Traditional Univariate Analysis Research Articles

Development and qualification of 3 L scale-down model for large scale vaccine process on Vero cell culture using microcarriers.

Scale-down models (SDM) are pivotal tools for process understanding and improvement to accelerate the development of vaccines from laboratory research to global commercialization. In this study, a 3 L SDM representing a 50 L scale Vero cell culture process of a live-attenuated virus vaccine using microcarriers was developed and qualified based on the constant impeller power per volume principle. Both multivariate data analysis (MVDA) and the traditional univariate data analysis showed comparable and equivalent cell growth, metabolic activity, and product quality results across scales. Computational fluid dynamics simulation further confirmed similar hydrodynamic stress between the two scales.

Performance-based design of environmental parameters for offshore wind turbine foundations

Taking into account the coupling effects of wind, wave, and current on offshore wind turbines (OWTs), a combined approach utilizing non-linear finite element analysis (FEA), back propagation neural network (BPNN), and probabilistic statistical methodology (PSM) is proposed for accurate environmental parameter estimation in OWT foundation design. The structural bearing performances of OWT foundations are precisely predicted through a combination of FEA and BPNN models, and then serve as constraints for probabilistic statistical analysis. Furthermore, for PSM, a robust multivariate joint probability distribution is constructed leveraging copula functions, which not only maximizes marginal information but also effectively captures the intricate dependencies among ocean environmental parameters. Subsequently, the proposed models, along with traditional univariate analysis, are utilized to estimate the return values of diverse load combinations. The results indicate that, for a given return period, the conditional probability model yields lower wave heights and current velocities compared to univariate analysis, while the joint probability model results in lower values for all ocean parameters, making both approaches more suitable for OWT foundation design. This coupled approach presents a more rational and cost-effective solution compared to univariate analysis, potentially resulting in decreased investment costs.

Constraints of Sedimentary Environment on Shale Organic Matter Enrichment: Insights from Elemental Geochemistry and Multiple Factor Analysis.

The organic-rich shale of the Wufeng-Longmaxi formation is an important section for shale gas exploration. The traditional univariate or bivariate analysis causes researchers to have great controversy about its enrichment mechanism. This study explores the combination of multiple factor analysis (MFA) and element geochemistry to calculate the contribution rate of a paleoenvironment to organic matter enrichment and clarify the main controlling factors of organic matter enrichment. Research has shown that there is generally high productivity from the Wufeng (O3w)-Longmaxi formation (S1l) deposition. The degree of terrigenous clastic input and weathering during the period of the O3w is relatively low, and sedimentary water restriction is strong, mainly developing an anoxic-dysoxic sedimentary environment. During the deposition of S1l1, the input intensity and weathering of terrigenous debris were slightly enhanced, and the increase of the water column led to the development of an anoxic environment at the bottom of the water layer. During the S1l2+3 period, the degree of terrigenous debris and weathering is the largest, and the high oxygen content of the water column is mainly a normal oxic environment. An MFA calculation shows that the paleoproductivity and paleoredox environment of the organic-rich shale section have the highest contribution rate of about 59.57% to the enrichment of organic matter, which is higher than that of paleoclimate conditions and terrigenous clastic input, indicating that the enrichment of organic matter is mainly controlled by paleoproductivity and the preservation environment. This study provides a basis for the application of MFA in element geochemistry and can serve as a model for other studies.

Using multivariate pattern analysis to increase effect sizes for event-related potential analyses.

Multivariate pattern analysis (MVPA) approaches can be applied to the topographic distribution of event-related potential (ERP) signals to "decode" subtly different stimulus classes, such as different faces or different orientations. These approaches are extremely sensitive, and it seems possible that they could also be used to increase effect sizes and statistical power in traditional paradigms that ask whether an ERP component differs in amplitude across conditions. To assess this possibility, we leveraged the open-source ERP CORE data set and compared the effect sizes resulting from conventional univariate analyses of mean amplitude with two MVPA approaches (support vector machine decoding and the cross-validated Mahalanobis distance, both of which are easy to compute using open-source software). We assessed these approaches across seven widely studied ERP components (N170, N400, N2pc, P3b, lateral readiness potential, error related negativity, and mismatch negativity). Across all components, we found that multivariate approaches yielded effect sizes that were as large or larger than the effect sizes produced by univariate approaches. These results indicate that researchers could obtain larger effect sizes, and therefore greater statistical power, by using multivariate analysis of topographic voltage patterns instead of traditional univariate analyses in many ERP studies.

