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Classification of psychedelic drugs based on brain-wide imaging of cellular c-Fos expression
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Official websites use. Share sensitive information only on official, secure websites. K planned the study. All authors reviewed the manuscript before submission. Correspondence to Alex Kwan, Ph. Psilocybin, ketamine, and MDMA are psychoactive compounds that exert behavioral effects with distinguishable but also overlapping features. The growing interest in using these compounds as therapeutics necessitates preclinical assays that can accurately screen psychedelics and related analogs. We posit that a promising approach may be to measure drug action on markers of neural plasticity in native brain tissues. We therefore developed a pipeline for drug classification using light sheet fluorescence microscopy of immediate early gene expression at cellular resolution followed by machine learning. We used Shapley additive explanation to pinpoint the brain regions driving the machine learning predictions. Our results support a novel approach for screening psychoactive drugs with psychedelic properties. Keywords: Psilocybin, ketamine, MDMA, antidepressant, entactogen, drug discovery, immediate early gene, neural plasticity. Psychedelics include classic serotonergic psychedelics, such as psilocybin and 5-methoxy- N,N -dimethyltryptamine 5-MeO-DMT , and related psychoactive compounds, such as ketamine and 3,4-methylenedioxymethamphetamine MDMA. These compounds have recently gained widespread interest as potential therapeutics for neuropsychiatric disorders 1 , 2. Psilocybin with psychological support is under active investigation as a treatment for major depressive disorder and treatment-resistant depression 3 , 4 , 5 , 6 , 7. Subanesthetic ketamine has long been studied for its efficacy for treating depression 8 , 9 , 10 and post-traumatic stress disorder PTSD The research efforts culminated in the approval of esketamine nasal spray by the FDA in the United States for treatment-resistant depression 12 , Beyond the known psychedelics, there is also growing excitement for synthesizing novel psychedelic-inspired analogs that can be new chemical entities for therapeutics 16 , 17 , Ideally, the novel compounds would retain therapeutic effects while improving pharmacokinetics, minimizing perceptual effects, and eliminating cardiovascular risks. A major roadblock in this pursuit lies in developing screens that can filter thousands of psychedelic-inspired analogs to a manageable list of the most promising compounds for further in-depth characterizations. Currently, most screens operate at the molecular or behavioral level. At the molecular level, candidate compounds can be docked in silico with the structure of the 5-HT 2A receptor, followed by biochemical measurements of receptor engagement and activation of downstream G-protein and beta-arrestin pathways. This target-based approach has yielded exciting leads 19 , 20 , 21 , 22 , but assumes that the 5-HT 2A receptor is the key mediator of the therapeutic effect, which has not been proven conclusively. At the behavioral level, candidate compounds may be tested in animals for defined phenotypes. Simple characterizations such as changes in animal movement patterns may be automated to increase throughput and accuracy 23 , However, more complex behavioral assays relevant for depression suffer from limitations including poor construct validity and weak predictive power for drug efficacy in humans The development of a new screening method may complement current molecular and behavioral approaches to accelerate preclinical drug discovery. Classic psychedelics and ketamine share the ability to enhance neural plasticity in the brain, as evidenced by the rapid and persistent growth of dendritic spines in the rodent medial frontal cortex after a single dose of ketamine 26 , 27 , psilocybin 28 , and related serotonergic receptor agonists 29 , 30 , 31 , A promising approach may thus focus on quantifying indicators of neural plasticity in native brain tissues. To this end, c-Fos is an immediate early gene activated in a cell in response to increased firing activity or an external stimulus Taking classic psychedelics as an example, drug administration induces robust increases in the expression of immediate early genes 34 , 35 , including c-Fos, that can be detected starting in as few as 30 minutes in multiple brain regions 36 , More recently, technological advances in tissue clearing, light sheet fluorescence microscopy, and automated detection of nuclei have enabled high-throughput mapping of c-Fos expression in the whole mouse brain 38 , We and others have applied this method to characterize the impact of psilocybin and ketamine 40 , 41 , 42 , joining a rapidly growing number of studies using brain-wide imaging of fluorescence signals to study drugs 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , Although these early studies have provided valuable biological insights, only one or two drugs were typically included in each study thus far. Developing the method as a drug screen requires evaluating its feasibility and accuracy on a larger panel of compounds. In this study, we measured brain-wide c-Fos expression in male and female mice for 8 drug conditions, including a variety of psychedelics, related psychoactive compounds, and vehicle control. We developed a pipeline for analysis and classification based on explainable machine learning, determining