Global increases in brain glucose metabolism following acute N,N-dimethyltryptamine and harmine administration in healthy volunteers: A randomised [ 18 F]FDG-PET study

Abstract

Classical psychedelics such as N,N-dimethyltryptamine (DMT) 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 h. While some neuroimaging studies have characterized effects of DMT on functional connectivity and electroencephalography its impact on cerebral energy metabolism remains largely unexplored. We assessed the cerebral metabolic rate for glucose consumption (CMRglc) with [¹⁸F]fluorodeoxyglucose positron emission tomography ([¹⁸F]FDG-PET) and linear graphic analysis following buccal DMT + harmine (90 mg DMT, 120 mg harmine) versus placebo in a single-blind, crossover design in 14 healthy males. Scans were acquired during peak drug effects (100–170 min post-administration). Global CMRglc increased by 12.5% under DMT+harmine versus placebo (t = 2.58, p = 0.011). Vertex- and network-wise analyses revealed widespread cortical increases, particularly in higher-order brain networks. Exploratory analyses found a significant positive correlation between global CMRglc and harmine plasma levels, but not with DMT plasma levels, subjective intensity ratings. A psychedelic dose of DMT + harmine globally increased cerebral glucose metabolism, recapitulating a classic finding for psilocybin, and suggesting a potential metabolic signature of the psychedelic state. 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.

Keywords

FDG-PET glucose metabolism psychedelics DMT harmine pharmahuasca

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.^6
–9

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.^10,11 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,^12,13 while minimizing the emetic effects and uncertain dosages associated with the plant-derived brew.¹⁴

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).^15
–18 Such studies have consistently documented functional changes during acute psychedelic states, such as increased global connectivity as marked by greater signal complexity or entropy,^19
–23 and reduced modular segregation between functional networks.^24
–28 These alterations are thought to underlie the dissolution of ego boundaries, vivid imagery, and heightened emotional states often reported during psychedelic experiences.¹⁵ 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.²⁹ To date, only two human studies—both conducted nearly 3 decades ago with psilocybin—have examined cerebral glucose metabolism using PET with the glucose analogue [¹⁸F]fluorodeoxyglucose (FDG). One study reported a global increase in the cerebral metabolic rate of glucose (CMRglc) during the acute psychedelic state,³⁰ 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.³¹ Nearly 3 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.^32
–34

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.^12,13,35 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. In addition, as a descriptive analysis of the spatial distribution of cortical metabolic activity, we compared Shannon entropy of CMRglc distributions between conditions.

Methods

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 and 45 years and previous experience with psychedelic substances, excluding the preceding 3 months. Key participant characteristics are shown in Table 1. Full eligibility (in- and exclusion) criteria are detailed in the Supplemental 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 6-h PET study days, preceded by a screening visit. Study days were separated by a washout period of at least 1 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 4 h 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 min apart to ensure a gradual and smooth transition into the psychedelic state,¹³ 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 (Egger et al.¹³ and Supplemental 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 Figure 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 min after the first drug dose, participants were transferred to the PET scanner for a ~70-min 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. Discharge occurred approximately 90–150 min after completion of the PET scan.

Figure 1.

Study 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 min intervals. Vital signs, and psychometry were assessed in regular intervals with the last time 5 h after first administration. Blood samples were drawn at 20-min intervals before start of the PET scan and once more after the PET scan, to a total of seven blood samples per study day.

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 mm², slices = 160, no interslice gap, voxel size = 1.0 × 1.0 × 1.0 mm³, 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 [¹⁸F]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 mm³, 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 mm³, 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 [¹⁸F]FDG-PET studies.^36,37 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.³⁸ 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. To reduce noise in the IDIF, the whole-blood TAC was additionally fitted using the Feng model to obtain a smooth input function curve.³⁹ To derive a plasma input function, the whole-blood IDIF was converted to plasma activity using a population-based red blood cell (RBC)-to-plasma ratio model as described by Phelps et al.⁴⁰ Specifically, a time-dependent RBC-to-plasma ratio r(t) = 0.8 + 0.0012 × t (with time in minutes) and a fixed hematocrit H = 0.42 were assumed, and plasma activity was calculated as

C_{plasma} (t) = \frac{C_{blood} (t)}{H r (t) + (1 - H)}

Where $C_{plasma} (t)$ and $C_{blood} (t)$ denote the plasma and whole-blood [¹⁸F]FDG concentrations, respectively. For [¹⁸F]FDG, the parent fraction was assumed to be unity throughout the scan duration. The resulting (Feng-smoothed) plasma IDIF was used for kinetic modeling.³⁹

