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Learn more: PMC Disclaimer | PMC Copyright Notice Hum Brain Mapp . 2026 Apr 9;47(5):e70522. doi: 10.1002/hbm.70522 Search in PMC Search in PubMed View in NLM Catalog Add to search Investigating Emotional Reactivity in Experienced Users of Psychedelics: A Cross‐Sectional fMRI Study Paweł Orłowski Paweł Orłowski 1 Centre for Brain Research, Jagiellonian University, Kraków, Poland 2 Doctoral School in the Social Sciences, Jagiellonian University, Kraków, Poland Find articles by Paweł Orłowski 1, 2, ✉ , Aleksandra Domagalik Aleksandra Domagalik 1 Centre for Brain Research, Jagiellonian University, Kraków, Poland Find articles by Aleksandra Domagalik 1 , Michał Bola Michał Bola 1 Centre for Brain Research, Jagiellonian University, Kraków, Poland Find articles by Michał Bola 1 Author information Article notes Copyright and License information 1 Centre for Brain Research, Jagiellonian University, Kraków, Poland 2 Doctoral School in the Social Sciences, Jagiellonian University, Kraków, Poland * Correspondence: Paweł Orłowski ( [email protected] ) ✉ Corresponding author. Revised 2026 Jan 28; Received 2025 Oct 22; Accepted 2026 Mar 26; Collection date 2026 Apr 1. © 2026 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. PMC Copyright notice PMCID: PMC13063118 PMID: 41954041 ABSTRACT Classic psychedelics profoundly alter emotional states, inducing intense acute experiences lasting hours, followed by subtler, longer‐lasting changes in emotional reactivity that can persist for weeks. While experimental and clinical studies document these prolonged effects, the highly context‐dependent nature of psychedelic experiences leaves open the question of whether naturalistic, nonclinical use similarly modulates emotional processing. To investigate this, we conducted a preregistered, cross‐sectional fMRI study comparing experienced psychedelic users (≥ 10 lifetime uses; N = 33) with closely matched nonusers ( N = 34). Participants performed an emotional face recognition task, and we examined behavioral performance and neural responses to angry, happy, and fearful facial expressions. Behavioral results revealed that psychedelic users recognized angry expressions more quickly and accurately, indicating enhanced processing efficiency for threat‐related stimuli. Consistent with this, whole‐brain fMRI analyses showed reduced activation to anger in key limbic and salience network regions. Psychedelic users also exhibited heightened responses to happy expressions in parietal and sensorimotor cortices—aligning with prior clinical observations—as well as increased precuneus activation to fearful expressions. Region‐of‐interest analyses further demonstrated reduced differentiation between emotional categories in two default mode network nodes: the frontal medial cortex and parahippocampal gyrus. These findings provide a nuanced characterization of neurofunctional changes in emotional processing linked to repeated naturalistic psychedelic use. By bridging clinical and real‐world contexts, this work deepens our understanding of the potential long‐term consequences of psychedelics and complements existing evidence from controlled therapeutic settings. Keywords: classic psychedelics, emotional reactivity, emotions, fMRI, long‐term effects, naturalistic use Key Points Psychedelic users demonstrated faster and more accurate recognition of angry facial expressions compared to nonusers, suggesting enhanced processing efficiency of threat‐related stimuli during the emotional face classification task. Psychedelic users exhibited lower activation in limbic and salience network regions in response to anger, but greater activation in sensorimotor and parietal regions in response to happiness, and greater precuneus activation to fear in comparison to nonusers. Region‐of‐interest analyses revealed reduced differentiation of emotional categories in default mode network regions (frontal medial cortex and parahippocampal gyrus); however, no differences in amygdala activation were found. This cross‐sectional fMRI study compared experienced psychedelic users and matched nonusers on behavioral and neural responses to emotional facial expressions. Findings reveal neurofunctional differences between groups in emotional reactivity, highlighting both similarities and important distinctions in the long‐term effects of naturalistic versus controlled (clinical) psychedelic use. 1. Introduction Classic psychedelics, such as psilocybin, lysergic acid diethylamide (LSD), and N,N‐dimethyltryptamine (DMT), are known for their profound ability to induce alterations in sensory perception, mood, self‐awareness, and cognition (Preller and Vollenweider 2018 ). In recent years, a growing body of evidence has been accumulated indicating that psychedelics have also the potential to induce long‐lasting psychological benefits, including enhanced well‐being, reduced symptoms of depression and anxiety, and increased psychological flexibility (Aday et al. 2020 ; Barrett et al. 2020 ; Vollenweider and Preller 2020 ; Hendricks et al. 2015 ). Modulation of emotional reactivity is hypothesized to constitute one of the core mechanisms through which psychedelics exert these long‐lasting effects (Moujaes et al. 2025 ). Interventional studies have documented acute and sustained reductions in negative affect and increases in positive affect following controlled administration of psychedelics. For example, Barrett et al. ( 2020 ) showed that positive mood changes and emotional regulation improvements persisted weeks after a psilocybin session. Similarly, controlled clinical trials by Ross et al. ( 2016 ) and Griffiths et al. ( 2016 ) indicated enhanced emotional empathy and reduced anxiety and depression, assessed via validated self‐report instruments. At the neural level, the administration of substances such as psilocybin and LSD has been shown to robustly modulate neural activity in brain regions involved in emotional processing. Most importantly, three previous fMRI studies observed that the amygdala exhibited reduced responsiveness to negatively valenced emotional stimuli during the acute phase of the psychedelic experience (Kraehenmann et al. 2015 ; Mueller et al. 2017 ; Armand et al. 2024 ). Further, in a controlled randomized clinical trial conducted by Barrett et al. ( 2020 ) lasting improvements in emotional regulation observed after psilocybin administration were accompanied by decreased amygdala reactivity to negative stimuli and increased prefrontal cortex activation linked to emotional control. Finally, in a resting‐state fMRI study, administration of DMT increased functional connectivity between the supramarginal gyrus, precuneus, posterior cingulate gyrus, amygdala, and orbitofrontal cortex, indicating psychedelic‐induced upregulation of brain regions involved in emotional processing (Soares et al. 2024 ). Psychedelics have also been shown to modulate activity of large‐scale functional networks implicated in processing of emotion. Functional magnetic resonance imaging (fMRI) studies report consistent decreases in functional connectivity and activity within the default mode network (DMN)—a network associated with self‐focused rumination, mind‐wandering, and autobiographical memory, as well as reduced activity and integrity of the