23 reconstructed frames. The resulting IDIF reflects the time-activity curve (TAC) of the [¹⁸F]-FDG tracer concentration in arterial blood, with one value corresponding to each PET frame. No partial volume, motion, or spillover correction was necessary.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003ePreprocessing of MRI and PET data\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eBefore preprocessing, all neuroimaging data were set to brain imaging data structure (BIDS) format with \\u003cem\\u003eDcm2Bids\\u003c/em\\u003e 3.1.1 [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. Then, all images were anonymized using \\u003cem\\u003emri_reface\\u003c/em\\u003e 0.3.5 [\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. T1w MR image preprocessing was performed using the configurable \\u003cem\\u003esMRIPrep\\u003c/em\\u003e 0.17.0 [\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e] pipeline, which included intensity non-uniformity correction, skull-stripping, and spatial normalization to \\u003cem\\u003eMNI152NLin2009cAsym, MNI305\\u003c/em\\u003e, and \\u003cem\\u003efsaverage\\u003c/em\\u003e space.\\u003c/p\\u003e\\u003cp\\u003eFDG-PET data were first motion-corrected with the \\u003cem\\u003epetprep_hmc\\u003c/em\\u003e 0.0.9 [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e] pipeline and then further preprocessed using the \\u003cem\\u003epetprep_extract_tacs\\u003c/em\\u003e 0.0.5 pipeline [\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. This included co-registration and spatial normalization of dynamic PET data to \\u003cem\\u003efsaverage\\u003c/em\\u003e space. TACs were extracted in \\u003cem\\u003efsaverage\\u003c/em\\u003e space and averaged across predefined cortical and subcortical regions of interest (ROIs). Both volume- and surface-based data were smoothed with a 6 mm full-width at half-maximum Gaussian kernel. For the ROI-based TAC extraction, partial volume correction was applied using an adapted geometric transfer matrix (aGTM) method with a starting point-spread function assumption of 3 mm instead of smoothing.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eKinetic modeling\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eKinetic modelling of [\\u003csup\\u003e18\\u003c/sup\\u003eF]FDG-PET data was performed in R (v. 4.2.2, R Foundation, Vienna, Austria) using the \\u003cem\\u003ekinfitr\\u003c/em\\u003e package (v. 0.8.0) [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. We segmented the TACs for brain ROIs using \\u003cem\\u003epetprep_extract_tacs\\u003c/em\\u003e, and then estimated the magnitude of the unidirectional blood brain clearance (\\u003cem\\u003eK\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ein\\u003c/em\\u003e\\u003c/sub\\u003e; ml hg\\u003csup\\u003e− 1\\u003c/sup\\u003e min\\u003csup\\u003e− 1\\u003c/sup\\u003e) by Gjedde-Patlak linear graphic analysis [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]. Based on a visual inspection on the diagnostic plots generated by \\u003cem\\u003ekinfitr’s Patlak_tstar\\u003c/em\\u003e function, we used the final ten frames (10–67 min post injection) for the linearization. We excluded the blood volume fraction (vB) parameter as its inclusion did not improve the model fits or change \\u003cem\\u003eK\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ein\\u003c/em\\u003e\\u003c/sub\\u003e estimates. To obtain the cerebral metabolic rate for glucose consumption (CMRglc; µmols glucose hg\\u003csup\\u003e− 1\\u003c/sup\\u003e min\\u003csup\\u003e− 1\\u003c/sup\\u003e) for the ROI- and surface-based analyses, \\u003cem\\u003eK\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ein\\u003c/em\\u003e\\u003c/sub\\u003e values were multiplied by the average of blood glucose concentrations measured before and after each PET recordings, with no lumped constant correction.\\u003c/p\\u003e\\u003cp\\u003eFor surface-based analyses, time-activity curves were fitted using a custom Gjedde-Patlak modeling function implemented in Python. This approach applied the same parameters as used in the ROI-based modeling with \\u003cem\\u003ekinfitr\\u003c/em\\u003e and was performed for each vertex on the \\u003cem\\u003efsaverage\\u003c/em\\u003e surface maps for each individual scan. To obtain network-wise CMRglc values, the resulting vertex-wise CMRglc maps were spatially averaged within each of the seven resting-state networks defined by Yeo et al. [\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eAdditionally, as a complementary analysis, we fitted the TACs from the same ROIs using \\u003cem\\u003ekinfitr’s twotcm_irr\\u003c/em\\u003e function, which implements the two-tissue compartment model (2TCM) with irreversible binding relative to the IDIF, to estimate the microparameters for unidirectional blood-brain clearance (K₁; ml g\\u003csup\\u003e− 1\\u003c/sup\\u003e min\\u003csup\\u003e− 1\\u003c/sup\\u003e), brain washout fractional rate constant (k₂; min\\u003csup\\u003e− 1\\u003c/sup\\u003e), and relative hexokinase activity, i.e., irreversible trapping fractional rate constant (k₃; min\\u003csup\\u003e− 