Preprocessing of MRI and PET data

Neuroimaging preprocessing combined established command-line and containerized pipelines with study-specific Python scripts for auxiliary PET-processing steps. Before preprocessing, all neuroimaging data were set to brain imaging data structure (BIDS) format with Dcm2Bids 3.1.1.⁴¹ Then, all images were anonymized using mri_reface 0.3.5.⁴² T1w MR image preprocessing was performed using the configurable sMRIPrep 0.17.0 pipeline,⁴³ which included intensity non-uniformity correction, skull-stripping, and spatial normalization to fsaverage space. The fsaverage space is a standardized cortical surface representation that enables comparison across participants in surface space.⁴⁴

FDG-PET data were first motion-corrected with the petprep_hmc 0.0.9 pipeline and then further preprocessed using the petprep_extract_tacs 0.0.5 pipeline.^45,46 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 [¹⁸F]FDG-PET data was performed in R (v. 4.2.2, R Foundation, Vienna, Austria) using the kinfitr package (v. 0.8.0).⁴⁷ 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.⁴⁸ 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; µmol 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, and divided with a lumped constant of 0.65.⁴⁹

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 the seven resting-state networks defined by Yeo et al.⁵⁰ Each network consists of all cortical surface vertices assigned to one of the seven networks (visual (VIS), somatomotor (SMN), dorsal attention (DAN), ventral attention/salience (SAL), limbic (LIM), frontoparietal (FPN), and default mode (DMN)), and mean CMRglc was computed across those vertices for each participant and condition. The networks are outlined on the cortical surface in Figure 4(a). These ROIs therefore represent atlas-defined cortical gray-matter networks on the standard surface template and do not include subcortical structures or white matter. To characterize whether DMT + harmine-induced network-wise CMRglc increases reflected a common cortical response or multiple dissociable patterns, we performed a descriptive principal component analysis (PCA) on the participant-by-network matrix of ∆CMRglc (DMT + harmine – placebo) values across the seven Yeo networks. Network delta values were z-standardized across participants before PCA, which was implemented in Python using singular value decomposition. We report the explained variance of the first components and their network loadings in Figure 4.

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, that is, irreversible trapping fractional rate constant (k₃; min⁻¹). We compared CMRglc estimates obtained with the 2TCM and Gjedde–Patlak approaches in the Supplemental material (Figure S4).

Psychometry

Acute subjective drug effects were monitored throughout the study days (for time points, see Figure 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 to 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 min 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 (Figure 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 min after the first dose (range: 172–245 min; mean: 189 min; median: 186 min): deviations of 2 min per time point were tolerated, but any blood withdrawals exceeding this tolerance range were discarded from analysis (except for the 180-min 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.⁵¹ Serotonin levels were included to evaluate the potential MAO-A inhibiting effects of harmine. The Supplemental 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,¹³ 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 with the ncappc package (v.0.3.0), as described in our previous publication.¹³

Statistical analyses

The primary hypothesis—that global CMRglc would be higher 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 in the DMT+harmine as well as ∆CMRglc (DMT + harmine—placebo) differences 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). As an additional descriptive analysis of the spatial distribution of cortical metabolic activity, Shannon entropy⁵² was calculated for the vertex-wise CMRglc distribution of each participant in each condition using the fsaverage surface maps. Entropy was estimated from normalized histograms of vertex-wise CMRglc values using identical bin edges across participants and conditions. Surface-based analyses were conducted using the SLM function for surface-based linear models (BrainStat 0.4.2),⁵³ 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. All statistical analyses were performed in Python (v.3.12.2; Python Software Foundation, Wilmington, DE, USA) or R (v.4.4.0).

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(13) = 2.58, p = 0.011, one-sided paired t-test, Cohen’s d (z-standardized) = 0.65; Figure 2(a)). Individual data points and paired lines indicate a consistent increase across participants. There was a 12.5% global increase in the active condition (CMRglc [µmol hg⁻¹ min⁻¹] DMT + harmine = 23.4 ± 3.3, placebo = 20.8 ± 1.9, mean difference = 2.6). To evaluate potential session/order effects, we compared global CMRglc between placebo scans acquired in the first versus second session and additionally re-tested the whole-brain drug condition effect in a repeated-measures mixed model including session as a covariate and subject as a random intercept. Neither analysis indicated a meaningful session/order effect: placebo scans did not differ by session (Figure S1), and the condition effect remained significant after adjustment (∆CMR_glc (DMT + harmine—Placebo) [µmol hg⁻¹ min⁻¹] = 2.75, t(12) = 2.73, p = 0.018), whereas session itself was not significant (p = 0.292). No significant difference was observed between placebo sessions 1 and 2 Exploratory analyses of the insula and subcortical regions revealed a broadly comparable increase in CMRglc following DMT + harmine relative to placebo (range: 7%–16%). Paired t-tests indicated significant increases in the insula, striatum, thalamus, caudate nucleus, and nucleus accumbens (two-sided, uncorrected; Table S1).

Figure 2.

