Nonlinear Dimensionality Reduction Technique Research Articles

A multi-dimensional dataset of Ordovician to Silurian graptolite specimens for virtual examination, global correlation, and shale gas exploration

Abstract. Multi-elemental and multi-dimensional data are more and more important in the development of data-driven research, as is the case in modern paleontology, in which, in an examination by experts, or some day artificial intelligence, every fossil specimen plays a fundamental role. We here release a dataset of 1550 graptolite specimens representing 113 Ordovician to Silurian graptolite species or subspecies that are significant in global stratigraphic correlation and shale gas exploration. The dataset contains 2951 high-resolution images and a data table of each specimen's scientific information, including the taxonomic, geologic, and geographic information, comments, and references. Our dataset provides images for specialists or laypeople worldwide, is supported by the tool FSIDvis (Fossil Specimen Image Dataset Visualizer), which we developed to facilitate the interactive exploration of the rich-attribution image dataset, and includes a nonlinear-dimension reduction technique, t-SNE (t-distributed stochastic neighbor embedding), to project image data into a two-dimensional space to visualize and explore the similarities. Our dataset potentially contributes to virtual examinations of specimens, global bio-stratigraphic correlation, and improvement of the shale gas exploration efficiency. All data, images and the spreadsheet are available at https://doi.org/10.5281/zenodo.6688670 (Xu, 2022).

Asset-return momentum prediction through pattern recognition

Asset-price momentum effects are important to study in finance. A novel nonparametric pattern-recognition algorithm is proposed to identify intraday momentum based on price patterns. Specifically, a classification-regression model for predicting future price-movement patterns is designed by combining Bayesian analysis and a nonparametric density-estimation method (with different similarity measures). The model is applied to asset prices in both original and compressed forms through nonlinear dimension-reduction techniques. Regardless of the data form, this model’s predictive power is statistically and empirically significant. The model most effectively predicts asset prices with high levels of long-range dependence. Empirical analysis demonstrates that the intraday-momentum effect can occur over longer time horizons than the half-hour documented in the literature. The intraday-trading strategy based on the similarity-prediction model achieves an increased expected return and a Sharpe ratio of 11% and 0.7, respectively, compared to alternative benchmark models.

T-SNE: A study on reducing the dimensionality of hyperspectral data for the regression problem of estimating oenological parameters

In recent years there is a growing importance in using machine learning techniques to improve procedures in precision agriculture: in this work we perform a study on models capable of predicting oenological parameters from hyperspectral images of wine grape berries, a specially relevant topic to boost production tasks for winemakers. Specifically, we explore the capabilities of a novel technique mostly used for visualization, t-Distributed Stochastic Neighbor Embedding (t-SNE), for reducing the dimensionality of the highly complex hyperspectral data and compare its performance with Principal Component Analysis (PCA) method, which despite the introduction of many nonlinear dimensionality reduction techniques over the years, had achieved the best results for real-world data across several studies in literature. Additionally we explore the potential of Kernel t-SNE, an extension to the t-SNE method that allows for the usage of the technique in streaming data or online scenarios. Our results show that, in a direct comparison, t-SNE achieves better metrics than PCA for most of the data sets in this work and that the regressor (Support Vector Regression, SVR) performs better with the t-SNE reduced features as inputs, accomplishing better predictions with lower error rates. Comparing the results with current literature, our shallow learning model paired with t-SNE achieves either better or on par results than those reported, even competing with more advanced models that use deep learning techniques, which should propel the introduction of t-SNE in more studies that require dimensionality reduction.

Robust correlation estimation and UMAP assisted topological analysis of omics data for disease subtyping

