A novel Skin lesion prediction and classification technique: ViT-GradCAM.

BACKGROUND: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS: In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT: The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION: This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

Lesion region inpainting: an approach for pseudo-healthy image synthesis in intracranial infection imaging.

The synthesis of pseudo-healthy images, involving the generation of healthy counterparts for pathological images, is crucial for data augmentation, clinical disease diagnosis, and understanding pathology-induced changes. Recently, Generative Adversarial Networks (GANs) have shown substantial promise in this domain. However, the heterogeneity of intracranial infection symptoms caused by various infections complicates the model's ability to accurately differentiate between pathological and healthy regions, leading to the loss of critical information in healthy areas and impairing the precise preservation of the subject's identity. Moreover, for images with extensive lesion areas, the pseudo-healthy images generated by these methods often lack distinct organ and tissue structures. To address these challenges, we propose a three-stage method (localization, inpainting, synthesis) that achieves nearly perfect preservation of the subject's identity through precise pseudo-healthy synthesis of the lesion region and its surroundings. The process begins with a Segmentor, which identifies the lesion areas and differentiates them from healthy regions. Subsequently, a Vague-Filler fills the lesion areas to construct a healthy outline, thereby preventing structural loss in cases of extensive lesions. Finally, leveraging this healthy outline, a Generative Adversarial Network integrated with a contextual residual attention module generates a more realistic and clearer image. Our method was validated through extensive experiments across different modalities within the BraTS2021 dataset, achieving a healthiness score of 0.957. The visual quality of the generated images markedly exceeded those produced by competing methods, with enhanced capabilities in repairing large lesion areas. Further testing on the COVID-19-20 dataset showed that our model could effectively partially reconstruct images of other organs.

Facial micro-expression recognition based on motion magnification network and graph attention mechanism.

Micro-expression is extensively studied due to their ability to fully reflect individuals' genuine emotions. However, accurate micro-expression recognition is a challenging task due to the subtle motion of facial muscle. Therefore, this paper introduces a Graph Attention Mechanism-based Motion Magnification Guided Micro-Expression Recognition Network (GAM-MM-MER) to amplify delicate muscle motions and focus on key facial landmarks. First, we propose a Swin Transformer-based network for micro-expression motion magnification (ST-MEMM) to enhance the subtle motions in micro-expression videos, thereby unveiling imperceptible facial muscle movements. Then, we propose a graph attention mechanism-based network for micro-expression recognition (GAM-MER), which optimizes facial key area maps and prioritizes adjacent nodes crucial for mitigating the influence of noisy neighbors, while attending to key feature information. Finally, experimental evaluations conducted on the CASME II and SAMM datasets demonstrate the high accuracy and effectiveness of the proposed network compared to state-of-the-art approaches. The results of our network exhibit significant superiority over existing methods. Furthermore, ablation studies provide compelling evidence of the robustness of our proposed network, substantiating its efficacy in micro-expression recognition.

PAFE: A lightweight visualization-based fast malware classification method.

With the development of automated malware toolkits, cybersecurity faces evolving threats. Although visualization-based malware analysis has proven to be an effective method, existing approaches struggle with challenging malware samples due to alterations in the texture features of binary images during the visualization preprocessing stage, resulting in poor performance. Furthermore, to enhance classification accuracy, existing methods sacrifice prediction time by designing deeper neural network architectures. This paper proposes PAFE, a lightweight and visualization-based rapid malware classification method. It addresses the issue of texture feature variations in preprocessing through pixel-filling techniques and applies data augmentation to overcome the challenges of class imbalance in small sample datasets. PAFE combines multi-scale feature fusion and a channel attention mechanism, enhancing feature expression through modular design. Extensive experimental results demonstrate that PAFE outperforms the current state-of-the-art methods in both efficiency and effectiveness for malware variant classification, achieving an accuracy rate of 99.25 % with a prediction time of 10.04 ms.

Application of the bicharacteristic attention residual pyramid for the treatment of brain tumors.