Whole-brain CMRglc increases under DMT + harmine, and associations with subjective and blood plasma concentrations of DMT and harmine. (a) Violin plot showing whole-brain CMRglc (µmol/100 g/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 t-test shows a statistically significant increase in whole-brain CMRglc in the DMT + harmine condition compared to placebo (t(13) = 2.58, p = 0.011). (b–g) 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_last), and (d) harmine exposure (AUC_last). A significant positive correlation is observed with harmine AUC_last (r = 0.62, p = 0.019), while associations with subjective intensity (r = 0.39, p = 0.168) and DMT AUC_last (r = 0.35, p = 0.218) are positive but not statistically significant. (e–g) Scatter plots showing correlations between whole-brain ∆CMRglc (DMT + harmine—placebo) and (e) mean subjective intensity ratings during the PET scan, (f) DMT exposure (AUC_last), and (g) harmine exposure (AUC_last). These correlations are also positive, but not significant: ∆CMRglc with subjective intensity (r = 0.35, p = 0.214), with DMT AUC_last (r = 0.19, p = 0.515), and with harmine AUC_last (r = 0.48, p = 0.082). Shaded areas represent 95% confidence intervals. AUC_last indicates the area under the time-concentration curve from the first to the last collected blood plasma sample.

Individual plasma concentration curves of DMT, harmine, their main metabolites 3-IAA, DMT-NO, and harmol, as well as serotonin are shown in Supplement (Figure S2). DMT, harmine, and metabolite concentrations follow a very similar pattern as reported in Egger et al.,¹³ serotonin plasma concentration increases at the last timepoint (180 min) compared to previous timepoints (Figure S2). Mean subjective acute effect curves are also shown in the supplement (Figure S3). Correlations between whole-brain CMRglc and pharmacokinetic (DMT and harmine AUC_last) as well as subjective intensity (mean intensity between 100 and 180 min post drug administration) under DMT + harmine are shown in Figure 2(b–d), and corresponding correlations with whole-brain ∆CMRglc (DMT + harmine—placebo) are shown in Figure 1(e–g). 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 = 0.218), 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 = 0.168). Correlations with whole-brain ∆CMRglc were likewise positive but not statistically significant, including mean subjective intensity (r = 0.35, p = 0.214), DMT AUC_last (r = 0.19, p = 0.515), and harmine AUC_last (r = 0.48, p = 0.082).

Vertex- and network-wise changes in CMRglc

The unthresholded cortical surface map of mean ∆CMRglc (DMT + harmine—placebo) is shown in Figure 3(a). Consistent with the vertex-wise histograms shown in Figure 3(b) and (c), Shannon entropy of the vertex-wise CMRglc distribution was significantly higher in the DMT + harmine than in the placebo condition (t(13) = 2.33, p = 0.037).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 DMN, FPN, and SAL networks, persisting with the more stringent threshold p_RFT < 0.01 (Figure 3(a)). Corresponding network-wise analysis of the surface data indicated increased CMRglc in attentional (i.e., DAN and SAL networks) and higher-level transmodal networks (i.e., FPN and DMN; Figure 4(b)).

Figure 3.

Surface-based differences in CMRglc between the DMT + harmine and placebo conditions. (a) CMRglc mapped onto the fsaverage brain surface for the DMT + harmine (top row) and placebo (second row) conditions and the CMRglc difference between scans (∆CMRglc (DMT + harmine—placebo); third row). Statistical maps showing t-values from two-tailed paired t-tests (DMT + harmine vs placebo) are presented at two significance thresholds: fourth row, p_RFT < 0.05; fifth row, p_RFT < 0.01. At p_RFT < 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 p_RFT < 0.01, significant increases are mainly localized within the DMN, FPN, and SAL. (b) 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. (c) Paired half-violin plots showing subject-wise Shannon entropy of the vertex-wise cortical CMRglc distributions for the DMT + harmine and placebo conditions. Shannon entropy was significantly higher under DMT + harmine than placebo (t(13) = 2.33, p = 0.037). Individual data points and paired lines indicate within-subject changes.

Figure 4.

Network-wise DMT + harmine-induced changes in CMRglc. (a) Visual representation of the seven canonical large-scale brain networks defined by Yeo et al.⁵⁰ (b) Network-wise averaged comparisons confirm the vertex-wise results, showing significantly increased CMRglc (q_FDR < 0.05) in the DAN, SAL, FPN, and DMN during the DMT + harmine scans. (c and d) Principal component analysis (PCA) was performed on participant-wise ∆CMRglc (DMT + harmine—placebo) across the seven Yeo resting-state networks. Left: scree plot showing the proportion of variance explained by each principal component and cumulative explained variance. Right: network loadings for the first principal components. The first principal component explained 93.8% of the variance and showed uniformly positive loadings across all networks, indicating that DMT + harmine-induced metabolic increases were largely coordinated across the whole cortex rather than reflecting strongly dissociable network-specific patterns.