1\\u003c/sup\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003ePsychometry\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eAcute subjective drug effects were monitored throughout the study days (for time points, see Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) through two single-item based questionnaire versions: 1) a short version assessing “intensity of drug effects” and “challenging drug effects” (i.e., if the content or the quality of the experience difficult to handle or navigate) and 2) a long version, additionally assessing “liking”, “arousal”, “emotionality”, and “visual alterations”. All items were verbally rated on a visual analog scale (VAS) from 0–10 (0 = no effect; 10 = maximal effect). For correlational analyses with global CMRglc, the mean intensity rating across timepoints corresponding to the PET acquisition window (100–180 minutes post-administration) was calculated.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eBlood sample collections and biochemical plasma analysis\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eVenous blood samples were collected at seven timepoints of each session via a peripheral venous catheter (BD Venflon™ Pro Safety 18G, Becton Dickinson GmbH, Heidelberg, Germany) placed in the median cubital vein, with baseline sample collection just prior to the first drug administration (either placebo or verum), and at 20, 40, 60, 80, 100, and 180 min after first administration (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Two additional 2 mL blood samples were collected immediately before and after PET scan start to measure blood glucose concentration for the calculation of CMRglc. The final plasma sample was collected after completion of the PET scan and could therefore not always be obtained at exactly 180 minutes after the first dose (range: 172–245 min; mean: 189 min; median: 186 min): deviations of two minutes per time point were tolerated, but any blood withdrawals exceeding this tolerance range were discarded from analysis (except for the 180-minute final timepoint).\\u003c/p\\u003e\\u003cp\\u003ePlasma concentrations of DMT, harmine, and their primary metabolites—3-IAA, DMT-NO, and harmol—and serotonin were quantified using an ultra-high-performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) method adapted from an earlier study [\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. Serotonin levels were included to evaluate the potential MAO-A inhibiting effects of harmine. The Supplementary Material provides detailed information on sample processing and analytical procedures.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003ePharmacokinetic analysis\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eGiven our recent pharmacokinetic/pharmacodynamic (PK/PD) characterization of the DMT + harmine formulation [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], and given the constraints of blood sampling in the setting of the PET examination, we confined our PK analysis to the calculation of the area under the concentration-time curves from the first to the last measured timepoint (AUC\\u003csub\\u003elast\\u003c/sub\\u003e) for DMT and harmine for exploratory correlational purposes with global CMRglc values from the DMT + harmine PET scans. We calculated AUC\\u003csub\\u003elast\\u003c/sub\\u003e by non-compartmental analysis in R (v.4.4.0) with the \\u003cem\\u003encappc\\u003c/em\\u003e package (v.0.3.0), as described in our previous publication [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eStatistical analyses\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe primary hypothesis—that global CMRglc would be lower in the drug condition compared to placebo—was tested using a one-sided paired \\u003cem\\u003et\\u003c/em\\u003e-test (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05). Exploratory Pearson’s correlations were conducted between global CMRglc and AUC\\u003csub\\u003elast\\u003c/sub\\u003e of DMT and harmine, as well as mean subjective intensity during the PET acquisition window, in the DMT + harmine condition (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05, uncorrected).\\u003c/p\\u003e\\u003cp\\u003eSecondary exploratory analyses of regional CMRglc differences were performed using two-sided paired \\u003cem\\u003et\\u003c/em\\u003e-tests (uncorrected). Surface-based analyses were conducted using the SLM function for surface-based linear models (BrainStat 0.4.2, [\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e]), applying cluster-forming thresholds of \\u003cem\\u003ep\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eRFT\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003e\\u0026lt;0.05\\u003c/em\\u003e and \\u003cem\\u003ep\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eRFT\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003e\\u0026lt;0.01\\u003c/em\\u003e across the whole cortical surface. CMRglc differences between conditions within each of the seven Yeo networks were further assessed by applying a significance threshold of \\u003cem\\u003eq\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eFDR\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026lt;0.05. Further