Comparative genomics of macaques and integrated insights into genetic variation and population history.

The crab-eating macaques ( Macaca fascicularis ) and rhesus macaques ( M. mulatta ) are widely studied nonhuman primates in biomedical and evolutionary research. Despite their significance, the current understanding of the complex genomic structure in macaques and the differences between species requires substantial improvement. Here, we present a complete genome assembly of a crab-eating macaque and 20 haplotype-resolved macaque assemblies to investigate the complex regions and major genomic differences between species. Segmental duplication in macaques is ∼42% lower, while centromeres are ∼3.7 times longer than those in humans. The characterization of ∼2 Mbp fixed genetic variants and ∼240 Mbp complex loci highlights potential associations with metabolic differences between the two macaque species (e.g., CYP2C76 and EHBP1L1 ). Additionally, hundreds of alternative splicing differences show post-transcriptional regulation divergence between these two species (e.g., PNPO ). We also characterize 91 large-scale genomic differences between macaques and humans at a single-base-pair resolution and highlight their impact on gene regulation in primate evolution (e.g., FOLH1 and PIEZO2 ). Finally, population genetics recapitulates macaque speciation and selective sweeps, highlighting potential genetic basis of reproduction and tail phenotype differences (e.g., STAB1 , SEMA3F , and HOXD13 ). In summary, the integrated analysis of genetic variation and population genetics in macaques greatly enhances our comprehension of lineage-specific phenotypes, adaptation, and primate evolution, thereby improving their biomedical applications in human diseases.

Brain-Derived Exosomal CircRNAs in Plasma Serve as Diagnostic Biomarkers for Acute Ischemic Stroke.

Acute ischemic stroke (AIS), commonly known as stroke, is a debilitating condition characterized by the interruption of blood flow to the brain, resulting in tissue damage and neurological deficits. Early diagnosis is crucial for effective intervention and management, as timely treatment can significantly improve patient outcomes. Therefore, novel methods for the early diagnosis of AIS are urgently needed. Several studies have shown that bioactive molecules contained in extracellular vesicles, especially circRNAs, could be ideal markers for the diagnosis of many diseases. However, studies on the effects of exosomes and their circRNAs on the development and prognosis of AIS have not been reported extensively. Therefore, we explored the feasibility of using circRNAs in plasma brain-derived exosomes as biomarkers for AIS. By high-throughput sequencing, we first identified 358 dysregulated circRNAs (including 23 significantly upregulated circRNAs and 335 significantly downregulated circRNAs) in the plasma brain-derived exosomes of the brain infarct patient group compared to those of the noninfarct control group. Five upregulated circRNAs (hsa_circ_0007290, hsa_circ_0049637, hsa_circ_0000607, hsa_circ_0004808, and hsa_circ_0000097) were selected for further validation via Real-Time Quantitative Reverse Transcription PCR (qRT‒PCR) in a larger cohort based on the exclusion criteria of log2FC > 1, p < 0.05 and measurable expression. We found that the expression levels of hsa_circ_0007290, hsa_circ_0049637, hsa_circ_0000607, hsa_circ_0004808 and hsa_circ_0000097 were significantly upregulated in AIS patients and could serve as potential biomarkers for AIS with high specificity and sensitivity. Moreover, the expression levels of hsa_circ_0007290, hsa_circ_0049637, hsa_circ_0000607, hsa_circ_0004808 and hsa_circ_0000097 were also found to be positively correlated with National Institutes of Health Stroke Scale (NISS) and modified Rankin scale (mRS) scores, which indicated that the presence of these circRNAs in plasma brain-derived exosomes could also determine the progression of AIS.

Mode coupling in FM-EDFAs caused by fiber bending: theory and experiments.

Refractive index perturbation caused by erbium-doped fiber (EDF) bending is inevitable in the fabrication of erbium-doped fiber amplifiers (EDFAs). The resulting mode coupling might bring about the deviation of theoretical results from experimental data. We present a theoretical model of FM-EDFAs with mode coupling due to fiber bending and carry out a proof-of-concept experiment by a 3.2-m-long EDF stretcher. Our experiments show that the fluctuation of modal gain due to fiber bending is about 1.5 dB for L P 01 and L P 11e modes, and about 2.5 dB for L P 11o mode, and the theoretical model is more useful for the FM-EDFA design in the presence of fiber bending.