salience network (SN), which is key for detecting and prioritizing emotionally relevant stimuli (Carhart‐Harris et al. 2012 ; Lebedev et al. 2015 ; Vollenweider and Preller 2020 ). These network‐level and regional alterations collectively suggest that psychedelics reshape both bottom‐up emotional processing and top‐down cognitive control mechanisms, facilitating changes in emotionality. Most of what is known about how psychedelics affect the brain comes from controlled experimental or therapeutic contexts, where participants undergo preparatory screening and receive psychological support during drug administration. In contrast, naturalistic use is more variable in a range of aspects including dosage, motivation, and environmental factors—all of which have been shown to significantly influence the acute psychedelic experience as well as its lasting effects (Hartogsohn 2016 ; Carhart‐Harris et al. 2018 ; Adamczyk et al. 2025 ). Naturalistic use accounts for the majority of psychedelic use worldwide, often driven by therapeutic intentions and increasing public awareness of clinical results. Yet it remains unclear whether the psychological and neural effects observed in clinical trials, including the effects on emotional reactivity, generalize to these less‐controlled settings. To address this question, we recently conducted a large‐scale cross‐sectional study in which questionnaire data from over 2500 participants, including regular psychedelic users, were analyzed. Analysis of the questionnaire data revealed that psychedelic use was associated with an adaptive emotional reactivity profile, where psychedelic users exhibited higher positive emotional reactivity and lower negative emotional reactivity compared to nonusers (Orłowski et al. 2022 ). A subsequent EEG study investigated neural responses to emotional facial expressions in experienced psychedelic users compared to nonusers (Orłowski et al. 2024 ). Results revealed significantly reduced early neural reactivity (N170 and N200 components) to fearful faces among users, suggesting a decreased automatic neural response to negative emotional stimuli. No differences were observed in later cognitive ERP components (P200, P300), indicating that modulation might be specific to early perceptual stages of emotional processing. Furthermore, we did not find any effects of increased positive emotional reactivity in psychedelic users. Other naturalistic studies have also reported associations between psychedelic use and improved emotion regulation, mood and well‐being (e.g., Watts et al. 2017 ; Thomson and Thomacos 2025 ), although neuroimaging studies investigating the persistent brain changes associated with prolonged neural effects of naturalistic psychedelic use remains scarce. Building upon prior EEG findings from our group (Orłowski et al. 2024 ), the current study employs fMRI to explore whole‐brain and region‐specific neural activity during an emotional face classification task. We aimed to investigate whether brain activity patterns evoked by perceiving emotional facial expressions differ between regular users of classic psychedelics and nonusers. Guided by evidence from acute pharmacological studies (Carhart‐Harris et al. 2012 ; Lebedev et al. 2015 ; Stoliker et al. 2022 ), we hypothesize that users will show attenuated neural reactivity to negative emotional stimuli, reflecting a persistent modulation of emotion‐processing neural networks. 2. Materials and Methods The methods and hypotheses for this study were preregistered on the Open Science Framework ( https://doi.org/10.17605/OSF.IO/CQUJR ). The study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Ethics Committee of SWPS University of Social Sciences and Humanities, Warsaw, Poland (approval no. 13/2020). 2.1. Recruitment Survey A total of 2573 individuals completed the online recruitment survey, which was created in the Qualtrics software (Qualtrics, Provo, UT) and disseminated through social media profiles of the Centre for Brain Research and several Polish organizations supporting harm reduction and drug policy (including the Polish Psychedelic Society, Społeczna Inicjatywa Narkopolityki, and Terapia; Psychodeliczna.info ). Participants read and electronically accepted the General Data Protection Regulation (GDPR) clause and the informed consent form before filling out the survey. The survey content was presented in a fixed sequence, reflecting the order described here. It began by collecting basic sociodemographic information, including sex, age, education level, and place of residence. Next, participants completed standardized screening measures for alcohol use (the Alcohol Use Disorders Identification Test—Concise, AUDIT‐C; Bush et al. 1998 ) and cannabis use (items 1–3 of the Cannabis Use Disorders Identification Test—Revised, CUDIT‐R; Adamson et al. 2010 ). Following this, respondents were asked whether they had ever used, or intended to use in the future, any substance classified as a classic psychedelic (i.e., LSD, psilocybin mushrooms, DMT, changa, mescaline, or ayahuasca). Those who indicated at least one psychedelic experience were additionally asked to specify the number of lifetime and past‐year uses. Participants also reported their lifetime and past‐year nonmedical use of other psychoactive substances, including stimulants (e.g., cocaine, amphetamine, mephedrone), empathogens (e.g., 3,4‐methylenedioxymethamphetamine (MDMA), 3,4‐methylenedioxyamphetamine (MDA)), dissociatives (e.g., ketamine, dextromethorphan—DXM), benzodiazepines (e.g., alprazolam, diazepam, lorazepam), opioids (e.g., heroin, morphine), and synthetic cannabinoids (e.g., Spice, K2). Meditation practice was assessed by asking participants about their total duration of practice (in months), frequency of sessions (e.g., number of sessions per week or month), and average session length (in minutes). Participants then completed a set of psychological questionnaires, including the Perth Emotional Reactivity Scale—Short Form (PERS‐S; Preece et al. 2019 ). Other questionnaires were also administered but are not reported here as they were not analyzed in the current study. They also reported any neurological or psychiatric diagnoses and use of prescribed psychoactive medications. At the end of the survey, participants consented to be contacted for further participation in the fMRI study and provided their contact details. The survey took approximately 15 min to complete. 2.2. Group Assignment and Matching The above‐described survey responses were subsequently analyzed to select a suitable group of participants for the fMRI study. Exclusion criteria included a current or past psychiatric or neurological diagnosis, a history of substance dependence, current use of psychoactive medications (e.g., SSRIs or sedatives), contraindications for MRI scanning, or left‐handedness to avoid confounds in fMRI analysis. Applicants meeting none of these exclusion criteria were considered eligible for group assignment. The inclusion criterion for the Users group was having at least 10 lifetime experiences with classic psychedelics, while the nonusers group consisted of individuals who had never used any of the classic psychedelics but expressed a willingness to do so in the future. To minimize the potential influence of confounding variables in this cross‐sectional comparison, the two groups were matched on key demographic characteristics (age, gender, education level, and place of residence categorized by settlement size) as well as on patterns of psychoactive substance use, including alcohol and cannabis use (AUDIT‐C, CUDIT‐R scores) and lifetime and past‐year use counts for stimulants, empathogens, dissociatives, benzodiazepines, opioids, and synthetic cannabinoids. Total lifetime meditation experience was also included in the matching criteria. Matching was verified with statistical tests appropriate to the variables' types and distributions ( t ‐tests, Mann–Whitney U tests, or chi‐square tests). Participants selected through this procedure were invited for fMRI sessions and instructed to abstain from psychedelics for at least 30 days prior to scanning. 