performance in one-versus-rest and one-versus-one classification tasks. We implemented Shapley additive explanation to interpret the machine learning models to identify the brain regions driving the classifications. Collectively the results demonstrate brain-wide imaging of immediate early gene expression as a promising approach for preclinical drug discovery. Line, mean. Box plot of the total number of head twitches detected within a 2-hour period after drug administration. Wilcoxon rank-sum test. Experimental timeline. Cross, female individual. Circle, male individual. Inset, magnified view of the dorsal anterior cingulate cortex. For b and c , the psilocybin and saline vehicle data had been shown in a prior study We elected to investigate these compounds for several reasons. Psilocybin is a classic psychedelic that acts on the 5-HT 2A receptor. Ketamine is primarily a NMDA receptor antagonist Despite the distinct molecular targets, ketamine and psilocybin have similarities in their plasticity-promoting action and behavioral effects 55 , 56 , making ketamine an intriguing compound to contrast with psilocybin. The doses and route of administration for psilocybin and ketamine were chosen based on prior studies showing behavioral effects in mice 28 , Thus 5-MeO-DMT serves as a useful case of another tryptamine psychedelic with distinct pharmacokinetics and receptor target profile. Although bioavailable in the brain and a 5-HT 2A receptor agonist 60 , 6-fluoro-DET induces autonomic effects without causing perceptual changes in humans. Therefore, it has been used as an active, non-hallucinogenic control To corroborate these prior results, we measured the effect of 6-fluoro-DET on head-twitch response in mice using magnetic ear tags for automated detection of head movements. Our study adds to other recent studies 20 , 21 that included 6-fluoro-DET as a non-hallucinogenic tryptamine for comparison. MDMA is different from psilocybin: it is a member of the phenethylamine chemical class and has distinct pro-social and euphoric qualities MDMA can act on monoamine transporters to enhance release and inhibit reuptake of neuromodulators including serotonin, thus it has been characterized as an entactogen rather than a classic psychedelic We selected a dose of 7. Fluoxetine is a commonly prescribed antidepressant that is a selective serotonin reuptake inhibitor SSRI. Clinical interest lies in understanding the relative efficacies of SSRIs versus psilocybin 4 and whether ketamine or psilocybin is suitable for treatment-resistant depression 5 , 12 , SSRIs require chronic administration to exert therapeutic effects, therefore likely engage a mechanism of action distinct than that of psilocybin and ketamine. For these reasons, we included acute and chronic fluoxetine for this study. Control animals received a single injection of saline vehicle. Brains were collected 2 hours after the administration of the single dose or 2 hours after the administration of the last dose for the chronic fluoxetine condition Fig. The 2-hour interval was chosen assuming drug penetrance to the brain by 0. Brains were processed for tissue clearing and c-Fos immunohistochemistry see Methods. Light sheet fluorescence microscopy was used to image each brain at a resolution of 1. An example image collected from a mouse administered with psilocybin is shown in Figure 1f. There were individual differences across samples from the same drug, but also notable contrasts across different drugs. This begets questions such as: How does the individual variability compare with the differences across drugs? How well can whole-brain c-Fos maps be used to discriminate the different drugs? The pixels in the heatmap are positioned by brain region row and animal grouped by drug column. The brain regions including acronyms and other details are provided in Supplementary Table 1. To answer these questions, we developed a pipeline for quantitative comparison of the brain-wide c-Fos expression data between different drug conditions. We posited that different compounds may elicit distinct regional distribution of cellular c-Fos expression that can serve as fingerprints for classifying drugs. Normalization is important because there may be batch effects across samples. The data were then processed to scale the input data to a standard range such that the values across brain regions are more comparable and amenable to fitting machine learning models second panel, Fig. We will herein refer to the values after this preprocessing step as the c-Fos scores. The pipeline consisted of three steps. Second, the Boruta procedure is used to select the set of informative brain regions. Third, c-Fos scores from this set of brain regions were used to fit a ridge logistic regression model. The entire process was iterated using different splits of the data for times. Linear discriminant analysis of the c-Fos scores to visualize the data in a low dimensional space. The confusion matrix showing the mean proportion of predicted labels for each of the true labels across all splits. The composite precision-recall curves for each drug condition across all splits and the grand average across all drugs. The values in parentheses are the area under the precision-recall curve for the compounds. Next, we adapted the Boruta feature selection procedure 19 to determine which brain regions to include for model fitting and testing third panel, Fig. The Boruta procedure is a permutation-based method for determining feature importance. Shadow variants were created