exploratory analyses correlated network-based CMRglc with publicly available 5-HT2AR density maps available in \\u003cem\\u003efsaverage\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e] by averaging the CMRglc and 5-HT2AR density per network for each scan and then using network-wise Pearson’s correlation for both DMT + harmine and placebo conditions. Differences in correlation coefficients (\\u003cem\\u003er\\u003c/em\\u003e) were compared using paired \\u003cem\\u003et\\u003c/em\\u003e-tests (\\u003cem\\u003eq\\u003c/em\\u003e\\u003csub\\u003eFDR\\u003c/sub\\u003e\\u0026lt;0.05). All statistical analyses were performed in Python (v.3.12.2) [\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e]or R (v.4.4.0) [\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e].\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cem\\u003eGlobal change in CMRglc and associations with plasma drug concentrations and subjective intensity\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eGlobal CMRglc was significantly higher in the DMT\\u0026thinsp;+\\u0026thinsp;harmine condition compared to placebo (\\u003cem\\u003et\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;2.57, df\\u0026thinsp;=\\u0026thinsp;13, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, one-sided paired \\u003cem\\u003et\\u003c/em\\u003e-test, Cohen\\u0026rsquo;s d (\\u003cem\\u003ez\\u003c/em\\u003e-standardized)\\u0026thinsp;=\\u0026thinsp;0.64) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cb\\u003epanel A\\u003c/b\\u003e). Individual data points and paired lines indicate a consistent increase across participants. There was a 12% global increase in the active condition (CMRglc [\\u0026micro;mol hg\\u003csup\\u003e-1\\u003c/sup\\u003e min\\u003csup\\u003e-1\\u003c/sup\\u003e] DMT\\u0026thinsp;+\\u0026thinsp;harmine\\u0026thinsp;=\\u0026thinsp;16.3, placebo\\u0026thinsp;=\\u0026thinsp;14.5, mean difference\\u0026thinsp;=\\u0026thinsp;1.8).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eIndividual plasma concentration curves of DMT, harmine, their main metabolites 3-IAA, DMT-NO, and harmol, as well as serotonin are shown in Supplement (\\u003cb\\u003eFig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e\\u003c/b\\u003e). DMT, harmine, and metabolite concentrations follow a very similar pattern as reported in [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], serotonin plasma concentration increases at the last timepoint (180 min) compared to previous timepoints (\\u003cb\\u003eFig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e\\u003c/b\\u003e). Mean subjective acute effect curves are also shown in the supplement (\\u003cb\\u003eFig. S2\\u003c/b\\u003e). Correlations between whole-brain CMRglc and pharmacokinetic (DMT and harmine AUC\\u003csub\\u003elast\\u003c/sub\\u003e) as well as subjective intensity (mean intensity between 100\\u0026ndash;180 min post drug administration) under DMT\\u0026thinsp;+\\u0026thinsp;harmine are shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cb\\u003epanels B\\u0026ndash;D\\u003c/b\\u003e. There was a significant positive correlation between global CMRglc and harmine AUC\\u003csub\\u003elast\\u003c/sub\\u003e (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.62, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.019). Positive but non-significant correlations were found for DMT AUC\\u003csub\\u003elast\\u003c/sub\\u003e (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.35, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.216), and for mean subjective intensity ratings while participants were in the scanner (i.e., 100\\u0026ndash;180 min after first DMT\\u0026thinsp;+\\u0026thinsp;harmine administration (\\u003cem\\u003er\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.39, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.166).\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eVertex- and network-wise changes in CMRglc\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eVertex and network-wise analysis of CMRglc differences between the active and placebo scan conditions revealed significantly increased CMRglc across large parts of the cerebral cortex at \\u003cem\\u003ep\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eRFT\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026lt;\\u003cem\\u003e0.\\u003c/em\\u003e05, and in specific regions belonging to the default mode (DMN), frontoparietal (FPN) and salience (SAL) networks, persisting with the more stringent threshold \\u003cem\\u003ep\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eRFT\\u003c/em\\u003e\\u003c/sub\\u003e\\u0026lt;0.01 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cb\\u003epanel A\\u003c/b\\u003e). Corresponding network-wise analysis of the surface data indicated increased CMRglc in attentional (i.e., dorsal attention (DAN) and SAL networks) and higher-level transmodal networks (i.e., FPN and DMN) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cb\\u003epanel B\\u003c/b\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eAssociations between network-wise CMRglc and 5-HT2AR