2.3. fMRI Experiment Sample The preregistered target sample size for the fMRI experiment was 70 participants. Ultimately, 69 fMRI sessions were conducted—34 psychedelic users and 35 nonusers—closely matching the recruitment plan. The data collection took place at the Centre for Brain Research, Jagiellonian University (Cracow, Poland). Data from two participants (one User) were excluded from analyses: one due to discontinuation of the fMRI session as a result of distress in the scanner, and one due to disclosure of noncompliance with inclusion criteria during the fMRI session. Detailed group characteristics of the participants included in the analysis are presented in Table 1 . TABLE 1. Descriptive statistics for demographic characteristics, substance use variables, meditation practice, questionnaire scores, and behavioral performance in the classification task for users and nonusers. Users ( N = 33) Nonusers ( N = 34) Statistic Male sex 15 (45.5%) 16 (47.1%) χ 2 (1)≈0, p = 1 M (SD) age in years 29.0 (5.16) 29.5 (7.55) t (65) = 0.30, p = 0.768 Education χ 2 (3) = 1.97, p = 0.578 Secondary 24.2% 32.4% Bachelor's degree 33.3% 23.5% Master's degree 39.4% 35.3% Doctoral degree 3.0% 8.8% Place of residence χ 2 (4) = 0.68, p = 0.954 Village 12.1% 8.8% City—population up to 50 K 12.1% 11.8% City—population from 50 K to 150 K 6.0% 2.9% City—population from 150 K to 500 K 12.1% 11.8% City—population over 500 K 57.6% 64.7% M (SD) reaction times F (3, 13,356) = 45.31, p < 0.001*** Angry faces 1.26 (0.37) 1.32 (0.37) p = 0.008** Fearful faces 1.24 (0.35) 1.31 (0.35) p = 0.120 Happy faces 1.11 (0.28) 1.06 (0.28) p = 0.169 Neutral faces 1.11 (0.27) 1.12 (0.29) p = 0.801 M Task accuracy χ 2 (3) = 9.79, p = 0.020* Angry faces 93.8% 90.4% p = 0.035* Fearful faces 95.7% 94.4% p = 0.301 Happy faces 98.4% 98.3% p = 0.981 Neutral faces 95.5% 96.2% p = 0.373 MED (IQR) Lifetime meditation in hours 18 (48.7) 60.5 (28) U = 457.0, p = 0.192 MED (IQR) AUDIT‐C (max score = 12) 3 (3) 4 (2.5) U = 684.5, p = 0.119 MED (IQR) CUDIT‐R (max score = 12) 3 (3) 2.5 (3) U = 442.5, p = 0.132 MED (IQR) Lifetime psychedelics use 18 (15) 0 (0) N/A MED (IQR) Lifetime stimulants use 1 (3) 0 (3.5) U = 475.0, p = 0.249 MED (IQR) Lifetime empathogens use 3 (5) 1 (6) U = 420.5, p = 0.073 MED (IQR) Lifetime dissociatives use 0 (1) 0 (0) U = 463.0, p = 0.137 MED (IQR) Lifetime benzodiazepines use 0 (0) 0 (0) U = 489.5, p = 0.183 MED (IQR) Lifetime opioids use 0 (0) 0 (0) U = 494.0, p = 0.092 MED (IQR) Lifetime synthetic cannabinoids use 0 (0) 0 (0) U = 544.0, p = 0.325 MED (IQR) Positive emotional reactivity (PERS‐S; max score = 45) 36 (2) 35 (8) t (65) = −1.68, p = 0.097 MED (IQR) Negative emotional reactivity (PERS‐S; max score = 45) 25 (7) 27 (9.25) t (65) = 1.35, p = 0.182 Open in a new tab Note: Groups were compared using independent sample t ‐tests, Mann–Whitney U tests, or χ 2 tests according to variable type, and linear or generalized linear mixed‐effects models (LMMs/GLMMs) for reaction times and classification accuracy in the experimental task. For behavioral measures (reaction times and task accuracy), the “Statistic” column presents the interaction effect (group × condition) derived from LMM/GLMM analyses in the section header, followed by p ‐values for post hoc pairwise comparisons (users vs. nonusers) in the corresponding rows. Statistical significance of between‐group differences is indicated by * p < 0.05, ** p < 0.01, *** p < 0.001. Bold values indicate statistically significant results. Abbreviations: AUDIT‐C, Alcohol Use Disorders Identification Test—Concise (Bush et al. 1998 ); CUDIT‐R, Cannabis Use Disorders Identification Test—Revised (Adamson et al. 2010 ); IQR, interquartile range; M, mean; MED, median; PERS‐S, Perth Emotional Reactivity Scale—Short Form (Preece et al. 2019 ); SD, standard deviation. All participants provided written informed consent and received monetary compensation of 150 PLN (approximately 35 EUR). Transportation costs of up to 160 PLN were also reimbursed. All participants were native Polish speakers with normal or corrected‐to‐normal vision. 2.4. Stimuli A set of images selected from the Warsaw Set of Emotional Facial Expression Pictures (Olszanowski et al. 2015 ) was used as a stimulus. The set comprised color photographs of 13 female models (IDs: AD, JS, KO, KP, Ks, MJ, MK1, MR1, MS, OG, PS, SO, and SS) and 13 male models (IDs: AG, DC, HW, KA, KM, MG, MK, MR2, PA, PB, PO, RA, and RB). For each model, we included four images depicting angry, fearful, happy, and neutral facial expressions, resulting in a total of 104 images used as experimental stimuli. The same stimuli were previously employed in our related EEG study (Orłowski et al. 2024 ), ensuring methodological consistency across studies. 2.5. Experimental Procedure Prior to entering the MRI scanner, participants completed a brief practice session to familiarize themselves with the emotional facial expression perception task procedure. During the experimental session, three types of neuroimaging data were acquired in the following sequence: (1) structural brain scans (T1‐weighted images), (2) functional resting‐state scans (10 min; not analyzed in the present study), and (3) task‐based functional MRI data collected during an emotional facial expression perception task (BOLD signal; approximately 28 min, split into two runs). The emotional facial expression perception task comprised 208 trials, equally distributed across four emotional conditions (Happy, Fearful, Angry, and Neutral; 52 trials per condition). The task was administered in two runs of 104 trials each, separated by a brief break. Faces of female and male models were presented in equal numbers within each condition. Images were shown in a pseudo‐randomized sequence, with constraints to ensure that no more than two consecutive trials featured the same model, emotion, or gender. On each trial, participants performed an emotion recognition task. They were instructed to maintain gaze fixation on a central cross and to identify the displayed facial emotion as quickly and accurately as possible using a four‐button response pad, with the assignment of buttons to specific emotions counterbalanced across participants. Each trial began with a fixation cross (3600 ms, jittered by ±600 ms), followed by the presentation of a face image (500 × 500 pixels) for 300 ms. After stimulus offset, a response screen appeared for 2000 ms, displaying the four emotion labels in an arrangement matching the response button mapping (Figure 1 ). Participants were required to indicate the perceived emotion by pressing the appropriate button within the allotted time. Importantly, the assignment of specific emotion labels to response buttons and their on‐screen positions was randomized across participants. FIGURE 1. Open in a new tab Scheme of the emotional perceptual task procedure. 