FABNet: Frequency-Aware Binarized Network for Single Image Super-Resolution.

Remarkable achievements have been obtained with binary neural networks (BNN) in real-time and energy-efficient single-image super-resolution (SISR) methods. However, existing approaches often adopt the Sign function to quantize image features while ignoring the influence of image spatial frequency. We argue that we can minimize the quantization error by considering different spatial frequency components. To achieve this, we propose a frequency-aware binarized network (FABNet) for single image super-resolution. First, we leverage the wavelet transformation to decompose the features into low-frequency and high-frequency components and then employ a "divide-and-conquer" strategy to separately process them with well-designed binary network structures. Additionally, we introduce a dynamic binarization process that incorporates learned-threshold binarization during forward propagation and dynamic approximation during backward propagation, effectively addressing the diverse spatial frequency information. Compared to existing methods, our approach is effective in reducing quantization error and recovering image textures. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed methods could surpass state-of-the-art approaches in terms of PSNR and visual quality with significantly reduced computational costs. Our codes are available at https://github.com/xrjiang527/FABNet-PyTorch.

Prognostic Value of the Average Lung CT Number in Patients with Acute Paraquat Poisoning.

Objective: The chest computed tomography (CT) examination is an important clinical examination in the diagnosis and monitoring of paraquat- (PQ-) induced lung injury. The aim of this study was to explore the prognostic value of the average lung CT number acquired by quantitative CT techniques in patients with acute paraquat poisoning in the early stages of the disease. Methods: 46 patients who suffered from acute PQ poisoning in the emergency department of the Nanjing Drum Tower Hospital from January 2015 to June 2020 were enrolled in the present study. The patients were divided into survival group (n = 21) and nonsurvival group (n = 25). Clinical data were collected from subjects who met the inclusion criteria, including general information, personal disease history, and laboratory test indicators. The average lung CT numbers of each patient were obtained by quantitative CT techniques. Receiver operating characteristic (ROC) analysis was conducted to assess the prognostic value of average lung CT number in patients with acute paraquat poisoning. Results: The average CT numbers of the middle-lung, lower-lung, and whole lung fields in the nonsurvival group were significantly higher than those of the survival group (p < 0.0001). However, the upper-lung field was not significantly different between the two groups (p = 0.7765). The AUCs of different levels ranged from 0.554 to 0.977, among which the lower-lung field presented the largest AUC of 0.977 (95% CI: 0.943∼1; cut-off value: -702Hu; sensitivity 96%; specificity, 90.5%; YI: 0.865), followed by the whole lung field 0.914 (95% CI: 0.830∼0.999; cut-off value: -727Hu; sensitivity 76%; specificity, 95.2%; YI: 0.712) and the middle-lung field 0.87 (95% CI: 0.768∼0.971; cut-off value: -779Hu; sensitivity 80%; specificity, 85.7%; YI: 0.657). Conclusion: The present study indicated that the average lung CT number could be used to evaluate the relationship between the severity of PQ-induced lung injury and prognosis, especially in the lower-lung field. However, further research is needed to draw a clear conclusion.

A high-quality genome assembly highlights the evolutionary history of the great bustard (Otis tarda, Otidiformes).

Conservation genomics often relies on non-invasive methods to obtain DNA fragments which limit the power of multi-omic analyses for threatened species. Here, we report multi-omic analyses based on a well-preserved great bustard individual (Otis tarda, Otidiformes) that was found dead in the mountainous region in Gansu, China. We generate a near-complete genome assembly containing only 18 gaps scattering in 8 out of the 40 assembled chromosomes. We characterize the DNA methylation landscape which is correlated with GC content and gene expression. Our phylogenomic analysis suggests Otidiformes and Musophagiformes are sister groups that diverged from each other 46.3 million years ago. The genetic diversity of great bustard is found the lowest among the four available Otidiformes genomes, possibly due to population declines during past glacial periods. As one of the heaviest migratory birds, great bustard possesses several expanded gene families related to cardiac contraction, actin contraction, calcium ion signaling transduction, as well as positively selected genes enriched for metabolism. Finally, we identify an extremely young evolutionary stratum on the sex chromosome, a rare case among birds. Together, our study provides insights into the conservation genomics, adaption and chromosome evolution of the great bustard.