density\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eWe calculated correlations between CMRglc and 5-HT2AR density (from publicly available maps [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]) per Yeo network and drug condition. There were no significant differences in correlation scores between placebo and active condition in any network (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn this single-blind, placebo-controlled within-subject FDG-PET study, we investigated the acute effects of a novel oromucosal formulation containing DMT and harmine on cerebral glucose metabolism in healthy participants. This ayahuasca-inspired combination was previously uncharacterized using molecular imaging, and our study provides first-in-human evidence for its metabolic impact. Using Gjedde-Patlak linear graphic analysis of [\\u0026sup1;⁸F]FDG uptake with individual IDIFs, we found a significant global increase in CMRglc under DMT\\u0026thinsp;+\\u0026thinsp;harmine compared to placebo. Complementary surface-based and network-level analyses revealed widespread metabolic increases, particularly within attention and transmodal association cortices of the SAL, FPN, and DMN. These findings suggest that the acute psychedelic state induced by DMT\\u0026thinsp;+\\u0026thinsp;harmine is associated with globally heightened cerebral energy demand, especially in higher-order cortical networks, and extend prior fMRI observations of DMT and ayahuasca by providing a direct index of neurometabolic activity. The cortical regions showing specifically increased CMRglc (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cb\\u003epanel A\\u003c/b\\u003e, last row) correspond to brain areas that already show the highest CMR\\u003csub\\u003eglc\\u003c/sub\\u003e at rest [\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]. The magnitude of global metabolic enhancement (~\\u0026thinsp;12%) in this study is comparable to, though slightly lower than, that reported in the only FDG-PET study with psilocybin (~\\u0026thinsp;20%) during resting state scans, further corroborating the conserved neurometabolic signature of serotonergic psychedelics [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Similar results (global CMRglc increased by ~\\u0026thinsp;20%, greater increase in frontal regions) have been obtained in an FDG-PET study with the \\u003cem\\u003eN-\\u003c/em\\u003emethyl-D-aspartate (NMDA) receptor antagonist ketamine, which is often referred to be an \\u0026ldquo;atypical\\u0026rdquo; psychedelic [\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e]. Cortical metabolic hyperfrontality was proposed both within these psilocybin and ketamine studies and an earlier SPECT study measuring cerebral blood flow under mescaline that was more pronounced in the right hemisphere in both cases [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e]. A similar right-lateralized hypermetabolic frontal pattern was also observed in a SPECT study of healthy individuals following ayahuasca administration [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e], as recapitulated in the present DMT\\u0026thinsp;+\\u0026thinsp;harmine dataset (ref. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cb\\u003epanel A\\u003c/b\\u003e, last two rows). This right-hemisphere predominance aligns with recent theoretical accounts, which suggests a psychedelic-induced loosening of interhemispheric hierarchy and a release of right-hemispheric processes often suppressed during normal waking consciousness [\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e]. Given the right hemisphere\\u0026rsquo;s established role in handling cognitive novelty and context-independent behavior [\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e], this lateralized pattern may reflect the brain\\u0026rsquo;s engagement with the psychedelic state as a subjectively novel and complex cognitive-emotional landscape. However, such a frontal hypermetabolic pattern was not evident in depressed patients after ayahuasca administration [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e], suggesting that it may be specific to healthy individuals.\\u003c/p\\u003e\\u003cp\\u003eAn earlier autoradiographic study showed dose-dependent \\u003cem\\u003edecreases\\u003c/em\\u003e in CMRglc in rats treated with either 5-methoxy-\\u003cem\\u003eN,N\\u003c/em\\u003e-dimethyltryptamine (5-MeO-DMT) or LSD [\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e], perhaps reflecting species differences, or differing serotonin receptor selectivities of LSD, 5-MeO-DMT, and DMT [\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e]. In a pilot PET study, we did not see any significant effect of low doses of DMT and/or harmine on FDG-uptake in brain of rats [\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e], thus further highlighting inconsistencies between pre-clinical and clinical studies.