2.6. MRI Data Acquisition MRI data were collected using a 3 T Siemens Magnetom Prisma scanner equipped with a 64‐channel head coil. Structural images were acquired using a 3D sagittal T1‐weighted MPRAGE sequence (192 slices, voxel size 0.9 × 0.9 × 0.9 mm, field of view 240 mm, TR = 2300 ms, TE = 2.32 ms, TI = 900 ms, flip angle 8°, base resolution 256, GRAPPA acceleration factor 2, phase encoding direction anterior‐to‐posterior). Functional images were collected using a T2*‐weighted 2D multiband echo‐planar imaging (EPI) sequences from the Center for Magnetic Resonance Research (CMRR), University of Minnesota (Moeller et al. 2010 ; Feinberg et al. 2010 ; Xu et al. 2013 ) (42 axial slices, voxel size 3 × 3 × 3 mm, field of view 192 mm, slice thickness 3 mm, TR = 1200 ms, TE = 27 ms, flip angle 75°, base resolution 64, GRAPPA acceleration factor 2, multiband acceleration factor 2, phase encoding direction anterior‐to‐posterior). To correct for spatial distortions in functional images, the same sequence with reverse phase was collected. 2.7. Data Analysis 2.7.1. Behavioral Data Analysis Behavioral data from the emotional facial expression classification task, recorded during the fMRI session, were analyzed for both reaction times and classification accuracy. Responses were considered correct if the participant identified the emotion corresponding to the presented facial stimulus within the 2‐s response window. Trials in which no response was given or in which the incorrect emotion was selected were classified as errors. In cases where more than one response was registered within the response window, only the first response was included in the analysis. Reaction time (RT) data, including both correct and incorrect responses, were analyzed using linear mixed‐effects models (LMMs), implemented with the lmer() function from the lme4 package in R (R Core Team 2022 ; Bates et al. 2015 ). Classification accuracy was analyzed using generalized linear mixed‐effects models (GLMMs) with a binomial link, implemented via the glmer() function from lme4. For both outcome measures, the models included group (users vs. nonusers), Emotional Condition (angry, fearful, happy, neutral), and their interaction as fixed effects, as well as a random intercept for participants. Statistical significance of fixed effects was assessed using Type III Wald chi‐square tests via the Anova() function from the car package. When a significant main effect of Emotional Condition was observed, post hoc pairwise comparisons between emotional conditions were conducted using the emmeans package, applying Holm's correction for multiple comparisons. Additionally, when a significant Group × Emotional Condition interaction was observed, post hoc pairwise comparisons between groups within each emotional condition were performed using the same approach. 2.7.2. Neuroimaging Data Analysis Functional and anatomical MRI data were preprocessed using fMRIPrep 24.0.1 (Esteban et al. 2019 ). The preprocessing workflow included correction for intensity nonuniformity and skull‐stripping of T1‐weighted anatomical images, segmentation into gray matter, white matter, and cerebrospinal fluid, and nonlinear spatial normalization to the MNI template. Functional runs underwent slice timing correction, head‐motion correction, and co‐registration to the participant's anatomical image using boundary‐based registration. Susceptibility distortion correction was performed utilizing two images with different phase‐encoding directions. Confound regressors related to head motion, global signal components, and physiological noise were extracted. All spatial transformations were concatenated and applied in a single interpolation step using cubic spline interpolation to minimize data degradation. Detailed preprocessing procedures and quality control metrics are comprehensively described in the fMRIPrep‐generated reports. A representative summary of these reports including the full description of preprocessing steps is provided in the Supporting Information . First‐level (subject‐level) analyses were performed using FEAT (FMRI Expert Analysis Tool) from FSL version 6.0.7.13 (Jenkinson et al. 2012 ). Within FEAT, additional preprocessing steps included brain extraction using Brain Extraction Tool (BET), spatial smoothing with a Gaussian kernel of 4 mm full width at half maximum (FWHM), grand‐mean intensity normalization, and temporal high‐pass filtering with a cutoff of 100 s. For each participant, separate general linear models (GLMs) were estimated for each of the two experimental runs to account for run‐specific variance. Each first‐level model included regressors for both experimental conditions and task performance. Four regressors captured the presentation of face stimuli for the four emotional conditions (angry, fearful, happy, and neutral), taking into account the duration of the stimulus presentation period for each emotion. In contrast, three additional regressors accounted for task performance—correct response trials, error trials, and no‐response trials—each of which was modeled as covering the entire trial duration, from onset of the fixation cross to the response window. All regressors were convolved with a canonical gamma hemodynamic response function (HRF) using standard parameters (phase = 0 s, standard deviation = 3 s, mean lag = 6 s). Six head motion parameters (translations and rotations along the x , y , and z axes) estimated during realignment, as well as the mean cerebrospinal fluid (CSF) signal, were included as nuisance regressors; these were modeled as regressors of no interest and were not convolved with the HRF. For each participant, first‐level contrasts from both runs were combined using a fixed‐effects model to produce condition‐specific activation maps for subsequent group analysis. 2.7.3. Whole‐Brain Analysis An exploratory (i.e., nonpreregistered) analysis of whole‐brain group‐level data was conducted using AFNI version 25.1.08 (Cox 1996 ). A linear mixed‐effects model was implemented with the 3dLME program (Chen et al. 2013 ), including Emotional Condition (Angry, Fearful, Happy, Neutral; within‐subject factor) and Group (users, nonusers; between‐subjects factor) as fixed effects. This allowed assessment of main effects and Emotional Condition × Group interactions. After model estimation, three key contrasts were computed by subtracting the activation for neutral faces from that for each emotional expression (Angry—Neutral, Fearful—Neutral, Happy—Neutral), using the neutral condition as the baseline in these difference contrasts. Group differences were evaluated for each contrast. All group‐level statistical tests used type III sums of squares, with random effects included to account for inter‐individual variability. Significance was determined using a voxel‐wise Z ‐threshold of 2.5, considering only clusters that exceeded a minimum size of 25 contiguous voxels (with cluster adjacency defined by any shared face or edge). 