Empathy from dissimilarity: Multivariate pattern analysis of neural activity during observation of somatosensory experience

Abstract Empathy seems to rely on our ability to faithfully simulate multiple aspects of others’ inferred experiences, often using brain structures we would use during a similar experience. Much neuroimaging work in this vein has related empathic tendencies to univariate correlates of simulation strength or salience. However, novel evidence suggests that empathy may rely on the multivariate distinctiveness of these simulations. Someone whose representations of painful and non-painful stimulation are more distinct from each other may more accurately simulate that experience upon seeing somebody else experience it. We sought to predict empathic tendencies from the dissimilarity between neural activity patterns evoked by observing other people experience pain and touch and compared those findings to traditional univariate analyses. In support of a simulationist perspective, diverse observed somatosensory experiences were best classified by activation patterns in contralateral somatosensory and insular cortices, the same areas that would be active were the subject experiencing the stimuli themselves. In support of our specific hypothesis, the degree of dissimilarity between patterns for pain and touch in distinct areas was each associated with different aspects of trait empathy. Furthermore, the pattern dissimilarity analysis proved more informative regarding individual differences than analogous univariate analyses. These results suggest that multiple facets of empathy are associated with an ability to robustly distinguish between the simulated states of others at corresponding levels of the processing hierarchy, observable via the distinguishability of neural patterns arising with those states. Activation pattern dissimilarity may be a useful tool for parsing the neuroimaging correlates of complex cognitive functions like empathy.

Semantic transparency modulates the semantic perception of morphemes: Evidence from RSA of BOLD signals

There is no converging evidence on how a word’s semantic transparency affects morphemes’ potential semantic activation. The inconsistent results may be due to the limitation of traditional univariate analyses, in which the semantic transparency was treated as discrete categories. In the current study, Chinese two-character words were used as stimuli and functional magnetic resonance imaging (fMRI) techniques were combined with a priming paradigm. Unlike most previous studies, the multivariate representation similarity analysis (RSA) was used to treat semantic transparency as a continuous variable. The RSA results showed that widespread regions in the frontal-parietal-temporal network represent the semantic perception of characters in all words and transparent words, but no brain areas were identified in opaque words. Unlike RSA results, univariate analyses showed no significant difference between the opaque and transparent words. These results suggest that RSA is more suitable to examine the neural mechanism related to continuous variables such as semantic transparency.

Multi-tracer PET correlation analysis reveals disease-specific patterns in Parkinson’s disease and asymptomatic LRRK2 pathogenic variant carriers compared to healthy controls

Several genetic pathogenic variants increase the risk of Parkinson’s disease (PD) with pathogenic variants in the leucine-rich repeat kinase 2 (LRRK2) gene being among the most common. A joint pattern analysis based on multi-set canonical correlation analysis (MCCA) was utilized to extract PD and LRRK2 pathogenic variant-specific spatial patterns in relation to healthy controls (HCs) from multi-tracer Positron Emission Tomography (PET) data. Spatial patterns were extracted for individual subject cohorts, as well as for pooled subject cohorts, to explore whether complementary spatial patterns of dopaminergic denervation are different in the asymptomatic and symptomatic stages of PD. The MCCA results are also compared to the traditional univariate analysis, which serves as a reference.We identified PD-induced spatial distribution alterations common to DAT and VMAT2 in both asymptomatic LRRK2 pathogenic variant carriers and PD subjects. The inclusion of HCs in the analysis demonstrated that the dominant common PD-induced pattern is related to an overall dopaminergic terminal density denervation, followed by asymmetry and rostro-caudal gradient with deficits in the less affected side still being the best marker of disease progression.The analysis was able to capture a trend towards PD-related patterns in the LRRK2 pathogenic variant carrier cohort with increasing age in line with the known increased risk of this patient cohort to develop PD as they age. The advantage of this method thus resides in its ability to identify not only regional differences in tracer binding between groups, but also common disease-related alterations in the spatial distribution patterns of tracer binding, thus potentially capturing more complex aspects of disease induced alterations.