Currently, surgery remains the primary treatment for craniocerebral tumors. Before doctors perform surgeries, they need to determine the surgical plan according to the shape, location, and size of the tumor; however, various conditions of different patients make the tumor segmentation task challenging. To improve the accuracy of determining tumor shape and realizing edge segmentation, a U-shaped network combining a residual pyramid module and a dual feature attention module is proposed. The residual pyramid module can enlarge the receptive field, extract multiscale features, and fuse original information, which solves the problem caused by the feature pyramid pooling where the local information is not related to the remote information. In addition, the dual feature attention module is proposed to replace the skip connection in the original U-Net network, enrich the features, and improve the attention of the model to space and channel features with large amounts of information to be used for more accurate brain tumor segmentation. To evaluate the performance of the proposed model, experiments were conducted on the public datasets Kaggle_3M and BraTS2021. Because the model proposed in this study is applicable to two-dimensional image segmentation, it is necessary to obtain the crosscutting images of fair class in the BraTS2021 dataset in advance. Results show that the model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and false negative rate (FNR) on the Kaggle_3M dataset are 0.9395, 0.8812, 0.8958, and 0.007, respectively. The model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and FNR on the BraTS2021 dataset were 0.9375, 0.9072, 0.8981, and 0.0087, respectively. Compared with existing algorithms, all the indicators of the proposed algorithm have been improved, but the proposed model still has certain limitations and has not been applied to actual clinical trials. For specific datasets, the generalization ability of the model needs to be further improved. In the future work, the model will be further improved to address the aforementioned limitations.

Capturing subjective cognitive decline with a new combined index in low education patients with Parkinson's disease.

Objectives: Subjective Cognitive Decline (SCD) refers to self-reported cognitive decline with normal global cognition. This study aimed to capture SCD among low educated patients with Parkinson's disease (PD) using a newly established indicator. Methods: We recruited 64 PD patients with low education levels (education ≤12 years) for the study. The presence of SCD was determined based on a Unified Parkinson's Disease Rating Scale Part I (1.1) score ≥ 1. Spearman analysis and multivariate binary logistic regression analyses were conducted to investigate factors associated with the PD-SCD group. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the new combined index. Results: The prevalence of SCD in PD patients was 43.75%. Low educated PD-SCD patients had higher scores on the Non-Motor Symptoms Scale (NMSS), Parkinson's Fatigue Scale (PFS), Epworth Sleepiness Scale (ESS), as well as higher scores on the UPDRS-I and UPDRS-II, compared to PD patients without SCD. They also demonstrated poorer performance on the Montreal Cognitive Assessment (MoCA), particularly in the domains of executive abilities/attention/language. Multivariate binary regression confirmed the significant association between PD-SCD and MoCA-executive abilities/attention/language. Based on these findings, a combined index was established by summing the scores of MoCA-executive abilities, MoCA-attention, and MoCA-language. ROC analysis showed that the combined index could differentiate PD-SCD patients with an area under the curve (AUC) of 0.876. A score of 12 or less on the combined index had a sensitivity of 73.9% and a specificity of 76.2% for diagnosing PD-SCD. Conclusion: These low education patients with PD-SCD may exhibit potential PD-related pathological changes. It is important for clinicians to identify PD-SCD patients as early as possible. The newly combined index can help capture these low educated PD-SCD patients, with an AUC of 0.867, and is expected to assist clinicians in earlier identification and better management of PD patients.

Examination of Excessive Mind-Wandering Following Attention-Deficit and Hyperactivity Disorder Treatment in Adults.