2.7.4. ROI Analysis The preregistered region‐of‐interest (ROI) analysis was conducted using masks derived from the Harvard‐Oxford cortical probabilistic atlas included in the FSL software. ROIs included the anterior division of the cingulate gyrus (denoted here as ACC—anterior cingulate cortex), frontal medial cortex, frontal pole, middle frontal gyrus, bilateral amygdala, fusiform gyrus (created by merging occipital fusiform gyrus, temporal occipital fusiform cortex and the anterior and posterior divisions of the temporal fusiform cortex), and parahippocampal gyrus (anterior and posterior divisions combined). ROI masks were resampled to the functional data resolution and thresholded at 50% probability to retain voxels assigned with high anatomical confidence. Composite ROIs (fusiform gyrus and parahippocampal gyrus) were formed by merging relevant subregions as specified. For each participant, mean beta values were extracted and averaged within each ROI for all emotional conditions. Statistical analysis was performed in R using mixed‐design ANOVAs (aov_ez() function from the afex package; Singmann et al. 2015 ), with group (users vs. nonusers) as a between‐subjects factor and Emotional condition as a within‐subject factor. Sphericity violations were assessed, and when present, Greenhouse–Geisser correction was applied to F ‐tests to adjust degrees of freedom accordingly. When significant main effects of Emotional condition or Emotional condition × Group interactions were observed ( p < 0.05), post hoc comparisons were performed using the emmeans() function, with Bonferroni correction applied to control for multiple comparisons. 3. Results 3.1. Behavioral Data Mixed‐effects linear modeling of reaction times revealed a significant main effect of Emotional Condition ( F (3, 13,356) = 526.76; p < 0.001; η p 2 = 0.11; 95% CI [0.10–0.12]). Post hoc pairwise comparisons indicated that reaction times differed significantly between all emotional conditions (all p < 0.001; d range = 0.09–0.57), with the longest reaction times for angry ( M = 1.31 s, SD = 0.37) and fearful faces ( M = 1.27 s, SD = 0.35) and the shortest for happy faces ( M = 1.08 s, SD = 0.28). There was no significant main effect of Group on reaction times ( F (1, 65) = 0.63; p = 0.43; η p 2 = 0.01). However, a significant Group × Emotional Condition interaction was observed ( F (3, 13,356) = 45.31; p < 0.001; η p 2 = 0.010; 95% CI [0.007, 0.014]). Post hoc comparisons exploring this interaction revealed that users responded significantly faster than nonusers during classification of angry faces (users: M = 1.26 s, SD = 0.37; nonusers: M = 1.36 s, SD = 0.37; estimate = 0.10; SE = 0.04; p = 0.008; d = 0.63; 95% CI [0.15–1.10]), while no significant group differences emerged for the other emotional conditions. Mixed‐effects logistic regression analysis of classification task accuracy revealed a significant main effect of Emotional Condition ( χ 2 (3) = 109.64; p < 0.001). Post hoc tests showed that accuracy was highest for happy faces ( M = 98.3%, SD = 12.7%) and lowest for angry faces ( M = 92.1%, SD = 27.1%). There was also a significant main effect of Group ( χ 2 (1) = 4.47; p = 0.034), with users demonstrating better overall accuracy (users: M = 95.9%, SD = 19.9%; nonusers: M = 94.8%, SD = 22.2%; OR = 1.13; d = 0.07; 95% CI [0.01, 0.05]). Moreover, we found a Group × Emotional Condition interaction effect ( χ 2 (3) = 9.79; p = 0.020). Post hoc analysis revealed that Users performed significantly better than nonusers specifically during classification of angry faces (users: M = 93.8%, SD = 24.2%; nonusers: M = 90.4%, SD = 29.5%; estimate = −0.44; SE = 0.21; p = 0.035; OR = 1.55; d = 0.24; 95% CI [0.02–0.47]), whereas no significant between‐group differences were found for fearful, happy, or neutral faces. 3.2. Whole‐Brain Analysis In the whole‐brain analysis, the hemodynamic response related to perception of emotional facial expression (Angry, Fearful, Happy) was assessed relative to the neutral baseline (i.e., the difference between each emotional condition and the neutral condition). For each emotion, significant group differences in activation (users vs. nonusers) are reported below and presented in Table 2 and Figure 2 . TABLE 2. Results of the whole‐brain analysis: anatomical regions showing significant group differences in activation for each emotional facial expression (Angry, Fearful, Happy) compared to the activation evoked by neutral faces. The Peak Z ‐value indicates the direction and magnitude of the group effect (users minus nonusers) for the emotion–neutral contrast: Positive values reflect greater activation differences in users, whereas negative values indicate lower activation differences in users compared to nonusers. Contrast Anatomical region Hemisphere Peak MNI coordinates ( x , y , z ) RAS Cluster size (voxels) Peak Z ‐value Angry Insula Left (−42, 21, −3) 117 −4.14 Supplementary motor area Left (−3, 12, 57) 72 −4.20 Inferior parietal lobule Right (66, −21, 18) 60 4.13 Inferior frontal gyrus Right (9, 30, 36) 46 −3.70 Inferior frontal gyrus Left (−48, 9, 30) 43 −3.92 Fearful Precuneus Bilateral (3, −48, 12) 26 4.28 Happy Inferior parietal lobule/supramarginal gyrus Right (57, −27, 18) 73 4.10 Inferior parietal lobule/postcentral gyrus Right (48, −30, 48) 59 3.54 Superior parietal lobule Left (−42, −39, 66) 58 3.50 Cerebellum Left (−36, −51, −33) 46 4.10 Superior parietal lobule Left (−57, −30, 54) 46 3.50 Supplementary motor area Right (3, −12, 66) 40 4.11 Inferior frontal gyrus Right (9, 33, 39) 29 −3.80 Superior parietal lobule Right (36, −36, 54) 28 3.74 Open in a new tab FIGURE 2. Open in a new tab Whole‐brain group differences in neural activation evoked by emotional facial expressions. Results of the whole‐brain analysis are presented for three contrasts, each comparing activation during perception of emotional faces to a neutral baseline: (A) Angry–Neutral, (B) Fearful–Neutral, and (C) Happy–Neutral. Statistical maps display clusters in which significant between‐group differences in activation were observed (users vs. nonusers). Yellow‐orange clusters indicate higher activation in users, whereas blue clusters represent lower activation in users compared to nonusers. Statistical thresholds: Voxel‐wise Z ≥ 2.5, cluster extent k ≥ 25 contiguous voxels (adjacency defined by shared face or edge). 3.2.1. Angry Compared to nonusers, psychedelic users showed lower activations during perception of angry faces in the left insula, left supplementary motor area, and bilateral inferior frontal gyri. Conversely, users showed higher angry‐related activation in the right inferior parietal lobule. 3.2.2. Fearful Users exhibited higher fearful‐related activation in the precuneus compared to nonusers. 3.2.3. Happy Users showed greater activation during perception of happy faces in the right inferior parietal lobule/supramarginal gyrus, right inferior parietal lobule/postcentral gyrus, left superior parietal lobule, left cerebellum, right supplementary motor area, and right superior parietal lobule. Conversely, in the right inferior frontal gyrus, Users displayed lower happy‐related activation than nonusers. 