for all brain regions to create the expanded Boruta dataset. A random forest classifier was built using this Boruta dataset to determine a feature-importance value for each brain region. This permutation process is iterated times. Given that each brain region can achieve only one of two outcomes hit or no hit in each iteration, the distribution of outcomes across all iterations is a binomial distribution, and a brain region is included by the statistical criterion of exceeding 95 th percentile of the binomial distribution. Why Boruta? We used the Boruta procedure in lieu of including all brain regions, because many regions likely contribute little or nothing towards differential drug action and their inclusion in the model would increase noise and lead to overfitting. A distinctive advantage of Boruta is that brain regions do not compete with each other, but rather with the shadows. As a result, the number of brain regions selected by Boruta is not pre-determined but instead dictated by the data as needed. For the last step, the c-Fos scores from the selected brain regions are used to construct a ridge logistic regression model fourth panel, Fig. Importantly, we emphasize that we used only the training data to optimize the preprocessing parameters, run feature selection, and construct regression model. The same optimized preprocessing parameters and selected features were then later applied for the test data, ensuring no data leakage. The splits were repeated times to evaluate the prediction accuracy of the pipeline. We performed a linear discriminant analysis on the c-Fos scores of all 64 samples, just after the preprocessing step. We plotted the data for the top two linear discriminants Fig. This visualization clearly shows that the differences in c-Fos scores across drugs are more separable than the differences in c-Fos scores across samples within the same drug condition. Drugs that alter the serotonergic tone via different mechanisms of action are positioned differently along the first linear discriminant. We first tested the pipeline with the entire data set and asked the models to predict the exact drug condition. The confusion matrix shows how the predicted drug labels compared with the true drug labels Fig. Because there were 8 conditions, the chance-level accuracy was Our interpretation for the low-performance conditions is that tradeoffs must be made to solve this 8-way classification problem. The machine learning models were tasked with maximizing the overall mean classification accuracy, which was not necessarily the most ideal for distinguishing any one specific condition such as saline. Confusion matrices are calculated based on a single decision threshold, which may exaggerate true positive rate for one drug type at the expense of more false positives for another drug type. To understand our model performance from a different perspective, we plotted precision-recall curves Fig. These curves consider performance across all possible decision thresholds and summarize the results in terms of precision true positives relative to false positives and recall true positives relative to false negative. The perfect classifier would have an area under the precision-recall curve precision-recall AUC of 1. Across all drugs, the pipeline yielded a mean precision-recall AUC value of 0. This is well above the theoretical chance-level of 0. The performance based on precision-recall AUC for predicting different drugs corresponds in rank order to the accuracy in the confusion matrix. Overall, these results provide evidence that brain-wide c-Fos maps can be leveraged to identify the exact drug administered out of a panel of related psychoactive compounds. We reasoned that one-versus-one classification, where the machine learning pipeline solves a binary problem of deciding between two drugs Fig. Given the prominence of psilocybin in clinical trials and drug discovery, we were particularly interested in comparisons between psilocybin and other conditions that differ in serotonergic receptor affinities 5-MeO-DMT , mechanism of action MDMA, acute fluoxetine, ketamine , or hallucinogenic potency 6-fluoro-DET. We trained the same machine learning pipeline using subsets of data involving only two or three drugs. The binary classifiers achieved near-perfect accuracy reflected by precision-recall AUC values at or exceeding 0. These results suggest that brain-wide cellular c-Fos expression is effective at discriminating between exemplars from different drug classes, such as a classic psychedelic versus an entactogen, a classic psychedelic versus a dissociative, and a classic psychedelic versus SSRI. However, the prediction is less reliable for the specific problem of predicting a non-hallucinogenic 5-HT 2A receptor agonist relative to a classic psychedelic. Schematic illustrating the one-versus-one classification problem. The mean area under the precision-recall curve across all splits for different binary classifiers. Dark gray, real data. Light gray, shuffled data. The number of brain regions selected via the Boruta procedure for inclusion in the regression model. Heatmaps showing the fraction of splits when a cortical left or thalamic right region was included in the regression model. The regions are sorted based on usage in all classifiers. As mentioned, a feature of the Boruta procedure is that a different number of regions may be included depending on the data and the desired classification. Indeed, there were differences in the brain regions chosen for the various drug