Deciphering information hidden in the gene expression assays for identifying disease subtypes has significant importance in precision medicine. However, computational limitations thwart this process due to the intricacy of the biological networks and the curse of dimensionality of gene expression data. Therefore, clustering in such scenarios often becomes the first choice of exploratory data analysis to identify natural structures and intrinsic patterns in the data. However, sparse and high dimensional nature of omics data prevents conventional clustering algorithms to discover subtypes that are clinically relevant and statistically significant. Hence, non-linear dimensionality reduction techniques coupled with clustering in such scenarios often becomes imperative to improve the clustering results. In this study, we present a robust pipeline to discover disease subtypes with clinical relevance. Specifically, we focus on discovering patient sub-groups that have a residual life patterns remarkably different from other sub-groups. This is significant because by refining prognosis, subtyping can reduce uncertainty in approximating patients expected outcome. The methodology present is based on robust correlation estimation, UMAP– a non-linear dimensionality reduction method and mapper– a tool from topology. Notably, we suggest a method for improving the robustness of the correlation matrix of gene expression data for improving the clustering results. The performance of the model is evaluated by applying to five cancer datasets obtained through TCGA and comparisons are performed with some state of the art methods of NEMO, RSC-OTRI and SNF with regard to log−rank test and Restricted Life Expectancy Difference. For example in GBM dataset, the minimum separation for any two discovered subtypes is 221 days which is significantly higher than the other methodologies. We also compared the results without using the robust correlation based estimate and observed that robust correlation improves separability between survival curves significantly. From the results we infer that our methodology performs better compared to other methodologies with regard to separating survival curves of patient sub-groups despite using single omics profiles of patients compared to multiple omics profiles of SNF and NEMO. Pathway over-representation analysis is performed on the final clustering results to investigate the biological underpinnings characterizing each subtype.

Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis.

Vibrational spectroscopy is a nondestructive technique commonly used in chemical and physical analyses to determine atomic structures and associated properties. However, the evaluation and interpretation of spectroscopic profiles based on human-identifiable peaks can be difficult and convoluted. To address this challenge, we present a reliable protocol based on supervised manifold learning techniques meant to connect vibrational spectra to a variety of complex and diverse atomic structure configurations. As an illustration, we examined a large database of virtual vibrational spectroscopy profiles generated from atomistic simulations for silicon structures subjected to different stress, amorphization, and disordering states. We evaluated representative features in those spectra via various linear and nonlinear dimensionality reduction techniques and used the reduced representation of those features with decision trees to correlate them with structural information unavailable through classical human-identifiable peak analysis. We show that our trained model accurately (over 97% accuracy) and robustly (insensitive to noise) disentangles the contribution from the different material states, hence demonstrating a comprehensive decoding of spectroscopic profiles beyond classical (human-identifiable) peak analysis.

Model order reduction techniques to identify submarining risk in a simplified human body model

This work investigates linear and non-linear parametric reduced order models (ROM) capable of replacing computationally expensive high-fidelity simulations of human body models (HBM) through a non-intrusive approach. Conventional crash simulation methods pose a computational barrier that restricts profound analyses such as uncertainty quantification, sensitivity analysis, or optimization studies. The non-intrusive framework couples dimensionality reduction techniques with machine learning-based surrogate models that yield a fast responding data-driven black-box model. A comparative study is made between linear and non-linear dimensionality reduction techniques. Both techniques report speed-ups of a few orders of magnitude with an accurate generalization of the design space. These accelerations make ROMs a valuable tool for engineers.

ETHNICITY-SPECIFIC EXTREME LONGEVITY VARIANTS IN A CONSORTIUM OF FOUR CENTENARIAN STUDIES

Abstract It has been shown that some longevity variants including the APOE variants have ethnicity-specific effects on EL within European ethnicities. The goal of the present study is to identify genetic variants whose effects vary by ethnicity by conducting a genome-wide association study of extreme longevity (EL: defined as living past the age at which less than 1% individuals from the 1900 - 1920 birth year cohorts survived) that includes the SNP-by-ethnicity interaction terms using a consortium of four centenarian studies: the New England Centenarian Study, the Long Life Family Study, the Southern Italian Centenarian Study, and the Longevity Gene Project. We used the Uniform Manifold Approximation and Projection (UMAP), a non-linear dimension reduction technique, to identify distinct ethnic clusters. The UMAP analysis revealed four distinct ethnic groups (Danish, Italian, Ashkenazi Jewish, and middle European) in the aggregated data set with 2223 cases and 5673 controls. Using a mixed effects logistic model with SNP-by-ethnicity interaction terms, we found 29 loci, in which the test of any interaction effect produced p< 10-5. The results showed that some variants had ethnicity-specific effects on EL. We sought for replication in two independent studies of longevity: the Danish Longevity Study and the Italian Longevity Study. In the Italian Longevity Study, rs7907949 (PFKP), rs11667516 (intergenic: RAB11B;MARCHF2), rs13245505 (intergenic: ZC3HC1;KLHDC10) replicated with a nominal significance level of 0.05. In the Danish Longevity Study, rs79853795 (PLCB1) and rs4072601 (SLC39A11) replicated. Future drug development should account for ethnic-specific differences in the genetic effects for higher efficacy for more diverse populations.