The UK BiLEVE and Mendelian randomisation: using multivariable instrumental variables to address “damned if you, damned if you don’t” adjustment problems

ObjectiveTo explore the use of multivariable instrumental variables to resolve the “damned if you do, damned if you don’t” adjustment problem created for Mendelian randomisation (MR) analysis using the smoking or lung function related phenotypes in the UK Biobank (UKB).Result“damned if you do, damned if you don’t” adjustment problems occur when both adjusting and not-adjusting for a variable will induce bias in an analysis. One instance of this occurs because the genotyping chip of UKB participants differed based on lung function/smoking status. In simulations, we show that multivariable instrumental variables analyses can attenuate potential collider bias introduced by adjusting for a proposed covariate, such as the UKB genotyping chip. We then explore the effect of adjusting for genotyping chip in a multivariable MR model exploring the effect of smoking on seven medical outcomes (lung cancer, emphysema, hypertension, stroke, heart diseases, depression, and disabilities). We additionally compare our results to a traditional univariate MR analysis using genome-wide analyses summary statistics which had and had not adjusted for genotyping chip. This analysis implies that the difference in genotyping chip has introduced only a small amount of bias.

Localized activity alternations in periventricular nodular heterotopia-related epilepsy.

Periventricular nodular heterotopia (PNH) is a common type of heterotopia usually characterized by epilepsy. Previous studies have identified alterations in structural and functional connectivity related to this disorder, but its local functional neural basis has received less attention. The purpose of this study was to combine univariate analysis and a Gaussian process classifier (GPC) to assess local activity and further explore neuropathological mechanisms in PNH-related epilepsy. We used a 3.0-T scanner to acquire resting-state data and measure local regional homogeneity (ReHo) alterations in 38 patients with PNH-related epilepsy and 38 healthy controls (HCs). We first assessed ReHo alterations by comparing the PNH group to the HC group using traditional univariate analysis. Next, we applied a GPC to explore whether ReHo could be used to differentiate PNH patients from healthy patients at an individual level. Compared to HCs, PNH-related epilepsy patients exhibited lower ReHo in the left insula extending to the putamen as well as in the subgenual anterior cingulate cortex (sgACC) extending to the orbitofrontal cortex (OFC) [p < 0.05, family-wise error corrected]. Both of these regions were also correlated with epilepsy duration. Furthermore, the ReHo GPC classification yielded a 76.32% accuracy (sensitivity=71.05% and specificity=81.58%) with p < 0.001 after permutation testing. Using the resting-state approach, we identified localized activity alterations in the left insula extending to the putamen and the sgACC extending to the OFC, providing pathophysiological evidence of PNH. These local connectivity patterns may provide a means to differentiate PNH patients from HCs.

Composite measures of motor performance and self-efficacy are better determinants of postural instability and gait difficulties than individual clinical measures in Parkinson's disease

BackgroundPostural instability and gait difficulties (PIGD) are a significant cause of disability and loss of quality of life (QoL) in Parkinson's Disease. Most research on clinical predictors of PIGD measures have focused on individual clinical often motor performance variables, However, PIGD motor features often result in fear of falling (FoF) lowering a patient's mobility self-efficacy. The purpose of this study was to assess composite measures of motor and self-efficacy determinants PIGD motor features in PD and compare these to analysis of individual clinical metrics. Methods75 PD participants underwent detailed motor and non-motor test batteries. Principal component analysis (PCA) was used to identify clusters of covarying correlates of slow walking, imbalance, falls, freezing of gait, FoG and compare these to traditional univariate analyses. ResultsA single PCA-derived composite measure of motor performance and self-efficacy of mobility was the most robust determinant of all PIGD motor features except for falls. In contrast, analysis of the individual clinical variables showed more limited and diverging findings, including evidence of better cognitive performance but more severe motor parkinsonian ratings in the fall group. ConclusionThere are robust associations between composite measures of motor performance and self-efficacy of mobility and all PIGD motor features except for falls. Univariate analysis of individual clinical measures showed limited correlates of PIGD motor features. Patient's own perception of motor performance, FoF, and QoL deserve more attention as PIGD therapeutic targets in PD.

Olfactory decoding is positively associated with ad libitum food intake in sated humans

The role of olfaction in eating behavior and body weight regulation is controversial. Here we reanalyzed data from a previous functional magnetic resonance imaging study to test whether central olfactory coding is associated with hunger/satiety state, food intake, and change in body weight over one year in healthy human adults. Since odor quality and category are coded across distributed neural patterns that are not discernible with traditional univariate analyses, we used multi-voxel pattern analyses to decode patterns of brain activation to food versus nonfood odors. We found that decoding accuracies in the piriform cortex and amygdala were greater in the sated compared to hungry state. Sated decoding accuracies in these and other regions were also associated with post-scan ad libitum food intake, but not with weight change. These findings demonstrate that the fidelity of olfactory decoding is influenced by meal consumption and is associated with immediate food intake, but not longer-term body weight regulation.