Optogenetics in taste research: A decade of enlightenment.

The electrophysiological function of the tongue involves complicated activities in taste sense, producing the perceptions of salty, sweet, bitter, and sour. However, therapies and prevention of taste loss arising from dysfunction in electrophysiological activity require further fundamental research. Optogenetics has revolutionized neuroscience and brought the study of sensory system to a higher level in taste. The year 2022 marks a decade of developments of optogenetics in taste since this technology was adopted from neuroscience and applied to the taste research. This review summarizes a decade of advances that define near-term translation with optogenetic tools, and newly-discovered mechanisms with the applications of these tools. The main limitations and opportunities for optogenetics in taste research are also discussed.

Construction and analysis of functional brain network based on emotional electroencephalogram.

Networks play an important role in studying structure or functional connection of various brain areas, and explaining mechanism of emotion. However, there is a lack of comprehensive analysis among different construction methods nowadays. Therefore, this paper studies the impact of different emotions on connection of functional brain networks (FBNs) based on electroencephalogram (EEG). Firstly, we defined electrode node as brain area of vicinity of electrode to construct 32-node small-scale FBN. Pearson correlation coefficient (PCC) was used to construct correlation-based FBNs. Phase locking value (PLV) and phase synchronization index (PSI) were utilized to construct synchrony-based FBNs. Next, global properties and effects of emotion of different networks were compared. The difference of synchrony-based FBN concentrates in alpha band, and the number of differences is less than that of correlation-based FBN. Node properties of different small-scale FBNs have significant differences, offering a new basis for feature extraction of recognition regions in emotional FBNs. Later, we made partition of electrode nodes and 10 new brain areas were defined as regional nodes to construct 10-node large-scale FBN. Results show the impact of emotion on network clusters on the right forehead, and high valence enhances information processing efficiency of FBN by promoting connections in brain areas.

Local Means Binary Networks for Image Super-Resolution.

The success of modern single image super-resolution (SISR) algorithms is inspired by the development of deep convolutional neural networks (CNNs). However, these CNN-based methods require considerable computation and complexity, making it impossible for these methods to perform real-time calculations in edge devices. Thus, lightweight model design has become a development trend in the super-resolution field, including pruning, quantization, and other methods. The 1-bit quantization is an extreme lightweight method which can reduce the calculation amount of the model in an extreme manner and is friendly to hardware such as edge devices. Most existing binary quantization approaches lead to a large information loss during forward propagation, especially in detailed color information (e.g., edge, texture, and contrast). The loss of color information makes modern binary methods unsuitable for SISR tasks. We think the loss occurs because these methods typically utilize a uniform threshold to quantize the weights and activations. Thus, in this article, we thoroughly analyze the difference between normal classification tasks and SISR tasks, and present a binarization scheme based on local means. The proposed method can maintain more detailed information in feature maps using dynamic thresholds during quantization. Specifically, each value in the full precision activations has a corresponding threshold during the quantization process, and those thresholds are determined by the full precision values of the surroundings. In addition, a gradient approximator is introduced to adaptively optimize the gradient for updating binary weights. We then verify the effectiveness of our method for training binary networks on several SISR benchmarks including VDSR and SRResNet. Experimental results show that the proposed method can outperform the state-of-the-art algorithms to obtain binary networks for image super-resolution with better peak signal-to-noise ratio (PSNR) values and visual quality.

Toward Pixel-Level Precision for Binary Super-Resolution With Mixed Binary Representation.

Binary neural network (BNN) is an effective method for reducing model computational and memory cost, which has achieved much progress in the super-resolution (SR) field. However, there is still a noticeable performance gap between a binary SR network and its full-precision counterpart. Considering that the information density in quantization features is far lower than full-precision features, we aim to improve the precision of quantization features to produce rich-enough output activations for SR task. First, we make several observations that a multibit value could be approximated by multiple 1-bit values, and the computation power of binary convolution could be improved by approximating the multibit convolution process. Then, we propose a mixed binary representation set to approximate multibit activations, which is effective in compensating the quantization precision loss. Finally, we present a new precision-driven binary convolution (PDBC) module, which increases the convolution precision and protects image detail information without extra computation. Compared with normal binary convolution, our method could largely reduce the information loss caused by binarization. In experiments, our methods consistently show superior performance over the baseline models and can surpass state-of-the-art methods in terms of peak signal to noise ratio (PSNR) and visual quality.