Geometric learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity

The history-dependent behaviors of classical plasticity models are often driven by internal variables evolved according to phenomenological laws. The difficulty to interpret how these internal variables represent a history of deformation, the lack of direct measurement of these internal variables for calibration and validation, and the weak physical underpinning of those phenomenological laws have long been criticized as barriers to creating realistic models. In this work, geometric machine learning on graph data (e.g. finite element solutions) is used as a means to establish a connection between nonlinear dimensional reduction techniques and plasticity models. Geometric learning-based encoding on graphs allows the embedding of rich time-history data onto a low-dimensional Euclidean space such that the evolution of plastic deformation can be predicted in the embedded feature space. A corresponding decoder can then convert these low-dimensional internal variables back into a weighted graph such that the dominating topological features of plastic deformation can be observed and analyzed.

Comparing different nonlinear dimensionality reduction techniques for data-driven unsteady fluid flow modeling

Computational fluid dynamics (CFD) is known for producing high-dimensional spatiotemporal data. Recent advances in machine learning (ML) have introduced a myriad of techniques for extracting physical information from CFD. Identifying an optimal set of coordinates for representing the data in a low-dimensional embedding is a crucial first step toward data-driven reduced-order modeling and other ML tasks. This is usually done via principal component analysis (PCA), which gives an optimal linear approximation. However, fluid flows are often complex and have nonlinear structures, which cannot be discovered or efficiently represented by PCA. Several unsupervised ML algorithms have been developed in other branches of science for nonlinear dimensionality reduction (NDR), but have not been extensively used for fluid flows. Here, four manifold learning and two deep learning (autoencoder)-based NDR methods are investigated and compared to PCA. These are tested on two canonical fluid flow problems (laminar and turbulent) and two biomedical flows in brain aneurysms. The data reconstruction capabilities of these methods are compared, and the challenges are discussed. The temporal vs spatial arrangement of data and its influence on NDR mode extraction is investigated. Finally, the modes are qualitatively compared. The results suggest that using NDR methods would be beneficial for building more efficient reduced-order models of fluid flows. All NDR techniques resulted in smaller reconstruction errors for spatial reduction. Temporal reduction was a harder task; nevertheless, it resulted in physically interpretable modes. Our work is one of the first comprehensive comparisons of various NDR methods in unsteady flows.

Active target classification using a shallow neural network with dimension reduction

Techniques for automatically identifying sonar targets using machine learning algorithms are being actively developed, and modern algorithms based on deep learning are often considered first. However, deep learning based algorithms require a lot of data to train a neural network, and if there is not enough data, overfitting easily occurs and the performance on real data can be degraded. In fact, in the case of active target detection where there is not enough data, it has been reported that using a shallow neural network after extracting appropriately designed features from the raw data showed better detection performance than applying deep learning directly to the raw data. With regard to this, we investigate the performance of a shallow neural network combined with dimensionality reduction techniques for active sonar target classification. In particular, several linear and nonlinear dimension reduction techniques are compared in terms of target classification performance, and the effects of the characteristics of background noise and the presence of reverberation on the target classification performance are discussed. [This work was supported by the research project PES4380 funded by KRISO.]

Non-Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models.

Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non‐linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed‐forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: (a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation; (b) high optically thin cirrus‐like clouds are separated from low optically thick cumulus clouds; and (c) shallow convective processes are associated with large‐scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub‐grid‐scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties.

Quantitative evaluation of nonlinear methods for population structure visualization and inference.