Cholesterol Levels and Monocyte Phenotype Are Predictors of Nonhealing in Individuals with Low-Grade Diabetic Foot Ulcers: A Prospective Cohort Study.

Objective: Inflammation has been linked to progression of diabetic foot ulcers (DFU); however, specific predictive markers of nonhealing are scarce. The goal of this study was to identify biochemical and immunological parameters from the blood as predictors of nonhealing in grade 1 and grade 2 DFU. Approach: Individuals with low-grade foot ulcers were enrolled in the study to determine if histopathological, biochemical, and immunological parameters could be used to predict individuals whose ulcers would not heal. Data analysis was performed using traditional univariate analyses as well as univariate and multivariable logistic regression, and STROBE guidelines were used for reporting data. Results: Among the 52 individuals who completed the study, we observe that no single histopathological and biochemical parameter was predictive. Conventional univariate analysis and univariate logistic regression analysis showed that the expression of the cell surface proteins CD63, HLA-DR, and CD11b on monocytes was significantly lower in nonhealed individuals, but with moderate discriminative ability. In comparison, a multivariable logistic regression model identified four of the 31 parameters to be salient predictors with low density lipoprotein (LDL) cholesterol (odds ratio [OR] 18.83, confidence interval [CI] 18.83-342) and cell-surface expression of CD63 on monocytes (OR 0.12, CI 0.12-0.45) showing significance and demonstrating high discrimination ability. Innovation: The approach of using a combination of biochemical and immunological parameters to predict ulcer healing is new. Conclusion: Through this study we conclude that LDL cholesterol and cell-surface expression of CD63 on monocytes strongly correlate with nonhealing in individuals with grade 1 and grade 2 DFU.

Assessment of inequity in bicyclist crashes using bivariate Bayesian copulas

Introduction: Physical activity associated with active transport modes such as bicycling has major health benefits and can help to reduce health concerns related to sedentary lifestyles, such as cardiovascular disease, Type II diabetes, and obesity, as well as risks of colon and breast cancer, high blood pressure, lipid disorders, osteoporosis, depression, and anxiety. However, as a vulnerable user group, bicyclists experience negative health impacts of transportation policies and infrastructure, such as traffic crashes and exposure to air and noise pollution that is disproportionately distributed within low-income and underserved areas. Method: This study used aggregated (block-group) bicyclist crash data from Harris County, Texas, to analyze how various equity measures are associated with both fatal and injury (FI) and no injury (property damage only) bicyclist crashes that occurred from 2010 to 2017. We used Bayesian bivariate copula-based random effects regression analysis to evaluate these associations. In contrast to more traditional univariate analysis, this novel methodology can consider the effects of factors of interest across different severity levels or crash types to fully understand their effects and how they may differ across categories. Results: The analysis results indicate that the bicyclist exposure, vehicle exposure, population demographics, population density, the percentage of African-Americans, and households below the poverty level are associated with both FI and PDO bicyclist crashes. Conclusions: Although more location and context-specific analyses are required, this study's overall results once again conform with the findings and assumptions in bicycling safety literature that the low-income and racially diverse communities are prone to experience more bicyclist crashes. Practical Applications: The findings of this study may have implications for future transportation and planning policies. These findings can be used to guide the policies and strategies targeting the elimination of inequity in transportation-related health concerns.

HyperPCA: A powerful tool to extract elemental maps from noisy data obtained in LIBS mapping of materials