\\u003c/p\\u003e\\u003cp\\u003ePresent findings with DMT\\u0026thinsp;+\\u0026thinsp;harmine concur with the earlier human studies with psilocybin and ketamine in showing a substantial and global activation of CMRglc relative to the placebo condition [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e]. In the simplest interpretation, an elevation of CMRglc reflects increased energy metabolism, i.e., neuronal activity. Alternately, it could also arise in relation to a shift in metabolic coupling [\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e]. Indeed, DMT altered the expression of mitochondrial membrane-associated proteins in the brain of Alzheimer\\u0026rsquo;s disease model transgenic mice, and altered the physical association of mitochondria with endoplasmic reticulum in vitro, along with restorative effects on oxidative phosphorylation and ATP synthase [\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e]. The authors attributed these effects to an action of DMT at intracellular sigma-1 receptors, which might present a mechanism for the present observation of globally increased CMRglc (but might not explain the increases seen earlier with psilocybin). In general, increased glycolysis (i.e, CMRglc to FDG-PET) without a proportional increase in mitochondrial oxidation\\u0026mdash;known as uncoupling\\u0026mdash;should lead to elevated lactate production, as occurs during certain sensory stimulation paradigms [\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e], which might conceptually also apply to the acute effects of psychedelics. This alternative interpretation, suggestive of altered oxidative stoichiometry (i.e., a reduced oxygen-to-glucose ratio), could be explored in future studies using MR spectroscopy to assess lactate levels and metabolic flux directly, and [\\u003csup\\u003e15\\u003c/sup\\u003eO]-oxygen PET studies to measure the metabolic rate for oxygen.\\u003c/p\\u003e\\u003cp\\u003eWe speculate that the observed increase in glucose metabolism in the DMT\\u0026thinsp;+\\u0026thinsp;harmine condition may reflect a shift toward a higher-entropy brain state. In thermodynamic terms, increased energy consumption\\u0026mdash;indexed here by elevated CMRglc\\u0026mdash;can support a larger number of accessible microstates, reflecting a more disordered, flexible, and less hierarchically constrained neural configuration. Notably, preclinical and in vitro studies have shown that psychedelic compounds can acutely increase neuronal firing rates and cortical excitability, offering a potential mechanism for this elevated metabolic demand [\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e]. Psychedelic states are consistently associated with a breakdown of structured functional networks, increased global integration, and greater signal complexity\\u0026mdash;patterns that have been interpreted as hallmarks of elevated brain entropy [\\u003cspan additionalcitationids=\\\"CR22\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR27 CR28 CR29\\\" citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e]. These features have been integrated into recent models of psychedelic action, which propose that psychedelics transiently relax the influence of top-down beliefs, allowing for more flexible, bottom-up processing and unusual combinations of percepts, thoughts, and emotions [\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e]. CMRglc may index the energetic cost of this transient functional reorganization. Rather than efficient, segregated processing, the brain under psychedelics may shift into a state of widespread, metabolically demanding communication across networks. This reorganization could help disrupt rigid cognitive and emotional patterns, opening the way for novel perspectives and insights [\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e]. Crucially, such entropic brain states may not only explain the altered conscious experience but also underpin therapeutic effects, by expanding the brain\\u0026rsquo;s dynamic range and weakening entrenched activity patterns\\u0026mdash;especially in conditions marked by cognitive or emotional rigidity [\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eIn an exploratory analysis, we examined how normative binding potential maps (BP\\u003csub\\u003eND\\u003c/sub\\u003e) for the 5-HT2AR agonist ligand [\\u003csup\\u003e11\\u003c/sup\\u003eC]Cimbi-36 from an independent data set [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e] might relate to CMRglc patterns across Yeo networks, notwithstanding caveats arising from such a comparison [\\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e70\\u003c/span\\u003e]. Nonetheless, CMRglc showed a moderate correlation with [\\u003csup\\u003e11\\u003c/sup\\u003eC]Cimbi-36 BP\\u003csub\\u003eND\\u003c/sub\\u003e in both the placebo and DMT\\u0026thinsp;+\\u0026thinsp;harmine conditions; the similarity in correlation coefficients across conditions suggests that receptor distribution alone does not explain the acute cerebrometabolic effects of the drug administration. The local 5-HT2AR density may possibly serve as a proxy for broader structural properties such as cortical thickness, which co-varies with both neuroreceptor expression and metabolic rate [\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e]. This could explain why frontal and transmodal areas\\u0026mdash;characterized by both high 5-HT2AR density and cortical thickness\\u0026mdash;exhibited stronger metabolic effects in the DMT\\u0026thinsp;+\\u0026thinsp;harmine condition.