prediction problems and different training and testing splits of the same data Fig. Furthermore, we plotted how often various cortical and thalamic regions were selected by the machine learning models Fig. We will explore the importance of specific brain regions quantitatively in the next section using Shapley additive explanation. Many thalamic regions were consistently included in comparisons involving MDMA, which contributed to the higher total number of brain regions used by classifiers when MDMA was involved. Overall, the results suggest that one-versus-one drug classifications based on brain-wide c-Fos expression is highly accurate, with the machine learning models only needing data from a small number of brain regions to produce the prediction. A brain region selected by Boruta in the pipeline suggests that it is informative, yet it does not communicate the importance of its contribution to the final prediction. To better understand how the c-Fos scores in individual brain regions contribute to decisions in one-versus-one drug classifications we used Shapley additive explanation SHAP Fig. SHAP uses a game-theoretical approach to determine how the brain regions contribute to driving the machine learning regression model from a starting base value to the final output value for decision To illustrate, we present the force plot of two test brain samples in one of our cross-validation splits Fig. The top half of the plot shows the c-Fos scores in selected brain regions for the sample of psilocybin and their additive contributions to the decision. The posteromedial visual area is located between the primary visual cortex and retrosplenial cortex 69 and has been suggested to mediate visual information between the neighboring regions Lateral habenula neurons had spiking activity associated with undesirable outcomes 71 , 72 , which is consistent with their posited role in mediating depression-related symptoms 73 and contributing to antidepressant response By contrast, the c-Fos scores in the same set of selected brain regions sums to an overall negative SHAP value for the 5-MeO-DMT sample, providing the basis for the correct prediction in this case. Diagram illustrating the concept behind SHAP values. The ridge regression model is akin to a black box that takes c-Fos scores as inputs to produce a prediction. SHAP values can be computed to quantitatively assess how strong and in what direction the c-Fos score of each brain region contributes to the prediction. The values in parentheses are the absolute value of the mean difference in SHAP values between the two drug conditions. Visualization of the brain regions included in c , color coded according to the compound which evoked higher c-Fos score in the region. We also analyzed other one-versus-one classification problems using Shapley additive explanation. Given the larger number of regions in each model, the SHAP value differences tended to be smaller, because there is redundancy in the information provided by the regions. Similar to Fig. Higher c-Fos scores in these lateral cortical regions informed the model to predict psilocybin. Xi and RE are part of the midline thalamus, which receives visual inputs to mediate behavioral responses to threat Interestingly, higher c-Fos scores in these midline thalamic regions are routinely used by the machine learning models to predict ketamine. In this study, we evaluated the possibility of using whole-brain imaging of cellular c-Fos expression for drug classification. We developed a machine learning pipeline with key features including adapting the statistical Boruta procedure to select informative brain regions and using Shapley additive explanation to identify features that drive the classifications. We tested the approach using 64 mice that were administered with a panel of psychedelics and related psychoactive drugs. The results demonstrated high accuracy in various one-versus-rest and one-versus-one classification problems, supporting the utility of the approach for preclinical drug discovery. For dissemination, the data and code are available at a public repository. Immunohistochemistry can be influenced by factors such as fixation method, incubation time, antibody quality, and antigen retrieval techniques. Consequently, the c-Fos antibody staining can differ from sample to sample. This normalization step is possible when whole-brain data is acquired via light sheet fluorescence microscopy. Experimentally, the variation in antibody staining is also reduced because active electrotransport methods were used for immunolabeling. These brain regions are likely important for drug action, but shared targets of ketamine and psilocybin are not helpful for distinguishing the compounds. By design, the machine learning pipeline emphasizes brain regions with c-Fos expression changes that can discriminate between drug conditions, for which we found a short list of brain regions. We anticipate the pipeline to be useful for classifying new chemical entities. For instance, when a novel psychedelic-inspired compound is synthesized, we may predict its action in the brain by its position in the linear discriminant axes Fig. For humans, psilocybin, ketamine, and MDMA exert comparable acute behavioral effects in metrics such as experience of unity, oceanic boundlessness, and changed meaning of percepts However, MDMA preferentially induce blissful state, whereas ketamine evokes disembodiment and psilocybin induces elementary imagery and audio-visual synesthesia 63 , In one study, human participants