To further characterize the covariance structure of these network-wise changes, we performed a descriptive principal component analysis (PCA) on participant-wise ∆CMRglc values across the seven Yeo networks (Figure 4). The first principal component explained 93.8% of the variance. Its loadings were uniformly positive across all networks, with similar magnitudes for VIS, SMN, DAN, SAL, FPN, DMN, and a somewhat smaller but still positive loading for LIM. The second principal component explained 5.2% of the variance, whereas all remaining components each explained less than 1%.

In an additional surface-based grouping analyses, mean ∆CMRglc was significantly greater in transmodal networks (including DAN, SAL, FPN, and DMN) compared to unimodal networks (including VIS and SMN; t(13) = 6.73, p = 1.40 × 10⁻⁵). By contrast, there was no significant difference in mean whole-cortex ∆CMRglc between the left and right hemispheres (t(13) = −1.06, p = 0.307; Figure S5).

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 (Figure 2(a), last row) correspond to brain areas that already show the highest CMRglc at rest.⁵⁴ The magnitude of global metabolic enhancement (~12.5%) 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.³⁰ 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.⁵⁵ Cortical metabolic hyperfrontality was proposed both within these psilocybin and ketamine studies and an earlier SPECT study measuring cerebral blood flow under mescaline.^30,34,55 These studies indicated slightly stronger effects in the right hemisphere^30,34,55; however, these earlier reports did not formally test hemispheric lateralization. A SPECT study following measuring blood perfusion in the subacute phase after ayahuasca administration did not show a comparably clear frontal or hemispheric pattern, but rather reported increases in only a few relatively small regions.³² To assess potential hemispheric asymmetry in the cortical metabolic response to DMT + harmine, we examined changes in left and right cortical CMRglc separately as well as their relative difference. Cortical metabolism increased relative to placebo in both hemispheres, with a numerically larger mean increase observed in the right cortex compared to the left (ref. Figure 2(a), last two rows). However, the magnitude of this difference was modest, and our surface-based analysis did not provide statistical evidence for a significant hemispheric asymmetry in metabolic response (Figure S5).

Recent theoretical models propose that psychedelic states may involve loosening of interhemispheric hierarchy and a release of right-hemispheric processes often suppressed during normal waking consciousness.⁵⁶ Given the right hemisphere’s established role in handling cognitive novelty and context-independent behavior,⁵⁷ this lateralized pattern may reflect the brain’s engagement with the psychedelic state as a subjectively novel and complex cognitive-emotional landscape. While the present data do not provide sufficient evidence to support this model, the observed pattern is consistent with it and highlights hemispheric organization as a promising target for future work.

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,⁵⁸ perhaps reflecting species differences, or differing serotonin receptor selectivities of LSD, 5-MeO-DMT, and DMT.⁵⁹ 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,⁶⁰ 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.^30,55 In the simplest interpretation, an elevation of CMRglc reflects increased energy metabolism, that is, neuronal activity. Alternately, it could also arise in relation to a shift in metabolic coupling.⁶¹ 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.⁶² 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,⁶¹ 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 [¹⁵O]-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, corresponding to a more variable, less hierarchically constrained mode of brain activity. However, this interpretation remains indirect, as FDG-PET does not permit direct assessment of neural microstates or moment-to-moment signal complexity. To provide a complementary descriptive measure, we calculated Shannon entropy of the vertex-wise cortical CMRglc distribution and found that it was significantly increased under DMT + harmine relative to placebo. This finding is more appropriately interpreted as reflecting a broader and less concentrated spatial distribution of cortical metabolic activity, rather than as a direct demonstration of increased thermodynamic entropy. 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.⁶³ More generally, psychedelic states have been associated with alterations in large-scale functional organization and with increases in some, but not all, measures of neural signal diversity or complexity, including fMRI degree-distribution entropy, fMRI sample entropy at certain temporal scales, and MEG Lempel-Ziv complexity.^{19
–21,24
–28,64} 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.⁶⁵ In this context, CMRglc may index the energetic cost of this transient functional reorganization. FDG-PET offers a complementary advantage by providing a direct, quantitative measure of cerebral glucose metabolism integrated over an extended time window, thereby indexing neuroenergetic demand more directly than fMRI or arterial spin labeling (ASL), which rely on hemodynamic correlates that may be altered under psychedelic conditions. Rather than efficient, segregated processing, the brain under psychedelics may transiently operate in a metabolically demanding state characterized by widespread and less predictable neural communication. Notably, this interpretation might be compatible with fMRI and ASL reporting reduced functional connectivity or regional perfusion in 5-HT2A–rich cortical regions.^24,66 However, recent work suggests that spatial correlations between population-level maps derived from separate cohorts, such as receptor-density and perfusion maps, can overstate biological correspondence and should therefore be interpreted with caution.⁶⁷ Increased metabolic demand at the cellular level may coexist with reduced hemodynamic correlations or altered neurovascular coupling, such that higher variability in neuronal activity leads to weaker statistical dependencies between regions despite elevated energy consumption. 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.^64,68