\\u003c/p\\u003e\\u003cp\\u003eBased on our previous pharmacokinetic study with this formulation, we had selected an intermediate DMT\\u0026thinsp;+\\u0026thinsp;harmine dose and the 100\\u0026ndash;180 min post-administration window for PET acquisition, a time corresponding to peak plasma concentrations and subjective effects at the administered dose [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Plasma and subjective intensity curves from the current participant group (see Supplement) support this timing. However, both DMT and harmine showed slightly lower plasma concentrations and faster clearance compared to our earlier findings, potentially due to the all-male sample in this study, in consideration that males typically exhibit faster first pass drug metabolism and hepatic clearance (e.g., via CYP450 and CYP2D6 enzymes) [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e73\\u003c/span\\u003e]. Notably, we observed a\\u0026thinsp;~\\u0026thinsp;50% increase in plasma serotonin concentrations three hours after DMT\\u0026thinsp;+\\u0026thinsp;harmine administration relative to earlier timepoints, doubtless reflecting the inhibition of MAO-A in peripheral tissues. Given preclinical findings with reversible MAO-A inhibitors [\\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e74\\u003c/span\\u003e], and behavioral associations of plasma serotonin levels [\\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e], we can infer that the present treatment likely also increased brain serotonin levels. This suggests a model wherein psychedelic effects of exogenous DMT (as in ayahuasca) occur in conjunction with a potentiation of serotonergic signaling due to inhibition of brain MAO-A, as distinct from the potentiation of DMT brain uptake via inhibition of peripheral MAO-A.\\u003c/p\\u003e\\u003cp\\u003eGlobal CMRglc under DMT\\u0026thinsp;+\\u0026thinsp;harmine correlated significantly with the AUC for harmine, but (unexpectedly) not for the AUCs for DMT or subjective intensity. While this finding might suggest a primary role for harmine in modulating glucose metabolism, we believe these correlation findings should be interpreted with caution. It does not follow necessarily from the observed correlations that harmine is the driver for the observed increase in brain metabolism. In a recent study using this same drug formulation, we observed strong correlations between the individual DMT and harmine AUCs, and saw similar temporal patterns for the plasma drug concentrations and the subjective effects [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Examination of Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e suggests that the present study was underpowered to detect such a significant correlation for DMT. Alternately, we note that our study protocol was primarily optimized to assess CMRglc rather than to capture with high precision the full pharmacokinetic profiles of DMT and harmine. Moreover, our own pilot FDG-PET study in rats found only a small change in glucose metabolism after low-dose harmine administration in the thalamus compared to placebo [\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e], and the broader literature remains sparse regarding direct metabolic effects of harmine on the brain. Given that harmine\\u0026rsquo;s primary pharmacological role in this context is presumably to inhibit MAO-A (although it may have other actions in the context of ayahuasca [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]) and thereby enable oral DMT bioavailability [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], we consider it unlikely that harmine alone contributes importantly to the observed global CMRglc increase. This holds especially in consideration that harmine alone does not induce psychedelic effects, but possesses a distinct psychoactive profile with different or even opposed characteristics to those typically observed with serotonergic psychedelics [\\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e76\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eLimitations\\u003c/p\\u003e\\u003cp\\u003eWe employed a single-blind, within-subject, placebo-controlled design, providing strong sensitivity and robustness for detecting the hypothesized drug-induced CMRglc changes. However, we note several limitations of the study. While the sample size sufficed to detect global and regional CMRglc changes, it was relatively small for the exploratory correlational analyses, especially those involving pharmacokinetics, which were further affected by high interindividual variability in drug disposition, as previously reported [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Additionally, the study design was not optimized for detailed pharmacokinetic profiling, as the blood sampling schedule lacked sufficient resolution to capture complete AUCs (i.e., during the PET recordings). The sample consisted exclusively of healthy, white, male participants, which limits the generalizability of our