were asked to guess the administered drug, choosing between mescaline mg and mg , LSD, and psilocybin For animals, there has been recent progress in capturing videos of freely moving mice and analyzing their motion using unsupervised machine learning methods. One study used motion sequencing method to investigate a larger panel of 30 psychoactive compounds and doses from a wide range of drug classes including benzodiazepines, antidepressants, antipsychotics, and stimulants but not psychedelics and the compounds tested in the current study to show a F1 precision-recall score of 0. Our pipeline based on brain-wide cellular c-Fos expression and machine learning therefore performed at a level comparable to earlier methods based on human and animal behaviors. As with any analysis pipeline, there are methodological choices that can affect the outcome, which can plague the interpretation as demonstrated in the field of neuroimaging Our codebase is available online for anyone to freely use, adapt, and test. We used a statistical method with the Boruta algorithm, rather than a strict threshold, for region selection. We were careful about data leakage, using only the training data for parameter optimization and feature selection, such that the prediction accuracy for test data would not be inflated. We implemented Shapley additive explanation to decipher the factors driving the decisions, which is a general approach that should find great utility in neuroscience 82 , and has already seen applications in behavioral classification There are areas of improvement for the pipeline. While we opted for the simplicity of treating each brain region on its own, regions may have correlated responses to drug administration. There may be biological reasons, such as anatomical proximity or synaptic connectivity, for clustering brain regions prior to region selection, which may outperform our procedure. Network analyses may be used to explore potential correlated responses to drugs. Furthermore, the pipeline will benefit from testing a larger range of compounds including enantiomers, other drug classes, and different doses. Finally, c-Fos is one immediate early gene. It is well characterized as an activity-dependent gene and has the advantage of nuclear labeling that permits automated detection. However, there are other immediate early genes and plasticity-related biomarkers that can provide complementary information. In summary, there is intense interest in using psychedelics for the treatment of neuropsychiatric disorders. Progress hinges on knowing more about existing psychedelics and finding new psychedelic-inspired drugs with improved characteristics. However, there is currently a paucity of reliable methods to screen psychedelics and related analogs. Here we developed and characterized an approach based on whole-brain imaging of cellular c-Fos expression. We demonstrated high prediction accuracy for drug classifications using a machine learning pipeline. We expect this and other neuroscience-based approaches to play an important role for accelerating the preclinical development of psychiatric drugs. Tissues were collected and imaged in three batches. Data from these mice were included in a previous study Tissue collection for all batches was done at Yale University, except for ketamine in the third batch that was done at Cornell University. Ketamine was prepared by diluting from the injection vial. For psilocybin, a stock solution was made and then the working solution was made from stock solution, with both solutions prepared within 1 month from the day of experiment. All the samples underwent the same tissue collection and imaging protocols. Each brain sample was stained with 3. Samples were then imaged at 3. Imaging was done blinded to treatment conditions. Fluorescence images were tile-corrected, de-striped, and registered to the Allen Brain Atlas using an automated process. For each brain, the image from the NeuN channel was registered to 8—20 atlas-aligned reference samples using SimpleElastix 87 , which implemented successive rigid, affine, and b-spline warping algorithms. The final atlas alignment value for each sample was determined by taking the average alignment generated across intermediate reference samples. Cell detection was automated by using a custom convolutional neural networked designed using the TensorFlow python package. First, a U-Net-based detection network was used to locate fluorescent puncta corresponding to c-Fos-immunolabeled cells. Second, a ResNet-based network was used to filter putative cells to arrive at a final list of cell locations. Each cell location was projected onto the Allen Brain Atlas to identify its anatomical region. Counts were then generated on a per-region basis for each sample. To correct for these differences, a scaling factor was calculated for the psilocybin, ketamine, and saline conditions individually. The factor was 2. All analyses were performed after the batch effect correction. We emphasize that this batch correction step should not affect the machine learning analysis pipeline described below. This is because the first step of the pipeline is to divide per-region count by total count in each brain, meaning that the absolute values of the cell count should have minimal influence on model fits but instead it is the relative values of the cell count e. Head movements were recorded using a magnetic ear tag system as described in detail previously Briefly, an ear tag consisted of a neodymium magnet N45, 3 mm diameter, 0. A