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.¹³ 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).^10,69 Notably, we observed a ~50% increase in plasma serotonin concentrations 3 h 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,⁷⁰ and behavioral associations of plasma serotonin levels,⁷¹ 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.¹³ Examination of Figure 2 suggests that the present study was underpowered to detect such a significant correlation for DMT. Interpretation of these associations is further complicated by the high interindividual variability characteristic of psychedelic pharmacokinetics and subjective effects, which has been documented for DMT both under intravenous administration and in combined formulations with harmine.^3,4,13 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 (1 mg/kg) administration in the thalamus compared to placebo,⁶⁰ 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 ayahuasca¹⁰) and thereby enable oral DMT bioavailability,¹³ 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.⁷²

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.¹³ 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.⁷³ We opted for an inert placebo to enhance neuroimaging contrasts, at the expense of effective blinding. The absence of pharmacological control conditions (e.g., DMT-only or harmine-only) does not allow disentangling the contributions of individual drug effects; instead, the observed metabolic changes should reflect the combined, synergistic action of DMT and harmine, analogous to the pharmacological composition of ayahuasca. 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.⁷⁴

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.⁷⁵ Additional surface- and network-based analyses indicated that these effects were spatially widespread, significantly greater in transmodal than in unimodal cortex, and largely coordinated across cortical networks. Together, these findings suggest that DMT + harmine induces a metabolically demanding and spatially distributed cortical state that may support the transient reorganization of ongoing brain activity and cognition. Future studies should aim to establish the causal mechanism whereby this drug formation stimulates brain glucose metabolism, and to establish the contribution of 5-HT2AR agonism to the cerebrometabolic and subjective effects of DMT + harmine.

Supplemental Material

sj-pdf-1-jcb-10.1177_0271678X261454172 – Supplemental material for Global increases in brain glucose metabolism following acute N,N-dimethyltryptamine and harmine administration in healthy volunteers: A randomised [18F]FDG-PET study

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X261454172 for Global increases in brain glucose metabolism following acute N,N-dimethyltryptamine and harmine administration in healthy volunteers: A randomised [18F]FDG-PET study by Klemens Egger, Robert Bozsak, Helena D. Aicher, Hasan Sari, Sandra N. Poetzsch, Axel Rominger, Chantal Martin-Soelch, John W. Smallridge, Dario Dornbierer, Boris B. Quednow, Milan Scheidegger and Paul Cumming in Journal of Cerebral Blood Flow & Metabolism

Footnotes

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 Henniphof for conducting the PET scans, Céline Birrer and Franziska Strunz for their administrative support, and Robin von Rotz for coordinating with the database provider.

Author contributions

Klemens Egger: Conceptualization, Data curation, Formal analysis, Investigation, Project administration, Visualization, Writing – Original draft, Writing – Review & editing. Robert Bozsak: Investigation, Project administration, Writing – Review & editing. Helena D. Aicher: Investigation, Writing – Review & editing. Hasan Sari: Resources, Methodology, Writing – Review & editing. Sandra N. Poetzsch: Formal analysis, Writing – Review & editing. Axel Rominger: Resources, Writing – Review & editing. Chantal Martin-Soelch: Conceptualization. John W. Smallridge: Formal analysis, Writing – Review & editing. Dario Dornbierer: Resources. Boris B. Quednow: Conceptualization, Writing – Review & editing. Milan Scheidegger: Conceptualization, Writing – Review & editing. Paul Cumming: Conceptualization, Funding acquisition, Methodology, Writing – Review & editing.

Declaration of conflicting interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: 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. JWS reports a relationship with Reconnect Labs that includes: consulting or advisory.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Swiss National Science Foundation (Grant Number 320030-204978) awarded to Professor Cumming.

Ethical considerations

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).

Consent to participate

All participants provided written informed consent.

ORCID iDs

Klemens Egger

Helena D. Aicher

Paul Cumming

Data availability

Data related to this project are available at OpenNeuro.org (doi.org/10.18112/openneuro.ds007768.v1.0.0) and analysis code is available at (doi.org/10.5281/zenodo.20396101). Additional information is available upon reasonable request.

Supplemental material

Supplemental material for this article is available online.

References

Nichols

DE.

Psychedelics. Pharmacol Rev 2016; 68: 264–355.

Vollenweider

Kometer

The neurobiology of psychedelic drugs: implications for the treatment of mood disorders. Nat Rev Neurosci 2010; 11: 642–651.

Vogt

Ley

Erne

, et al. Acute effects of intravenous DMT in a randomized placebo-controlled study in healthy participants. Transl Psychiatry 2023; 13: 172.