Efficacy and Safety of Non-Steroidal Mineralocorticoid Receptor Antagonists in Patients With Chronic Kidney Disease and Type 2 Diabetes: A Systematic Review Incorporating an Indirect Comparisons Meta-Analysis.

Background: The non-steroidal mineralocorticoid receptor antagonists (MRAs) are promising treatments in patients with chronic kidney disease (CKD) and type 2 diabetes (T2D). We conducted a meta-analysis to explore the efficacy and safety of the non-steroidal MRAs (finerenone, apararenone, esaxerenone) and detect the differences among them. Methods: We searched several databases for eligible randomized controlled trials (RCTs) investigating non-steroidal MRAs versus placebo in patients with CKD and T2D. We performed a conventional meta-analysis separately, and then indirect comparisons for efficacy and safety outcomes were conducted among these included drugs. Results: Eight RCTs with 14,450 subjects were enrolled. In patients with CKD and T2D, a greater reduction in urinary albumin-to-creatinine ratio (UACR) (WMD -0.40, 95% CI -0.48 to -0.32, p < 0.001), estimated glomerular filtration rate (eGFR) (WMD -2.69, 95% CI -4.47 to -0.91, p = 0.003), systolic blood pressure (SBP) (WMD -4.84, 95% CI -5.96 to -3.72, p < 0.001) and a higher risk of hyperkalemia (RR 2.07, 95% CI 1.86 to 2.30, p < 0.001) were observed in the non-steroidal MRAs versus placebo; there is no significant difference in the incidence of serious adverse events between two groups (RR 1.32, 95% CI 0.98 to 1.79, p = 0.067). Compared with finerenone, esaxerenone showed no significant difference in UACR reduction (WMD 0.24, 95% CI -0.016 to 0.496, p = 0.869); apararenone and esaxerenone showed greater decreases in SBP (WMD 1.37, 95% CI 0.456 to 2.284, p = 0.010; WMD 3.11, 95% CI 0.544 to 5,676, p = 0.021). Conclusions: Despite the moderate increased risk of hyperkalemia, use of non-steroidal MRAs could reduce proteinuria and SBP in patients with CKD and T2D. In terms of renoprotection, esaxerenone and finerenone may have similar effects. Esaxerenone and apararenone may have better antihypertensive effects than finerenone. The head-to-head RCTs are still needed to compare the differences of the efficacy and safety in these non-steroidal MRAs.

Representation of Cone-Opponent Color Space in Macaque Early Visual Cortices.

In primate vision, the encoding of color perception arises from three types of retinal cone cells (L, M, and S cones). The inputs from these cones are linearly integrated into two cone-opponent channels (cardinal axes) before the lateral geniculate nucleus. In subsequent visual cortical stages, color-preferring neurons cluster into functional domains within "blobs" in V1, "thin/color stripes" in V2, and "color bands" in V4. Here, we hypothesize that, with increasing cortical hierarchy, the functional organization of hue representation becomes more balanced and less dependent on cone opponency. To address this question, we used intrinsic signal optical imaging in macaque V1, V2, and V4 cortices to examine the domain-based representation of specific hues (here referred to as "hue domains") in cone-opponent color space (4 cardinal and 4 intermediate hues). Interestingly, we found that in V1, the relative size of S-cone hue preference domain was significantly smaller than that for other hues. This notable difference was less prominent in V2, and, in V4 was virtually absent, resulting in a more balanced representation of hues. In V2, hue clusters contained sequences of shifting preference, while in V4 the organization of hue clusters was more complex. Pattern classification analysis of these hue maps showed that accuracy of hue classification improved from V1 to V2 to V4. These results suggest that hue representation by domains in the early cortical hierarchy reflects a transformation away from cone-opponency and toward a full-coverage representation of hue.

Fear in the Theater of the Mind: Differential Fear Conditioning With Imagined Stimuli.