Population structure (also called genetic structure and population stratification) is the presence of a systematic difference in allele frequencies between subpopulations in a population as a result of nonrandom mating between individuals. It can be informative of genetic ancestry, and in the context of medical genetics, it is an important confounding variable in genome-wide association studies. Recently, many nonlinear dimensionality reduction techniques have been proposed for the population structure visualization task. However, an objective comparison of these techniques has so far been missing from the literature. In this article, we discuss the previously proposed nonlinear techniques and some of their potential weaknesses. We then propose a novel quantitative evaluation methodology for comparing these nonlinear techniques, based on populations for which pedigree is known a priori either through artificial selection or simulation. Based on this evaluation metric, we find graph-based algorithms such as t-SNE and UMAP to be superior to principal component analysis, while neural network-based methods fall behind.

Haisu: Hierarchically supervised nonlinear dimensionality reduction.

We propose a novel strategy for incorporating hierarchical supervised label information into nonlinear dimensionality reduction techniques. Specifically, we extend t-SNE, UMAP, and PHATE to include known or predicted class labels and demonstrate the efficacy of our approach on multiple single-cell RNA sequencing datasets. Our approach, "Haisu," is applicable across domains and methods of nonlinear dimensionality reduction. In general, the mathematical effect of Haisu can be summarized as a variable perturbation of the high dimensional space in which the original data is observed. We thereby preserve the core characteristics of the visualization method and only change the manifold to respect known or assumed class labels when provided. Our strategy is designed to aid in the discovery and understanding of underlying patterns in a dataset that is heavily influenced by parent-child relationships. We show that using our approach can also help in semi-supervised settings where labels are known for only some datapoints (for instance when only a fraction of the cells are labeled). In summary, Haisu extends existing popular visualization methods to enable a user to incorporate labels known a priori into a visualization, including their hierarchical relationships as defined by a user input graph.

FiberNeat: Unsupervised White Matter Tract Filtering.

Whole-brain tractograms generated from diffusion MRI digitally represent the white matter structure of the brain and are composed of millions of streamlines. Such tractograms can have false positive and anatomically implausible streamlines. To obtain anatomically relevant streamlines and tracts, supervised and unsupervised methods can be used for tractogram clustering and tract extraction. Here we propose FiberNeat, an unsupervised white matter tract filtering method. FiberNeat takes an input set of streamlines that could either be unlabeled clusters or labeled tracts. Individual clusters/tracts are projected into a latent space using nonlinear dimensionality reduction techniques, t-SNE and UMAP, to find spurious and outlier streamlines. In addition, outlier streamline clusters are detected using DBSCAN and then removed from the data in streamline space. We performed quantitative comparisons with expertly delineated tracts. We ran FiberNeat on 131 participants' data from the ADNI3 dataset. We show that applying FiberNeat as a filtering step after bundle segmentation improves the quality of extracted tracts and helps improve tractometry.

Impact of COVID-19 emergency on the psychological well-being of susceptible individuals

The current pandemic has exerted an unprecedented psychological impact on the world population, and its effects on mental health are a growing concern. The present study aims to evaluate psychological well-being (PWB) during the COVID-19 crisis in university workers with one or more diseases likely to increase the risk of severe outcomes in the event of SARS-CoV-2 infection, defined as susceptible. 210 susceptible employees of an Italian University (aged 25–71 years) were recruited during the COVID-19 second wave (October–December 2020). A group comprising 90 healthy university employees (aged 26–69 years) was also recruited. The self-report Psychological General Well Being Index (PGWBI) was used to assess global PWB and the influence on six sub-domains: anxiety, depressed mood, positive well-being, self-control, general health, and vitality. We applied non-linear dimension-reduction techniques and regression methods to 45 variables in order to assess the main demographic, occupational, and general-health-related factors predicting PWB during the COVID-19 crisis. PGWBI score was higher in susceptible than in healthy workers, both as total score (mean 77.8 vs 71.3) and across almost all subscales. Age and jobs involving high social interaction before the pandemic were inversely associated with the PWB total score, general health, and self-control subscores. The current data suggest no decline in PWB during the second wave of COVID-19 health emergency in susceptible individuals of working age. Critically, higher risk for mental-health issues appears to be inversely related to age, particularly among individuals deprived of their previous level of social interaction at work.