Luan

Eckernäs

Ashton

, et al. Psychological and physiological effects of extended DMT. J Psychopharmacol 2023; 38(1): 56–67.

Labate

MacRae

Ayahuasca, ritual and religion in Brazil. London: Routledge, 2016.

Prue

Voss

RW.

Indigenous healing practice: ayahuasca. Opening a discussion. J Pastoral Care Counsel 2014; 68: 6.

Maia

Daldegan-Bueno

Wießner

, et al. Ayahuasca’s therapeutic potential: what we know – and what not. Eur Neuropsychopharmacol 2023; 66: 45–61.

Dominguez-Clave

Soler

Elices

, et al. Ayahuasca: pharmacology, neuroscience and therapeutic potential. Brain Res Bull 2016; 126: 89–101.

Labate

Cavnar

Labate

, et al. Ayahuasca healing and science. Switzerland: Springer Nature, 2021.

10.

Egger

Aicher

Cumming

, et al. Neurobiological research on N,N-dimethyltryptamine (DMT) and its potentiation by monoamine oxidase (MAO) inhibition: from ayahuasca to synthetic combinations of DMT and MAO inhibitors. Cell Mol Life Sci 2024; 81: 1–26.

11.

Grob

McKenna

Callaway

, et al. Human psychopharmacology of hoasca, a plant hallucinogen used in ritual context in Brazil. J Nerv Mental Dis 1996; 184: 86–94.

12.

Dornbierer

Marten

Mueller

, et al. Overcoming the clinical challenges of traditional ayahuasca: a first-in-human trial exploring novel routes of administration of N,N-dimethyltryptamine and harmine. Front Pharmacol 2023; 14: 1246892.

13.

Egger

Jareño Redondo

Müller

, et al. Examining the pharmacokinetic and pharmacodynamic interaction of N,N-dimethyltryptamine and harmine in healthy volunteers: a factorial dose-escalation study. Biomed Pharmacother 2025; 184: 117908.

14.

Bouso

Andión

Sarris

, et al. Adverse effects of ayahuasca: results from the Global Ayahuasca Survey. PLoS Global Public Health 2022; 2: e0000438.

15.

Shinozuka

Jerotic

Mediano

, et al. Synergistic, multi-level understanding of psychedelics: three systematic reviews and meta-analyses of their pharmacology, neuroimaging and phenomenology. Transl Psychiatry 2024; 14: 485.

16.

Moujaes

Preller

, et al. Toward mapping neurobehavioral heterogeneity of psychedelic neurobiology in humans. Biol Psychiatry 2023; 93: 1061–1070.

17.

McCulloch

DE-W

Knudsen

Barrett

, et al. Psychedelic resting-state neuroimaging: a review and perspective on balancing replication and novel analyses. Neurosci Biobehav Rev 2022; 138: 104689.

18.

Soares

Lima

Teixeira

, et al. Ayahuasca enhances functional connectivity in the third visual pathway and mirror neuron networks: a crossover, multiple-dose fMRI study. Soc Cogn Affect Neurosci 2026; 21(1): nsag004.

19.

Lebedev

Kaelen

Lövdén

, et al. LSD-induced entropic brain activity predicts subsequent personality change. Hum Brain Mapp 2016; 37: 3203–3213.

20.

Viol

Palhano-Fontes

Onias

, et al. Shannon entropy of brain functional complex networks under the influence of the psychedelic ayahuasca. Sci Rep 2017; 7: 7388.

21.

Schartner

Carhart-Harris

Barrett

, et al. Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin. Sci Rep 2017; 7: 46421.

22.

Viol

Palhano-Fontes

Onias

, et al. Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca. Entropy 2019; 21: 128.

23.

Timmermann

Roseman

Schartner

, et al. Neural correlates of the DMT experience assessed with multivariate EEG. Sci Rep 2019; 9: 16324.

24.

Timmermann

Roseman

Haridas

, et al. Human brain effects of DMT assessed via EEG-fMRI. Proc Natl Acad Sci 2023; 120(13): e2218949120.

25.

Mallaroni

Mason

Kloft

, et al. Shared functional connectome fingerprints following ritualistic ayahuasca intake. Neuroimage 2024; 285: 120480.

26.

Avram

Fortea

Wollner

, et al. Large-scale brain connectivity changes following the administration of lysergic acid diethylamide, d-amphetamine, and 3,4-methylenedioxyamphetamine. Mol Psychiatry 2025; 30(4): 1297–1307.

27.

Sampedro

de la Fuente Revenga

Valle

, et al. Assessing the psychedelic ‘after-glow’ in ayahuasca users: post-acute neurometabolic and functional connectivity changes are associated with enhanced mindfulness capacities. Int J Neuropsychopharmacol 2017; 20: 698–711.