findings. Blinding efficacy was limited; most participants correctly identified their treatment condition by the second study day, reflecting a common challenge in psychedelic research [\\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e77\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e78\\u003c/span\\u003e]. We opted for an inert placebo to enhance neuroimaging contrasts, at the expense of effective blinding. Finally, we used an IDIF instead of the more conventional arterial input function (AIF) for CMRglc quantification, which might have biased the evaluation of CMRglc. However, in a recent study, there was a considerable degree of concordance between IDIF- and AIF-based analyses of CMRglc [\\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e79\\u003c/span\\u003e]. In effect, the advent of LAFOV PET scanners enable recovery of the FDG signal from large vascular structures, such as the aorta in the present study, without penalty in accuracy due to spillover effects of heart motion [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e].\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eOur findings demonstrate that acute administration of a novel oromucosal DMT\\u0026thinsp;+\\u0026thinsp;harmine formulation induces a robust global increase in cerebral glucose metabolism, with particularly strong effects in attentional and higher-order transmodal networks. These metabolic changes may reflect a distinct brain state characterized by globally heightened glucose metabolism, which is generally held to reflect increased neuronal activity [\\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e]. However, we cannot exclude the possibility that the increased CMRglc reflects mitochondrial uncoupling, rather than increased metabolic activity per se. On the other hand, the spatial pattern of CMRglc increases under DMT\\u0026thinsp;+\\u0026thinsp;harmine appears consistent with a shift toward a more entropic and less hierarchically constrained brain state. Such a configuration may support the breakdown of entrenched patterns of neural activity, promoting cognitive and emotional flexibility. The right hemisphere predominance of the increased CMRglc may be in accord with a recent model of psychedelic action involving a change in hemispheric hierarchy. Future studies should aim to establish the causal mechanism whereby this drug formation stimulates brain glucose metabolism, and to establish better the contribution of 5-HT2AR agonism to the cerebrometabolic and subjective effects of DMT\\u0026thinsp;+\\u0026thinsp;harmine.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eClinical trial registry name and URL incl. registration number:\\u0026nbsp;\\u003c/strong\\u003eMolecular Imaging Study of Harmine/DMT: a Basic Research Approach (HaD-PET) \\u0026nbsp;https://clinicaltrials.gov/study/NCT06252506\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003cbr\\u003e\\u0026nbsp;The authors thank study physicians Jovin Müller and Sarah Njoh for their medical support and screening of participants, the medical imaging personnel at the study site in Bern, namely Marco Viscione,\\u0026nbsp;Ângela Mendes,\\u0026nbsp;Ângelo Felgosa Cardoso, and Janneke Henniphoffor conducting the PET scans, Céline Birrer and Franziska Strunz for their administrative support, Robin von Rotz for coordinating with the database provider, and John Smallridge for sharing analysis scripts for pharmacokinetic analyses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003cbr\\u003e\\u003c/strong\\u003eThis work was supported by the Swiss National Science Foundation (Grant Number 320030-204978) awarded to Professor Cumming.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u003cbr\\u003e\\u0026nbsp;Klemens Egger:\\u0026nbsp;\\u003c/strong\\u003eConceptualization,\\u0026nbsp;Data curation, Formal analysis, Investigation, Project administration, Visualization, Writing – Original draft, Writing – Review and editing. \\u003cstrong\\u003eRobert Bozsak:\\u0026nbsp;\\u003c/strong\\u003eInvestigation, Project administration, Writing – Review and editing.\\u003cstrong\\u003e\\u0026nbsp;Helena D. Aicher:\\u0026nbsp;\\u003c/strong\\u003eInvestigation, Writing – Review and editing.\\u003cstrong\\u003e\\u0026nbsp;Hasan Sari:\\u0026nbsp;\\u003c/strong\\u003eResources, Methodology, Writing – Review and editing.\\u003cstrong\\u003e\\u0026nbsp;Sandra N. Poetzsch:\\u0026nbsp;\\u003c/strong\\u003eFormal analysis, Writing – Review and editing. \\u003cstrong\\u003eAxel Rominger:\\u0026nbsp;\\u003c/strong\\u003eResources, Writing – Review and editing. \\u003cstrong\\u003eChantal Martin-Soelch:\\u0026nbsp;\\u003c/strong\\u003eConceptualization. \\u003cstrong\\u003eDario Dornbierer:\\u0026nbsp;\\u003c/strong\\u003eResources. \\u003cstrong\\u003eBoris B. Quednow:\\u0026nbsp;\\u003c/strong\\u003eConceptualization, Writing – Review and editing. \\u003cstrong\\u003eMilan Scheidegger:\\u0026nbsp;\\u003c/strong\\u003eConceptualization, Writing – Review and editing. \\u003cstrong\\u003ePaul Cumming:\\u0026nbsp;\\u003c/strong\\u003eConceptualization, Funding acquisition, Methodology, Writing – Review and editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflicts of Interests\\u003c/strong\\u003e\\u003cbr\\u003e\\u0026nbsp;KE, RB, HAD, HS, SNP, AR, CM-S, BBQ, MS, PC have nothing to declare. DD and MS declare that they co-founded Reconnect Labs AG, an academic spin-off at the University of Zurich, focused on the development of psychedelic medicines for mental health.