A drive-by bridge inspection framework using non-parametric clusters over projected data manifolds

Developing robust bridge health monitoring (BHM) frameworks based on the vehicle-mounted sensing, or so-called indirect structural health monitoring (SHM) or Drive-by Bridge Inspection is currently a rapidly growing research area. The goal is to monitor any change in the structural integrity of a bridge using the vibration responses measured from sensors installed on a vehicle passing over the bridge. To this aim, we present a novel data-driven approach by integrating a nonlinear dimensionality-reduction technique using Uniform Manifold Approximation and Projection (UMAP) together with a non-parametric clustering technique using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Ultimately, through the combined use of UMAP and HDBSCAN, it is demonstrated that such methods align perfectly well with the problems and restrictions present when analyzing drive-by bridge inspection data. That is, in the drive-by bridge inspection context data sets may have low cardinality, and the individual clusters may have different sizes and densities and do not need to have a spherical shape. Furthermore, new clusters may emerge over time as new damage cases evolve. The pairing of UMAP and HDBSCAN will be shown to assist in these issues in a principled manner. For the validation, we first make use of the theoretical formulation of the vehicle-bridge interaction system and conduct a finite element simulation of this system to obtain the moving vehicle response once traveling over five different bridge states. Further, a simply supported bridge model in the laboratory is considered for the experimental validation. The structure of the bridge model is gradually changed by adding multiple stiffener plates, and by gradually increasing a concentrated mass at mid-span. The proposed method is then applied to identify changes that occur in the laboratory bridge model. The experimental results for damage characterization demonstrate that various bridge states can successfully be separated from one another. The contributions of the work are three-fold; (1) First, a novel feature extraction and classification framework based on the combined use of UMAP and HDBSCAN is developed and validated. (2) Second, this paper demonstrates one of the early successful attempts of drive-by bridge inspection for monitoring the progressive change in the structure of a bridge based on the extensive numerical and experimental investigations. (3) Finally, the research presented in this work can open up new opportunities for condition monitoring of bridge network.

Casting multiple shadows: interactive data visualisation with tours and embeddings

Non-linear dimensionality reduction (NLDR) methods such as t-distributed stochastic neighbour embedding (t-SNE) are ubiquitous in the natural sciences, however, the appropriate use of these methods is difficult because of their complex parameterisations; analysts must make trade-offs in order to identify structure in the visualisation of an NLDR technique. We present visual diagnostics for the pragmatic usage of NLDR methods by combining them with a technique called the tour. A tour is a sequence of interpolated linear projections of multivariate data onto a lower dimensional space. The sequence is displayed as a dynamic visualisation, allowing a user to see the shadows the high-dimensional data casts in a lower dimensional view. By linking the tour to an NLDR view, we can preserve global structure and through user interactions like linked brushing observe where the NLDR view may be misleading. We display several case studies from both simulations and single cell transcriptomics, that shows our approach is useful for cluster orientation tasks. The implementation of our framework is available as an R package called liminal available at https://github.com/sa-lee/liminal.

Real-time inflation forecasting using non-linear dimension reduction techniques

In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower-dimensional set of latent factors. We model the relationship between inflation and the latent factors using constant and time-varying parameter (TVP) regressions with shrinkage priors. Our models are then used to forecast monthly US inflation in real-time. The results suggest that sophisticated dimension reduction methods yield inflation forecasts that are highly competitive with linear approaches based on principal components. Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation. Zooming into model performance over time reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle or the current COVID-19 pandemic.

MO386: Visualising and Differentiating Kidney Disorders by Urinary Peptidomics Using a Machine Learning Approach