Many symptoms of anxiety and posttraumatic stress disorder are elicited by fearful mental imagery. Yet little is known about how visual imagery of conditioned stimuli (CSs) affects the acquisition of differential fear conditioning. Across three experiments with younger human adults (Experiment 1: n = 33, Experiment 2: n = 27, Experiment 3: n = 26), we observed that participants acquired differential fear conditioning to both viewed and imagined percepts serving as the CSs, as measured via self-reported fear and skin conductance responses. Additionally, this differential conditioning generalized across CS-percept modalities such that differential conditioning acquired in response to visual percepts generalized to the corresponding imagined percepts and vice versa. This is novel evidence that perceived and imagined stimuli engage learning processes in very similar ways and is consistent with the theory that mental imagery is depictive and recruits neural resources shared with visual perception. Our findings also provide new insight into the mechanisms of anxiety and related disorders.

Landscape of Metabolic Fingerprinting for Diagnosis and Risk Stratification of Sepsis.

Background: Sepsis and septic shock, a subset of sepsis with higher risk stratification, are hallmarked by high mortality rates and necessitated early and accurate biomarkers. Methods: Untargeted metabolomic analysis was performed to compare the metabolic features between the sepsis and control systemic inflammatory response syndrome (SIRS) groups in discovery cohort, and potential metabolic biomarkers were selected and quantified using multiple reaction monitoring based target metabolite detection method. Results: Differentially expressed metabolites including 46 metabolites in positive electrospray ionization (ESI) ion mode, 22 metabolites in negative ESI ion mode, and 4 metabolites with dual mode between sepsis and SIRS were identified and revealed. Metabolites 5-Oxoproline, L-Kynurenine and Leukotriene D4 were selected based on least absolute shrinkage and selection operator regularization logistic regression and differential expressed between sepsis and septic shock group in the training and test cohorts. Respective risk scores for sepsis and septic shock based on a 3-metabolite fingerprint classifier were established to distinguish sepsis from SIRS, septic shock from sepsis. Significant relationship between developed sepsis risk scores, septic shock risk scores and Sequential (sepsis-related) Organ Failure Assessment (SOFA), procalcitonin (PCT) and lactic acid were observed. Conclusions: Collectively, our findings demonstrated that the characteristics of plasma metabolites not only manifest phenotypic variation in sepsis onset and risk stratification of sepsis but also enable individualized treatment and improve current therapeutic strategies.

Mental imagery can generate and regulate acquired differential fear conditioned reactivity.

Mental imagery is an important tool in the cognitive control of emotion. The present study tests the prediction that visual imagery can generate and regulate differential fear conditioning via the activation and prioritization of stimulus representations in early visual cortices. We combined differential fear conditioning with manipulations of viewing and imagining basic visual stimuli in humans. We discovered that mental imagery of a fear-conditioned stimulus compared to imagery of a safe conditioned stimulus generated a significantly greater conditioned response as measured by self-reported fear, the skin conductance response, and right anterior insula activity (experiment 1). Moreover, mental imagery effectively down- and up-regulated the fear conditioned responses (experiment 2). Multivariate classification using the functional magnetic resonance imaging data from retinotopically defined early visual regions revealed significant decoding of the imagined stimuli in V2 and V3 (experiment 1) but significantly reduced decoding in these regions during imagery-based regulation (experiment 2). Together, the present findings indicate that mental imagery can generate and regulate a differential fear conditioned response via mechanisms of the depictive theory of imagery and the biased-competition theory of attention. These findings also highlight the potential importance of mental imagery in the manifestation and treatment of psychological illnesses.

Wavelet-Based Dual Recursive Network for Image Super-Resolution.

Although remarkable progress has been made on single-image super-resolution (SISR), deep learning methods cannot be easily applied to real-world applications due to the requirement of its heavy computation, especially for mobile devices. Focusing on the fewer parameters and faster inference SISR approach, we propose an efficient and time-saving wavelet transform-based network architecture, where the image super-resolution (SR) processing is carried out in the wavelet domain. Different from the existing methods that directly infer high-resolution (HR) image with the input low-resolution (LR) image, our approach first decomposes the LR image into a series of wavelet coefficients (WCs) and the network learns to predict the corresponding series of HR WCs and then reconstructs the HR image. Particularly, in order to further enhance the relationship between WCs and image deep characteristics, we propose two novel modules [wavelet feature mapping block (WFMB) and wavelet coefficients reconstruction block (WCRB)] and a dual recursive framework for joint learning strategy, thus forming a WCs prediction model to realize the efficient and accurate reconstruction of HR WCs. Experimental results show that the proposed method can outperform state-of-the-art methods with more than a 2× reduction in model parameters and computational complexity.