28.

Pasquini

Palhano-Fontes

Araujo

DB.

Subacute effects of the psychedelic ayahuasca on the salience and default mode networks. J Psychopharmacol 2020; 34: 623–635.

29.

Cumming

Egger

Knudsen

GM.

Molecular brain imaging of psychedelic action. In: Nutt

Thurgur

(eds) International review of neurobiology: psychedelics in psychiatry. Cambridge, MA: Academic Press, 2025.

30.

Vollenweider

Leenders

Scharfetter

, et al. Positron emission tomography and fluorodeoxyglucose studies of metabolic hyperfrontality and psychopathology in the psilocybin model of psychosis. Neuropsychopharmacology 1997; 16: 357–372.

31.

Gouzoulis-Mayfrank

Schreckenberger

Sabri

, et al. Neurometabolic effects of psilocybin, 3,4-methylenedioxyethylamphetamine (MDE) and d-methamphetamine in healthy volunteers. A double-blind, placebo-controlled PET study with [18F]FDG. Neuropsychopharmacology 1999; 20: 565–581.

32.

Sanches

de Lima Osorio

Dos Santos

, et al. Antidepressant effects of a single dose of ayahuasca in patients with recurrent depression: A SPECT study. J Clin Psychopharmacol 2016; 36: 77–81.

33.

Riba

Romero

Grasa

, et al. Increased frontal and paralimbic activation following ayahuasca, the pan-Amazonian inebriant. Psychopharmacology (Berl) 2006; 186: 93–98.

34.

Hermle

Fünfgeld

Oepen

, et al. Mescaline-induced psychopathological, neuropsychological, and neurometabolic effects in normal subjects: experimental psychosis as a tool for psychiatric research. Biol Psychiatry 1992; 32: 976–991.

35.

Aicher

Mueller

Dornbierer

, et al. Potential therapeutic effects of an ayahuasca-inspired N,N-DMT and harmine formulation: a controlled trial in healthy subjects. Front Psychiatry 2024; 14: 1302559.

36.

Cumming

Dias

Gormsen

, et al. Single time point quantitation of cerebral glucose metabolism by FDG-PET without arterial sampling. EJNMMI Res 2023; 13: 104.

37.

Sari

Mingels

Alberts

, et al. First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients. Eur J Nucl Med Mol Imaging 2022; 49: 1997–2009.

38.

Pigg

Munk

Dias

, et al. “Snake” reference region for aortic input function extraction from dynamic PET data. J Nucl Med 2022; 63: 4006.

39.

Wang

Feng

. A study on physiological parameter estimation accuracy for tracer kinetic modeling with positron emission tomography (pet). In: 1992 American Control Conference, Chicago, IL, USA, 24–26 June 1992, pp. 1632–1633. New York, NY: IEEE.

40.

Phelps

Huang

Hoffman

, et al. Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-fluoro-2-deoxy-D-glucose: validation of method. Ann Neurol 1979; 6: 371–388.

41.

Boré

Guay

Bedetti

, et al. Dcm2Bids, Zenodo 2023; https://doi.org/10.5281/zenodo.8436509.

42.

Schwarz

Kremers

Wiste

, et al. Changing the face of neuroimaging research: comparing a new MRI de-facing technique with popular alternatives. Neuroimage 2021; 231: 117845.

43.

Esteban

Markiewicz

Blair

, et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 2019; 16: 111–116.

44.

Fischl

Sereno

Tootell

, et al. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp 1999; 8: 272–284.

45.

Nørgaard

Galassi

mnoergaard/petprep_extract_tacs: v0.0.5, https://github.com/mnoergaard/petprep_extract_tacs (2024, accessed 3 July 2025).

46.

Nørgaard

Galassi

and vbeliveau. mnoergaard/petprep_hmc: v.0.0.9 (v.0.0.9). Zenodo. https://doi.org/10.5281/zenodo.14168647

47.

Tjerkaski

Cervenka

Farde

, et al. Kinfitr—an open-source tool for reproducible PET modelling: validation and evaluation of test-retest reliability. EJNMMI Res 2020; 10: 77.

48.

Patlak

Blasberg

Fenstermacher

JD.

Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab 1983; 3: 1–7.

49.

Measurement of the global lumped constant for 2-deoxy-2-[18F]fluoro-D-glucose in normal human brain using [15O]water and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography imaging a method with validation based on multiple methodologies. Mol Imaging Biol 2003; 5: 32–41.

50.

Yeo

BTT

Krienen

Sepulcre

, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 2011; 106: 1125–1165.

51.

Mueller

Aicher

Dornbierer

, et al. Pharmacokinetics and pharmacodynamics of an innovative psychedelic N,N-dimethyltryptamine/harmine formulation in healthy participants: a randomized controlled trial. Int J Neuropsychopharmacol 2024; 28(1): pyaf00128.