3.3. ROI In this section, we present detailed results only for ROIs in which statistically significant interactions between Emotion × Group were observed (as this interaction allows testing our main hypothesis; see Figure 3 ). Comprehensive summaries of all ROI analyses, including detailed statistics for nonsignificant effects, are provided in Supporting Information . Note that all numerical results, including those not discussed in detail here, are summarized in Table 3 . FIGURE 3. Open in a new tab Mean beta values associated with the perception of emotional facial expressions (Angry, Fearful, Happy, and Neutral) in two groups (users and nonusers) and two regions of interest (frontal medial cortex and parahippocampal gyrus). Statistical significance of post hoc comparisons is indicated: * p < 0.05, ** p < 0.01, *** p < 0.001. TABLE 3. Results of the ROI analysis. Degrees of freedom were corrected with Greenhouse–Geisser method in case of sphericity violations. ROI Effect df F p η p 2 Anterior cingulate cortex Emotion (2.94, 191.05) 1.88 0.135 0.03 Group (1.65) 1.40 0.242 0.02 Emotion × Group (2.94, 191.05) 0.59 0.617 0.01 Frontal medial cortex Emotion (2.72, 176.62) 6.18 < 0.001*** 0.09 Group (1.65) 1.08 0.302 0.02 Emotion × Group (2.72, 176.62) 2.78 0.048* 0.04 Frontal pole Emotion (2.84, 184.89) 6.32 < 0.001*** 0.09 Group (1.65) 0.59 0.444 0.01 Emotion × Group (2.84, 184.89) 1.59 0.196 0.02 Left amygdala Emotion (2.91, 189.00) 0.61 0.603 0.01 Group (1.65) 0.06 0.804 < 0.01 Emotion × Group (2.91, 189.00) 0.84 0.468 0.01 Right amygdala Emotion (2.91, 188.88) 3.13 0.028* 0.05 Group (1.65) 0.04 0.835 < 0.01 Emotion × Group (2.91, 188.88) 1.00 0.392 0.02 Parahippocampal gyrus Emotion (2.87, 186.59) 8.38 < 0.001*** 0.11 Group (1.65) 0.59 0.447 0.01 Emotion × Group (2.87, 186.59) 3.54 0.017* 0.05 Fusiform gyrus Emotion (2.81, 182.46) 2.98 0.036* 0.04 Group (1.65) 0.67 0.417 0.01 Emotion × Group (2.81, 182.46) 0.14 0.925 < 0.01 Open in a new tab Note: Statistical significance: * p < 0.05, *** p < 0.001. 3.3.1. Frontal Medial Cortex In the frontal medial cortex, where mean beta values were predominantly negative (indicating deactivation relative to baseline), we found a significant main effect of Emotional condition ( F = 6.18; p < 0.001; η p 2 = 0.09): the deactivation level was significantly greater—meaning lower beta values—during the perception of both Angry ( t = −3.50; p = 0.005; d = 0.87; 95% CI [0.36–1.37]) and Fearful faces ( t = −3.20; p = 0.013; d = 0.79; 95% CI [0.29–1.30]) compared to Happy faces. No significant main effect of Group was detected ( F = 1.08; p = 0.302; η p 2 = 0.02). A significant Emotion × Group interaction was found ( F = 2.78; p = 0.048; η p 2 = 0.04). Post hoc analyses revealed that, within the nonuser group, deactivation for both angry ( t = −4.65; p < 0.001; d = 1.15; 95% CI [0.63–1.68]) and fearful faces ( t = −3.33; p = 0.009; d = 0.83; 95% CI [0.32–1.33]) was significantly greater than for happy faces, but no significant differences between emotional conditions were observed within the user group. No significant between‐group differences were found within any of the emotional conditions. 3.3.2. Parahippocampal Gyrus In the parahippocampal gyrus, where mean beta values were predominantly negative (again showing deactivation relative to baseline), there was a significant main effect of Emotional condition ( F = 8.38; p < 0.001; η p 2 = 0.11). Post hoc comparisons showed that deactivation was significantly greater during the perception of Angry ( t = −4.24; p < 0.001; d = 1.05; 95% CI [0.53–1.57]) and Fearful faces ( t = −3.99; p = 0.001; d = 0.99; 95% CI [0.47–1.50]) compared to Happy faces. Additional comparisons revealed stronger deactivation for Angry faces relative to Neutral faces ( t = −2.80; p = 0.040; d = 0.69; 95% CI [0.19–1.19]). The main effect of Group was not significant ( F = 0.59; p = 0.447; η p 2 = 0.01). A significant Emotion × Group interaction was found ( F = 3.54; p = 0.017; η p 2 = 0.05): in the nonuser group, deactivation for both Angry ( t = −4.86; p < 0.001; d = 1.21; 95% CI [0.67–1.73]) and Fearful faces ( t = −4.83; p = 0.001; d = 1.20; 95% CI [0.67–1.72]) was significantly greater than for happy faces, and deactivation was also stronger for angry compared to neutral faces ( t = −3.30; p = 0.010; d = 0.82; 95% CI [0.31–1.32]). No significant differences between emotional conditions were observed within the User group, nor were any between‐group effects found within any specific Emotional condition. 4. Discussion Psychedelic substances are able to strongly affect the subjective qualities of emotional experiences (Preller and Vollenweider 2018 ). Therefore, studying perception and processing of emotions has been one of the core areas of interest in psychedelic research. Extensive empirical evidence indeed supports the view that emotional reactivity might be modulated, both acutely and in the long‐term, by psychedelic substances (Vollenweider and Preller 2020 ). Importantly, adaptive changes in emotional reactivity—such as reduced sensitivity to negative affect—have been proposed to underpin many of the mental health benefits related to psychedelic use (Moujaes et al. 2025 ). However, the majority of data comes from highly controlled clinical studies, in which the context of psychedelics intake is not representative of real‐world environments. Not much is known about the effects of using psychedelics in a naturalistic context as relevant research remains scarce and is often limited by methodological challenges, such as the retrospective and self‐report nature of the employed measures (Carvalho et al. 2025 ). To address this gap, the current study investigated differences in the perception of emotional facial expressions between experienced psychedelic users and a matched group of nonusers using behavioral and fMRI data. 4.1. Main Findings Analysis of behavioral data from the emotion recognition task revealed that psychedelic users were faster when recognizing angry faces compared to nonusers. Importantly, this effect was specific to angry faces, as no significant group differences were observed for fearful, happy, or neutral faces. Regarding recognition accuracy, we found higher overall accuracy in the users group but, in line with the reaction times analysis, this effect was mainly driven by users' better recognition of angry faces. We interpret this pattern of results as indicating enhanced processing efficiency of threat‐related information among psychedelic users. Crucially, given that shorter reaction times were accompanied by higher accuracy, this performance is unlikely to result from an impulsive response bias (i.e., a speed‐accuracy trade‐off). One plausible interpretation of the observed effect is that users might be less prone to interference from emotional, threat‐related information when performing the recognition task compared to nonusers. This suggests a more efficient regulation of the emotional response. Such regulation would allow subjects to override the typical ‘freezing’ response to threat and maintain cognitive control, enabling rapid behavioral integration despite the threatening nature of the stimulus. In line, Stroud et al. ( 2018 ) reported that patients suffering from treatment‐resistant depression undergoing psilocybin‐assisted therapy exhibited improved emotion recognition performance, in terms of both reaction times and accuracy, in an emotional