spool of enameled cooper wire 30 AWG was used to wind around the cube like a solenoid, with the ends of the wire connected to a current-to-voltage preamplifier PP, Pyle where the voltage was captured with a computer via a data acquisition device USB, National Instruments. Each mouse was recorded using one cube. Up to four cubes could be used to record from four mice at once inside a soundproof chamber. The filtered signal was then processed for peak detection to identify individual head-twitch events. The analysis pipeline used the Python package sci-kit learn Version 1. The first step of the pipeline was preprocessing, which entails three steps: normalization, transformation, and scaling. This was done to mitigate influence of batch effects across samples. The Yeo-Johnson transformation is a generalized form of the Box-Cox transformation. The transformation leads to data values that more closely approximate a Gaussian distribution. The Yeo-Johnson transformation is parameterized by one variable, lambda. The optimal lambda parameter was calculated for each brain region independently using maximum likelihood estimation to optimize for normality. For scaling, for each brain region, the RobustScaler module in scikit-learn was used to subtract the median value and scales values by the range of the 25 th to 75 th percentile quartile scaling. We decided to do this, rather than subtracting mean value and standard-deviation scaling, because it is less sensitive to outliers. We were concerned that a model involving c-Fos scores from regions may be overfitting due to our limited sample size of 64 brains. Many regions are likely not informative and only contribute noise to the machine learning models. Therefore, we implemented a method to filter out features i. Region selection was carried out using the Boruta algorithm, as implemented in the BorutaPy package This was done by creating scrambled versions of each feature, which are called shadow features, and appending them to the original data set. This expanded data set was then used to fit a random forest classifier, as implemented in scikit-learn. We used the BorutaPy package to automatically select the number of trees for the RandomForestClassifier module based on the size of the feature set. Following this, a threshold was established based on the highest feature importance amongst shadow features. This procedure was repeated times. The distribution across these iterations created a binomial distribution. Features i. We used the c-Fos scores of the selected brain regions to fit a ridge regression model L2 normalized logistic regression. The regularization parameter C is a hyperparameter used to modulate the penalty strength. Given the interconnected nature of the exact feature set and hyperparameter, as well as our desire to eventually merge results across many cross-validation splits of the data, we opted to fix this parameter to its default value of 1. Importantly, preprocessing parameters e. Nevertheless, after those stages were fixed, the test data would undergo the same preprocessing and feature selection steps before being inputted into the ridge regression model to generate the prediction of the drug condition. Combining the outcome across the iterations, the predicted classifications were used to generate a mean confusion matrix Fig. RandomState to generate reproducible results. Remaining random states were set using a random state object. A null distribution for area under the precision recall curve was established by shuffling labels during each cross validation split prior to model fitting and label prediction Fig. SHAP values were generated in part by breaking dependencies across features and testing the influence of perturbations on individual features. This ran the risk of creating unrealistic feature combinations, because many brain regions which would normally change in lockstep may be changed individually by the algorithm to infer feature importance, which would lead to inflated feature importance scores Regions meeting this criterion were visualized using the brainrender package 92 Figs. The views and opinions expressed are those of the authors. K has received research support from Transcend Therapeutics and Freedom Biosciences. The other authors report no financial relationships with commercial interests. This section collects any data citations, data availability statements, or supplementary materials included in this article. As a library, NLM provides access to scientific literature. This is a preprint. It has not yet been peer reviewed by a journal. Find articles by Farid Aboharb. Find articles by Pasha A Davoudian. Find articles by Ling-Xiao Shao. Find articles by Clara Liao. Find articles by Gillian N Rzepka. Find articles by Cassandra Wojtasiewicz. Find articles by Mark Dibbs. Find articles by Jocelyne Rondeau. Find articles by Alexander M Sherwood. Find articles by Alfred P Kaye. Find articles by Alex C Kwan. These authors contributed equally to the work. Contributions F. PMC Copyright notice. The complete version history of this preprint is available at bioRxiv. Open in a new tab. Competing interests A. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel.
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Classification of psychedelic drugs based on brain-wide imaging of cellular c-Fos expression
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Classification of psychedelic drugs based on brain-wide imaging of cellular c-Fos expression
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