Laser-induced breakdown spectroscopy (LIBS) is a preferred technique for fast and direct multi-elemental mapping of samples under ambient pressure, without any limitation on the targeted element. However, LIBS mapping data have two peculiarities: an intrinsically low signal-to-noise ratio due to single-shot measurements, and a high dimensionality due to the high number of spectra acquired for imaging. This is all the truer as lateral resolution gets higher: in this case, the ablation spot diameter is reduced, as well as the ablated mass and the emission signal, while the number of spectra for a given surface increases. Therefore, efficient extraction of physico-chemical information from a noisy and large dataset is a major issue. Multivariate approaches were introduced by several authors as a means to cope with such data, particularly Principal Component Analysis (PCA). PCA is useful to analyze correlations between different elements, but it is limited to low signal-to-noise ratios.In this paper, we introduce HyperPCA, a new analysis tool for hyperspectral images based on a sparse representation of the data using Discrete Wavelet Transform (DWT) and kernel-based sparse PCA (kSPCA) to reduce the impact of noise on the data and to consistently extract the spectroscopic signal, with a particular emphasis on LIBS data. The method is first illustrated using simulated LIBS mapping datasets to emphasize its performances with an extremely low shot-to-shot signal-to-noise ratio, and with a variable degree of spectral interference. Comparisons to standard PCA and to traditional univariate data analyses are provided. Finally, it is used to process real data in two cases that clearly illustrate the potential of the proposed algorithm. We show that the method presents advantages both in quantity and quality of the information recovered, thus improving the physico-chemical characterization of analysed surfaces.Recovering the principal components of noisy spectroscopic data is difficult Current PCA approaches show limited efficacy for LIBS mapping data Improvements are introduced through sparse signal representation and kernel-based PCA Noise is reduced by using a kernel function on the entries of the covariance matrix Wavelets mostly group lines of given elements in the same principal components.

The role of left angular gyrus in the representation of linguistic composition relations.

Language comprehension is compositional: individual words are combined structurally to form larger meaning representations. The neural basis for compositionality is at the center of a growing body of recent research. Previous work has largely used univariate analysis to investigate the question, a technique that could potentially lead to the loss of fined‐grained information due to the procedure of averaging over neural responses. In a functional magnetic resonance imaging experiment, the present study examined different types of composition relations in Chinese phrases, using a 1‐back composition relation probe (CRP) task and a 1‐back word probe (WP) task. We first analyzed the data using the multivariate representation similarity analysis, which better captures the fine‐grained representational differences in the stimuli. The results showed that the left angular gyrus (AG) represents different types of composition relations in the CRP task, but no brain areas were identified in the WP task. We also conducted a traditional univariate analysis and found greater activations in the bilateral inferior frontal gyrus in the CRP task relative to the WP task. We discuss the methodological and theoretical implications of our findings in the context of the larger language neural network identified in previous studies. Our findings highlight the role of left AG in representing and distinguishing fine‐grained linguistic composition relations.

Physical activity spectrum discriminant analysis-A method to compare detailed patterns between groups.

Investigating physical activity (PA) patterns as a detailed intensity spectrum instead of crude intensity categories have improved the ability to analyze the relationship between measured PA and health variables. The aim of this methodological study was to introduce and investigate the utility of using detailed PA intensity spectrum compared to crude PA intensity categories for comparison of PA between groups and between repeated measures. The study sample consisted of two groups of children, where one group was scheduled for extended physical education (PE) by daily classes while the other group followed usual PE schedule. Accelerometer data was processed into traditional crude PA intensity categories and into detailed PA intensity spectrum. Multivariate partial least squares regression for discriminant analysis (PLS-DA) was applied for PA intensity spectrum group comparison and compared to traditional univariate statistical analysis. Repeated measures were investigated using independent PLS-DA as well as multilevel PLS-DA for paired analysis. While traditional analysis of crude PA intensity categories was unable to find any group differences, multivariate analysis of the PA intensity spectrum identified statistically significant differences. By the extension of multilevel PLS-DA for paired comparison, a clear difference in the PA intensity spectrum was demonstrated between repeated measures. In conclusion, analysis of detailed PA intensity spectrum demonstrates utility for comparing detailed PA data between groups and between repeated measures in interventional and observational research.

NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.

In studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species. However, previous toolboxes have been made to fit specific datasets. Here, we develop NeuroRA, a novel and easy-to-use toolbox for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g., EEG, MEG, fNIRS, fMRI, and other sources of neruroelectrophysiological data), behavioral data, and computer-simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. Using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity among different task conditions and conduct a representational analysis among different RDMs to achieve a cross-modal comparison. Besides, users can calculate neural pattern similarity (NPS), spatiotemporal pattern similarity (STPS), and inter-subject correlation (ISC) with this toolbox. NeuroRA also provides users with functions performing statistical analysis, storage, and visualization of results. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.

Multivariate white matter alterations are associated with epilepsy duration.