face recognition task (however no emotion‐specific differences in performance were observed in their study). Together, their findings and the behavioral enhancement observed in our study suggest that these effects are attributable to psychedelic use rather than to other uncontrolled factors. Nevertheless, we cannot rule out alternative explanations. For instance, the observed faster and more accurate responses could hypothetically stem from increased vigilance or a specific attentional bias toward threatening cues, allowing for more rapid detection. While high vigilance is often linked to anxiety, in the context of our experiment it could simply reflect a specific sensitivity to emotional cues facilitating the response. In line with the behavioral outcome, whole‐brain fMRI analyses revealed that psychedelic users exhibited lower activations in key limbic and SN regions during perception of angry faces. This result aligns with findings from previous clinical and experimental studies, which consistently reported that psychedelics attenuated the processing of negative emotional stimuli and enhanced emotional regulation (e.g., Kraehenmann et al. 2015 ; Barrett et al. 2020 ). However, we also observed that psychedelic users showed increased activation in the precuneus in response to fearful faces. Notably, this effect was not reflected in behavioral performance, and it contrasts with the patterns reported in previous studies. Regarding positive stimuli, perception of happy faces resulted in generally greater activations in widespread parietal and sensorimotor regions among psychedelic users. Although this pattern was not accompanied by significant differences in behavioral measures, it is consistent with accumulating evidence that psychedelics enhance neural sensitivity to positively valenced emotional stimuli. Previous functional neuroimaging studies have reported greater activation of parietal, sensorimotor, and prefrontal regions following psychedelic administration in response to positive emotions (Moujaes et al. 2025 ; Barrett et al. 2020 ). Additionally, psilocybin has been shown to bias facial emotion recognition and goal‐directed behavior toward positive rather than negative emotions (Kometer et al. 2012 ). Such enhanced reactivity to positive emotional stimuli has been linked to improvements in mood and well‐being frequently reported after psychedelic experiences. Therefore, our findings align with existing evidence from clinical studies and suggest that similar neurofunctional patterns may be associated with naturalistic psychedelic use. Furthermore, our previous work revealed, first, that psychedelics users reported lower negative and higher positive emotional reactivity when measured by questionnaires (Orłowski et al. 2022 ); and second, that they exhibited reduced amplitudes of early ERP components in response to fearful faces, likely indicating attenuated early‐stage automatic processing of negative emotion (Orłowski et al. 2024 ). Importantly, despite differences in behavioral paradigms used—specifically a passive viewing paradigm with emotion as a task‐irrelevant feature in the EEG study versus an explicit emotion recognition task in the fMRI study—this pattern of attenuated responsiveness to negative stimuli was observed in both studies, providing converging evidence of their reliability. Regarding differences, the current study indicates increased neural responses to positive emotional stimuli in psychedelic users, whereas a similar effect was not observed in the EEG study. This discrepancy may stem from the fact that fMRI might be better suited to detect broader, integrative neural network responses that are not accessible through EEG. Additionally, positive emotional processing may be particularly sensitive to task characteristics, as positive emotions are generally less salient than negative ones (Vaish et al. 2008 ), making explicit processing of emotions potentially a better way to detect related effects. Finally, between group differences in emotional processing were further confirmed in the ROI analyses. Specifically, we observed reduced differentiation of brain responses to emotional categories in the frontal medial cortex and the parahippocampal gyrus. Both areas are hubs of the DMN, with the former constituting a core region and the latter part of the medial‐temporal subsystem. Thus, our findings suggest that naturalistic psychedelic use may be associated with distinct profiles of DMN function, reflected in diminished neural sensitivity to distinct emotional categories, particularly through reduced reactivity to negative emotions. This result resonates with clinical observations that psychedelics decrease negative affect and improve emotional regulation (e.g., Ross et al. 2016 ; Kraehenmann et al. 2015 ; Roseman et al. 2018 ; Barrett et al. 2020 ), potentially by modulating DMN activity. Furthermore, diminished differentiation within this system is consistent with reports linking reduced DMN overactivity to alleviated rumination and mood improvement in depression (Hamilton et al. 2011 , 2015 ), conditions for which psychedelic therapies have demonstrated efficacy (Carhart‐Harris et al. 2021 ). Thus, our result of a flattened emotional response profile within DMN regions may represent a neurobiological correlate of the adaptive pattern of emotional functioning associated with naturalistic psychedelic use. These findings can be further contextualized within the framework of predictive processing and the REBUS model (Relaxed Beliefs Under Psychedelics; Carhart‐Harris and Friston 2019 ). According to this framework, psychedelics act by reducing the precision and weight of high‐level priors—rigid top‐down beliefs or expectations—thereby increasing the sensitivity to bottom‐up sensory information. From the perspective of emotion regulation frameworks, rigid priors in response to threat often manifest as maladaptive, habitual defensive reactions (e.g., freezing or avoidance) that consume cognitive resources (Richards and Gross 2000 ; Eysenck et al. 2007 ). The observed enhancement in processing efficiency among users might therefore reflect a long‐term adaptation where these restrictive top‐down constraints are loosened. This interpretation is further supported by our ROI results, indicating that key nodes of the DMN (frontal medial cortex and parahippocampal gyrus) are associated with maintaining high‐level priors. While nonusers exhibited strong, emotion‐specific deactivation patterns in these regions—suggesting active top‐down modulation to handle the threat—users showed a diminished neural differentiation between emotional conditions. This implies a mode of processing that relies less on heavy‐handed top‐down suppression and more on efficient, automatic integration of bottom‐up sensory data. Crucially, such a reduction in costly top‐down monitoring likely minimizes the cognitive latency associated with threat detection, directly accounting for the faster behavioral responses to angry faces observed in the user group. However, it is important to emphasize that this interpretation remains speculative. Given that the behavioral advantage was specific to angry faces, future research should clarify the generalizability of this effect. Notably, in the ROI analysis we observed no significant group