52.

Shannon

CE.

A mathematical theory of communication. Bell Sys Tech J 1948; 27: 379–423.

53.

Larivière

Bayrak

Vos

Wael

, et al. BrainStat: a toolbox for brain-wide statistics and multimodal feature associations. Neuroimage 2023; 266: 119807.

54.

Vaishnavi

Vlassenko

Rundle

, et al. Regional aerobic glycolysis in the human brain. Proc Natl Acad Sci 2010; 107: 17757–17762.

55.

Vollenweider

Leenders

Scharfetter

, et al. Metabolic hyperfrontality and psychopathology in the ketamine model of psychosis using positron emission tomography (PET) and [18F]fluorodeoxyglucose (FDG). Eur Neuropsychopharmacol 1997; 7: 9–24.

56.

Levin

AW.

Hemispheric annealing and lateralization under psychedelics (HEALS): a novel hypothesis of psychedelic action in the brain. J Psychopharmacol 2025; 39: 416–430.

57.

Goldberg

Podell

Lovell

Lateralization of frontal lobe functions and cognitive novelty. J Neuropsychiatry Clin Neurosci 1994; 6: 371–378.

58.

Grome

Harper

AM.

Local cerebral glucose utilisation following indoleamine- and piperazine-containing 5-hydroxytryptamine agonists. J Neurochem 1986; 46: 117–124.

59.

Ray

TS.

Psychedelics and the human receptorome. PLoS One 2010; 5: e9019.

60.

Egger

Gudmundsen

Jessen

, et al. A pilot study of cerebral metabolism and serotonin 5-HT2A receptor occupancy in rats treated with the psychedelic tryptamine DMT in conjunction with the MAO inhibitor harmine. Front Pharmacol 2023; 14: 1140656.

61.

Aalling

Nedergaard

DiNuzzo

Cerebral metabolic changes during sleep. Curr Neurol Neurosci Rep 2018; 18: 57.

62.

Cheng

Lei

Z-G

Chu

, et al. N, N-Dimethyltryptamine, a natural hallucinogen, ameliorates Alzheimer’s disease by restoring neuronal Sigma-1 receptor-mediated endoplasmic reticulum-mitochondria crosstalk. Alzheimers Res Ther 2024; 16: 95.

63.

Hatzipantelis

Olson

DE.

The effects of psychedelics on neuronal physiology. Ann Rev Physiol 2024; 86: 27–47.

64.

Carhart-Harris

Leech

Hellyer

, et al. The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs. Front Hum Neurosci 2014; 8: 20.

65.

Carhart-Harris

Friston

KJ.

REBUS and the anarchic brain: toward a unified model of the brain action of psychedelics. Pharmacol Rev 2019; 71: 316–344.

66.

Rieser

Gubser

Moujaes

, et al. Psilocybin-induced changes in cerebral blood flow are associated with acute and baseline inter-individual differences. Sci Rep 2023; 13: 17475.

67.

Fisher

Larsen

Plavén-Sigray

, et al. Discordance between spatial and population correlations from human brain imaging data. Hum Brain Mapp 2025; 46(17): e70421.

68.

Andersen

KAA

Carhart-Harris

Nutt

, et al. Therapeutic effects of classic serotonergic psychedelics: a systematic review of modern-era clinical studies. Acta Psychiatr Scand 2021; 143: 101–118.

69.

Schwartz

JB.

The influence of sex on pharmacokinetics. Clin Pharmacokinet 2003; 42: 107–121.

70.

Nair

Ahmed

Kin

NM.

Biochemistry and pharmacology of reversible inhibitors of MAO-A agents: focus on moclobemide. J Psychiatry Neurosci 1993; 18: 214–225.

71.

Zhukovsky

Alsiö

Jupp

, et al. Perseveration in a spatial-discrimination serial reversal learning task is differentially affected by MAO-A and MAO-B inhibition and associated with reduced anxiety and peripheral serotonin levels. Psychopharmacology (Berl) 2017; 234: 1557–1571.

72.

Ables

Israel

Wood

, et al. A Phase 1 single ascending dose study of pure oral harmine in healthy volunteers. J Psychopharmacol 2024; 38(10): 911–923.

73.

Aday

Simonsson

Schindler

EAD

, et al. Addressing blinding in classic psychedelic studies with innovative active placebos. Int J Neuropsychopharmacol 2025; 28(4): pyaf023.

74.

Henriksen

Lonsdale

Fuglø

, et al. Non-invasive quantification of cerebral glucose metabolism using Gjedde-Patlak plot and image-derived input function from the aorta. Neuroimage 2022; 253: 119079.

75.

Lundgaard

Xie

, et al. Direct neuronal glucose uptake heralds activity-dependent increases in cerebral metabolism. Nat Commun 2015; 6: 6807.

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