Previous studies investigating associations between white matter alterations and duration of temporal lobe epilepsy (TLE) have shown differing results, and were typically limited to univariate analyses of tracts in isolation. In this study, we apply a multivariate measure (the Mahalanobis distance), which captures the distinct ways white matter may differ in individual patients, and relate this to epilepsy duration. Diffusion MRI, from a cohort of 94 subjects (28 healthy controls, 33 left‐TLE and 33 right‐TLE), was used to assess the association between tract fractional anisotropy (FA) and epilepsy duration. Using ten white matter tracts, we analysed associations using the traditional univariate analysis (z‐scores) and a complementary multivariate approach (Mahalanobis distance), incorporating multiple white matter tracts into a single unified analysis. For patients with right‐TLE, FA was not significantly associated with epilepsy duration for any tract studied in isolation. For patients with left‐TLE, the FA of two limbic tracts (ipsilateral fornix, contralateral cingulum gyrus) were significantly negatively associated with epilepsy duration (Bonferonni corrected p < .05). Using a multivariate approach we found significant ipsilateral positive associations with duration in both left, and right‐TLE cohorts (left‐TLE: Spearman's ρ = 0.487, right‐TLE: Spearman's ρ = 0.422). Extrapolating our multivariate results to duration equals zero (i.e., at onset) we found no significant difference between patients and controls. Associations using the multivariate approach were more robust than univariate methods. The multivariate Mahalanobis distance measure provides non‐overlapping and more robust results than traditional univariate analyses. Future studies should consider adopting both frameworks into their analysis in order to ascertain a more complete understanding of epilepsy progression, regardless of laterality.

New insights on the painting “Portrait of Mario Nuzzi”: a preliminary analytical study of Mario Nuzzi’s pictorial production and of his artistic collaborations

The study and characterization of an art work carried out by two or more artists is always an interesting challenge since each painter, although belonging to the same artistic current, can introduce specific pictorial materials or peculiar aesthetic effects, which constitute the artist fingerprint. This work presents a preliminary analytical study of the “Portrait of Mario Nuzzi”, painted by Giovanni Maria Morandi and Mario Nuzzi. In particular, non-invasive investigations were conducted using UV–VIS–NIR–SWIR multispectral imaging, radiography and X-ray spectroscopy (XRF). Multivariate analyses (PCA) were performed on VIS–NIR multispectral imaging in order to highlight some details not clearly visible using a traditional univariate analysis. Such an approach could be useful for authentication processes. In particular, in this paper we propose the use of the score density plot to determine the artist’s “chiaroscuro” fingerprint. Finally, the artist’s palette was compared with that of the “Primavera” painting, painted by Mario Nuzzi and Filippo Lauri, by using XRF analyses. We discovered that “Portrait of Mario Nuzzi” has not been significantly changed since the final version. Furthermore, the elemental composition of the pigments revealed that the palette used in the two paintings does not completely overlap. This aspect encourages us to perform a more detailed analysis of the pigments with the aim of better discriminating the parts painted by Mario Nuzzi from those of each collaborator.

Extinction learning alters the neural representation of conditioned fear.

Extinction learning is a primary means by which conditioned associations to threats are controlled and is a model system for emotion dysregulation in anxiety disorders. Recent work has called for new approaches to track extinction-related changes in conditioned stimulus (CS) representations. We applied a multivariate analysis to previously -collected functional magnetic resonance imaging data on extinction learning, in which healthy young adult participants (N = 43; 21 males, 22 females) encountered dynamic snake and spider CSs while passively navigating 3D virtual environments. We used representational similarity analysis to compare voxel-wise activation t-statistic maps for the shock-reinforced CS (CS+) from the late phase of fear acquisition to the early and late phases of extinction learning within subjects. These patterns became more dissimilar from early to late extinction in a priori regions of interest: subgenual and dorsal anterior cingulate gyrus, amygdala and hippocampus. A whole-brain searchlight analysis revealed similar findings in the insula, mid-cingulate cortex, ventrolateral prefrontal cortex, somatosensory cortex, cerebellum, and visual cortex. High state anxiety attenuated extinction-related changes to the CS+ patterning in the amygdala, which suggests an enduring threat representation. None of these effects generalized to an unreinforced control cue, nor were they evident in traditional univariate analyses. Our approach extends previous neuroimaging work by emphasizing how evoked neural patterns change from late acquisition through phases of extinction learning, including those in brain regions not traditionally implicated in animal models. Finally, the findings provide additional support for a role of the amygdala in anxiety-related persistence of conditioned fears.