Abstract BACKGROUND AND AIMS Currently >20 000 native peptides in urine are known that are highly dynamic and able to display the status of different organs, especially the kidney. The characterization of urinary peptide profiles (UPP) enables the depiction of kidney disease severity, progression, fibrosis, and informs about the disease etiology. Advanced machine learning algorithms enable combining the changes in the very complex UPP associated with specific disease etiologies and reducing the dataspace to only few dimensions. Here, we show the application of a supervised machine learning pipeline for the visualization of different CKD etiologies based on high-dimensional peptidomics data, toward non-invasive disease classification. METHOD The Uniform Manifold Approximation and Projection (UMAP) algorithm was used as a novel nonlinear dimensionality-reduction technique to visualize and differentiate the UPP of patients with CKD of different etiologies. UPP of individual CKD patients (with diabetic kidney disease DKD, (n = 386), IgA nephropathy (n = 743) and vasculitis (n = 150)) and 369 healthy controls were extracted from the Human Urinary Proteome Database which contains >85 000 proteomics datasets analyzed using capillary electrophoresis coupled mass spectrometry. About 80% of the extracted datasets were used as a training and 20% as validation set. RESULTS When applying supervised-UMAP to the DKD patient and control datasets, excellent separation with an F1 score of 99.5% ± 0.9% in the training set, and 93.1% ± 3.3% in the independent test set could be observed. Subsequently, this approach was applied to differentiate controls and three kidney diseases (DKD, IgA nephropathy and vasculitis) simultaneously. In the training set an accuracy of up to 98% in DN and controls, and an overall F1 score of 93.7% ± 2.3% (Figure) was achieved. In the independent test set, accuracy decreased as expected to around 90% for controls, 83.8% for IgA nephropathy, 79.2% for DKD and 66.7% for vasculitis. The overall F1 score in the test set is 81.9% ± 2.2%. Of note, controls (n = 369) were consistently classified with the highest accuracy across all groups, the disease with smallest sample size (vasculitis, n = 150) always showed the lowest accuracy. A substantial proportion of vasculitis was classified as IgA nephropathy, which has the largest sample size of n = 743. For the validation of the pipeline the permutation test was used. Permutation test was repeated 100 times using all the samples of CKD -free controls and three kidney diseases. The resulted scores were normally distributed, with a mean of 32.5% and standard deviation of 1.2%. Compared with the true F1 score, which was calculated as 81.9% from above, the probability of obtaining such a high score by chance is very low (P < 0.01). CONCLUSION We show that UMAP combined with supervised machine learning applied to high dimensional peptidomics data, enables distinguishing multiple kidney diseases with good accuracy and with very small standard deviation between multiple train-test splits. To our knowledge, our study is the first of its kind to reduce the complexity of the urinary peptidome to a single point in space, and categorize disease etiology based on the spatial information. The approach presented has the potential to enable non-invasive differential diagnosis of kidney disease etiologies. To improve accuracy of this non-invasive method, inclusion of additional clinical parameters will be tested.

Serotonergic psychedelic drugs LSD and psilocybin reduce the hierarchical differentiation of unimodal and transmodal cortex

Lysergic acid diethylamide (LSD) and psilocybin are serotonergic psychedelic compounds with potential in the treatment of mental health disorders. Past neuroimaging investigations have revealed that both compounds can elicit significant changes to whole-brain functional organization and dynamics. A recent proposal linked past findings into a unified model and hypothesized reduced whole-brain hierarchical organization as a key mechanism underlying the psychedelic state, but this has yet to be directly tested. We applied a non-linear dimensionality reduction technique previously used to map hierarchical connectivity gradients to assess cortical organization in the LSD and psilocybin state from two previously published pharmacological resting-state fMRI datasets (N = 15 and 9, respectively). Results supported our primary hypothesis: The principal gradient of cortical connectivity, describing a hierarchy from unimodal to transmodal cortex, was significantly flattened under both drugs relative to their respective placebo conditions. Between-condition contrasts revealed that this was driven by a reduction of functional differentiation at both hierarchical extremes – default and frontoparietal networks at the upper end, and somatomotor at the lower. Gradient-based connectivity mapping indicated that this was underpinned by a disruption of modular unimodal connectivity and increased unimodal-transmodal crosstalk. Results involving the second and third gradient, which, respectively represent axes of sensory and executive differentiation, also showed significant alterations across both drugs. These findings provide support for a recent mechanistic model of the psychedelic state relevant to therapeutic applications of psychedelics. More fundamentally, we provide the first evidence that macroscale connectivity gradients are sensitive to an acute pharmacological manipulation, supporting a role for psychedelics as scientific tools to perturb cortical functional organization.