differences in the amygdala activation, despite its known role in processing negative emotional stimuli. This finding can be considered at odds with numerous prior studies in which decreases in amygdala reactivity were observed both in the acute phase (Kraehenmann et al. 2015 ; Mueller et al. 2017 ; Armand et al. 2024 ), as well as up to 7 days (Barrett et al. 2020 ) or even 30 days after the psychedelic administration (Ross et al. 2016 ). This discrepancy may stem from contextual differences: clinical trials often include controlled dosing, therapeutic support, and motivated participants, which may amplify or prolong neural effects. In contrast, naturalistic use, characterized by variable doses, motivations, and environments, may produce subtler or less enduring modulation of amygdala activity. Moreover, prior neuroimaging studies typically measured brain activity shortly (days to weeks) after psychedelic use, whereas our sample consisted of individuals who had used psychedelics regularly but abstained from using for at least 30 days before the measurement. Therefore, it is plausible that amygdala effects observed acutely or subacutely dissipate over longer periods, and do not represent stable long‐term neural changes following naturalistic psychedelic use. To sum up, our findings from the whole‐brain and ROI analyses reveal a mixed and regionally specific pattern of effects, highlighting the nuanced and dynamic nature of psychedelic modulation of emotion processing. As Moujaes et al. ( 2025 ) emphasize, empirical evidence remains inconsistent, likely stemming from limited granularity in measuring discrete emotions, the temporal dynamics of psychedelic effects, and differences between clinical and nonclinical populations. Psychedelics may foster emotional flexibility through modulating emotion regulation, yet the temporal course and specific circuit‐level adaptations require further investigation. Overall, our results contribute to this emerging narrative by highlighting that psychedelic‐mediated emotional modulation is unlikely to be a unitary phenomenon, instead reflecting a network‐level reorganization with emotion‐ and region‐specific effects, modulated by individual and contextual factors. 4.2. Methodological Aspects and Limitations The key limitation of our work is the inability to draw causal conclusions regarding psychedelic use, which is a consequence of the cross‐sectional design of the study. Therefore, our results complement rather than extend previous clinical findings. Moreover, although demographic and lifestyle factors were matched between groups and potential confounds were statistically controlled, the influence of residual confounds and biases, as well as expectancy effects on our results cannot be ruled out. Another important limitation arises from the potential self‐selection bias in the Users group. Recruited participants reported at least 10 lifetime uses, suggesting they are likely individuals who have had predominantly positive experiences and continued psychedelic use. Such a self‐selection bias, common in naturalistic psychedelic research, may lead to an overrepresentation of individuals with favorable outcomes and underrepresentation of those who had challenging or adverse experiences (Aday et al. 2020 ; Muthukumaraswamy et al. 2022 ). Furthermore, as individuals currently under psychiatric medication or diagnosed with psychiatric disorders were excluded from our sample, the generalizability of findings to clinical populations undergoing psychedelic‐assisted psychotherapy is limited. Lastly, as the sample consisted exclusively of Polish nationals, cultural factors—such as specific social attitudes toward illicit substances—might limit the cross‐cultural generalizability of our findings. Regarding the experimental design, the use of an event‐related fMRI paradigm may have influenced our ability to detect amygdala activation differences. In contrast to block designs, which typically involve sustained presentation of stimuli from the same condition, event‐related designs present stimuli from different conditions in a randomized manner. This can reduce statistical power for detecting subtle activations, particularly in small subcortical regions such as the amygdala (Chee et al. 2003 ; Paret et al. 2016 ). Many prior neuroimaging studies of emotional face perception that reported amygdala engagement employed block designs (Barrett et al. 2020 ; Armand et al. 2024 ), potentially explaining their greater sensitivity to amygdala responses. Finally, regarding the statistical thresholding in our whole‐brain exploratory analyses, we employed a cluster‐defining threshold of Z > 2.5 with a minimum cluster extent of 25 voxels. While this approach was chosen to maximize sensitivity to subtle effects in this exploratory framework, we acknowledge that it is less conservative than rigorous family‐wise error correction or permutation testing. Consequently, the possibility of false positives cannot be entirely ruled out, and these findings should be interpreted with caution pending future replication. 5. Conclusion The present study provides novel insights into mechanisms taking part in processing of emotional facial expressions in individuals engaging in repetitive naturalistic use of classic psychedelics. While our findings must be interpreted with consideration of the inherent limitations of the employed cross‐sectional design (i.e., causality cannot be inferred) it is noteworthy that the observed pattern of results is partially consistent with findings from previous experimental and clinical studies. Therefore, our results contribute to a growing understanding of the nuanced neurofunctional alterations associated with long‐term psychedelic use and their potential relevance for emotional processing and therapeutic outcomes. Author Contributions Paweł Orłowski: conceived study, designed the online survey, programmed the online survey, analyzed data, interpreted data, drafted, and revised the manuscript. Aleksandra Domagalik: supervised data analysis, interpreted data, and revised the manuscript. Michał Bola: acquired funding, conceived study, designed study, and revised the manuscript. Funding This work was supported by Narodowe Centrum Nauki (2020/39/O/HS6/01545). Ethics Statement The study was conducted in accordance with the Declaration of Helsinki and was approved by the Human Ethics Committee of SWPS University of Social Sciences and Humanities, Warsaw, Poland (approval no. 13/2020). Conflicts of Interest The authors declare no conflicts of interest. Supporting information Data S1: hbm70522‐sup‐0001‐Supinfo.docx. HBM-47-e70522-s001.docx (20.2KB, docx) Acknowledgments This study was funded by a National Science Center Poland grant (grant no. 2020/39/O/HS6/01545). 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[ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Data S1: hbm70522‐sup‐0001‐Supinfo.docx. HBM-47-e70522-s001.docx (20.2KB, docx) Data Availability Statement The statistical models of the whole‐brain analysis as well as the data and scripts used for the ROI analyses are accessible in the OSF repository ( https://osf.io/46gmu ). Data from earlier stages of the analysis is available from the corresponding author upon reasonable request. 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