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData statement\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003cbr\\u003e\\u003c/strong\\u003eImaging data related to this project will be made available in an online repository. Additional information is available upon reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eNichols DE. Psychedelics. Pharmacol Rev. 2016;68:264\\u0026ndash;355.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVollenweider FX, Kometer M. The neurobiology of psychedelic drugs: implications for the treatment of mood disorders. Nat Rev Neurosci. 2010;11:642\\u0026ndash;651.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVogt SB, Ley L, Erne L, Straumann I, Becker AM, Klaiber A, et al. Acute effects of intravenous DMT in a randomized placebo-controlled study in healthy participants. Transl Psychiatry. 2023;13:172.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLuan LX, Eckern\\u0026auml;s E, Ashton M, Rosas FE, Uthaug M V, Bartha A, et al. Psychological and physiological effects of extended DMT. 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Nat Commun. 2015;6:6807.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7099164/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7099164/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eClassical psychedelics such \\u003cem\\u003eN,N\\u003c/em\\u003e-dimethyltryptamine (DMT), psilocybin, and lysergic acid diethylamide (LSD) modulate consciousness via serotonergic receptor agonism, and are increasingly investigated for their psychotherapeutic potential. When combined with the monoamine oxidase A (MAO-A) inhibitor harmine\\u0026mdash;mimicking the pharmacological profile of ayahuasca\\u0026mdash;oral DMT induces a psychedelic experience lasting 4\\u0026ndash;5 hours. While neuroimaging studies have examined changes in brain activity, connectivity, and cerebral perfusion under psychedelics, their effects on cerebral glucose metabolism remain largely unexplored. Here, we used positron emission tomography with [\\u003csup\\u003e18\\u003c/sup\\u003eF]fluorodeoxyglucose ([\\u0026sup1;⁸F]FDG-PET) to assess the cerebral metabolic rate for glucose consumption (CMRglc) following buccal DMT\\u0026thinsp;+\\u0026thinsp;harmine (90 mg DMT, 120 mg harmine) versus placebo in a single-blind, placebo-controlled, crossover design in (n\\u0026thinsp;=\\u0026thinsp;14) healthy males. Scans were acquired during peak drug effects, i.e., 100\\u0026ndash;170 min post-administration. Global CMRglc increased by 12% under DMT\\u0026thinsp;+\\u0026thinsp;harmine compared to placebo (\\u003cem\\u003et\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;2.57, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), with relatively greater activation in the right hemisphere. Vertex- and network-wise analyses revealed widespread cortical increases, with localized effects in the default mode, frontoparietal, and attentional networks. Exploratory correlational analyses found a significant positive correlation between global CMRglc and harmine plasma levels (area under the curve (AUC); \\u003cem\\u003er\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.61, \\u003cem\\u003ep\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.021) in the DMT\\u0026thinsp;+\\u0026thinsp;harmine condition, but not with DMT AUC, subjective intensity ratings, or regional serotonin-2A receptor (5-HT2AR) density derived from a publicly available PET atlas. These findings advance the mechanistic understanding of psychedelics by demonstrating that DMT\\u0026thinsp;+\\u0026thinsp;harmine increases cerebral glucose metabolism, particularly in higher-order networks, and augment pioneering work indicating increased brain glucose metabolism as a potential metabolic signature of the psychedelic state.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Global Increases in Brain Glucose Metabolism Following Acute N,N-Dimethyltryptamine and Harmine Administration in Healthy Volunteers: An [¹⁸F]FDG-PET Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-27 10:56:15\",\"doi\":\"10.21203/rs.3.rs-7099164/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"be1a5fb4-a7ff-4610-b088-60917e6908be\",\"owner\":[],\"postedDate\":\"July 27th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":51826830,\"name\":\"Health sciences/Biomarkers/Predictive markers\"},{\"id\":51826831,\"name\":\"Biological sciences/Neuroscience\"},{\"id\":51826832,\"name\":\"Biological sciences/Biological techniques\"}],\"tags\":[],\"updatedAt\":\"2025-11-26T10:16:34+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-07-27 10:56:15\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7099164\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7099164\",\"identity\":\"rs-7099164\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}