Dually Distribution Pulling Network for Cross-Resolution Person Reidentification.

Person reidentification (Re-ID) aims at recognizing the same identity across different camera views. However, the cross resolution of images [high resolution (HR) and low resolution (LR)] is unavoidable in a realistic scenario due to the various distances among cameras and pedestrians of interest, thus leading to cross-resolution person Re-ID problems. Recently, most cross-resolution person Re-ID methods focus on solving the resolution mismatch problem, while the distribution mismatch between HR and LR images is another factor that significantly impacts the person Re-ID performance. In this article, we propose a dually distribution pulling network (DDPN) to tackle the distribution mismatch problem. DDPN is composed of two modules, that is: 1) super-resolution module and 2) person Re-ID module. They attempt to pull the distribution of LR images closer to the distribution of HR images from image and feature aspects, respectively, through optimizing the maximum mean discrepancy losses. Extensive experiments have been conducted on three benchmark datasets and the results demonstrate the effectiveness of DDPN. Remarkably, DDPN shows a great advantage when compared to the state-of-the-art methods, for instance, we achieve rank-1 accuracy of 76.9% on VR-Market1501, which outperforms the best existing cross-resolution person Re-ID method by 10%.

A second-order accelerated neurodynamic approach for distributed convex optimization.

Based on the theories of inertial systems, a second-order accelerated neurodynamic approach is designed to solve a distributed convex optimization with inequality and set constraints. Most of the existing approaches for distributed convex optimization problems are usually first-order ones, and it is usually hard to analyze the convergence rate for the state solution of those first-order approaches. Due to the control design for the acceleration, the second-order neurodynamic approaches can often achieve faster convergence rate. Moreover, the existing second-order approaches are mostly designed to solve unconstrained distributed convex optimization problems, and are not suitable for solving constrained distributed convex optimization problems. It is acquired that the state solution of the designed neurodynamic approach in this paper converges to the optimal solution of the considered distributed convex optimization problem. An error function which demonstrates the performance of the designed neurodynamic approach, has a superquadratic convergence. Several numerical examples are provided to show the effectiveness of the presented second-order accelerated neurodynamic approach.

Learning lightweight super-resolution networks with weight pruning.

Single image super-resolution (SISR) has achieved significant performance improvements due to the deep convolutional neural networks (CNN). However, the deep learning-based method is computationally intensive and memory demanding, which limit its practical deployment, especially for mobile devices. Focusing on this issue, in this paper, we present a novel approach to compress SR networks by weight pruning. To achieve this goal, firstly, we explore a progressive optimization method to gradually zero out the redundant parameters. Then, we construct a sparse-aware attention module by exploring a pruning-based well-suited attention strategy. Finally, we propose an information multi-slicing network which extracts and integrates multi-scale features at a granular level to acquire a more lightweight and accurate SR network. Extensive experiments reflect the pruning method could reduce the model size without a noticeable drop in performance, making it possible to apply the start-of-the-art SR models in the real-world applications. Furthermore, our proposed pruning versions could achieve better accuracy and visual improvements than state-of-the-art methods.

Psychophysiological evidence for fear extinction learning via mental imagery.

Imagery-based extinction procedures have long been used in the treatments of fear-related conditions. The assumption is that imagery can substitute for the perceptual stimuli in the extinction process. Yet, experimental validations of this assumption have been limited in number and some have relied exclusively on measures of autonomic reactivity without consideration of conscious feelings of fear. The current investigation sought to assess whether imagery-based exposure could lead to extinction of conditioned fear to the corresponding perceptual stimulus. Conditioned fear responses were measured by both a physiological (i.e., skin conductance response [SCR]) and a subjective (i.e., self-reported fear) measure. Participants (N = 56) first underwent perceptual differential fear conditioning, then imagery extinction, then perceptual extinction. SCR evidence was found for successful fear conditioning, generalization of fear from viewing to imagery, and most importantly, the absence of differential fear after imagery extinction upon re-exposure to the conditioned perceptual stimulus. Self-reported fear confirmed the acquisition and generalization of fear and provided evidence of a significant reduction in differential fear conditioning across extinction. Consistent with clinical evidence of the efficacy of imagery extinction and the existing limited experimental literature, this study offers support for fear extinction to perceptual stimuli via imagery exposure.