Although mind-wandering (MW) is a part of attention deficit and hyperactivity disorder (ADHD), the impact of psychostimulants on excessive MW remains unclear. We aimed to elucidate how psychostimulants impact the MW of adult ADHD patients post treatment. This cross-sectional cohort study consisted of 54 randomly selected ADHD patients who applied to our psychiatry outpatient clinic and 40 healthy controls. The ADHD patients were administered methylphenidate or atomoxetine. A Semi-Structured Sociodemographic and Clinical Data Form, the Adult ADHD Self-Report Scale (ASRS), and the Mind Excessively Wandering Scale (MEWS) were applied. Routine psychiatric assessments in the 1st, 2nd, and 3rd months of pharmacological treatment were carried out by a psychiatrist. The pre-treatment MEWS score of the ADHD patients was 26.09 ± 1.92, which significantly decreased to 12.78 ± 2.54 post-treatment (F = 715.250, p < .001). A statistically significant difference was identified between the mean pre-treatment ASRS total score (44.07 ± 10.09) and post-treatment score (27.34 ± 11.22; F = 50.364, p < .001). A lifetime history of alcohol/substance use was positively associated with the MEWS score. ADHD pharmacotherapy led to significant reductions in MW. Recognizing the interaction between MW and ADHD could help in the design of more specific and comprehensive interventions.

Integrating Contrastive Learning and Cycle Generative Adversarial Networks for Non-invasive Fetal ECG Extraction.

Fetal electrocardiogram (FECG) contains crucial information about the fetus during pregnancy, making the extraction of FECG signal essential for monitoring fetal health. However, extracting FECG signal from abdominal electrocardiogram (AECG) poses several challenges: (1) FECG signal is often contaminated by noise, and (2) FECG signal is frequently overshadowed by high-amplitude maternal electrocardiogram (MECG). To address these issues and enhance the accuracy of signal extraction, this paper proposes an improved Cycle Generative Adversarial Networks (CycleGAN) with integrated contrastive learning for FECG signal extraction. The model introduces a dual-attention mechanism in the generator of the generative adversarial network, incorporating a multi-head self-attention (MSA) module and a channel-wise self-attention (CSA) module to enhance the quality of generated signals. Additionally, a contrastive triplet loss is integrated into the CycleGAN loss function, optimizing training to increase the similarity between the extracted FECG signal and the scalp fetal electrocardiogram. The proposed method is evaluated using the ADFECG dataset and the PCDB dataset both from the Physionet. In terms of signal extraction quality, Mean Squared Error is reduced to 0.036, Mean Absolute Error (MAE) to 0.009, and Pearson Correlation Coefficient reaches 0.924. When validating the model performance, Structural Similarity Index achieves 95.54%, Peak Signal-to-Noise Ratio (PSNR) reaches 38.87 dB, and R-squared (R2) attains 95.12%. Furthermore, the positive predictive value (PPV), sensitivity (SEN) and F1-score for QRS wave cluster detection on the ADFECG dataset also reached 99.56%, 99.43% and 99.50%, respectively. On the PCDB dataset, the positive predictive value (PPV), sensitivity (SEN) and F1-score for QRS wave cluster detection also reached 98.24%, 98.60% and 98.42%, respectively. All of them are higher than other methods. Therefore, the proposed model has important applications in effective monitoring of fetal health during pregnancy.

EXPRESS: Navigating the Mind's Eye: Understanding Gaze Shifts in Visuospatial Bootstrapping.

Visuo-spatial bootstrapping refers to the well-replicated phenomena in which serial recall in a purely verbal task is boosted by presenting digits within the familiar spatial layout of a typical telephone keypad. The visuo-spatial bootstrapping phenomena indicates that additional support comes from long-term knowledge of a fixed spatial pattern, and prior experimentation supports the idea that access to this benefit depends on the availability of the visuo-spatial motor system (e.g., Allen et al., 2015). We investigate this by tracking participants' eye movements during encoding and retention of verbal lists to learn whether gaze patterns support verbal memory differently when verbal information is presented in the familiar visual layout. Participants' gaze was recorded during attempts to recall lists of seven digits in three formats: centre of the screen, typical telephone keypad, or a spatially identical layout with randomized number placement. Performance was better with the typical than with the novel layout. Our data show that eye movements differ when encoding and retaining verbal information that has a familiar layout compared with the same verbal information presented in a novel layout, suggesting recruitment of different spatial rehearsal strategies. However, no clear link between gaze pattern and recall accuracy was observed, which suggests that gazes play a limited role in retention, at best.

Prediction of Human Microbe-Drug Association based on Layer Attention Graph Convolutional Network.

Human microbes are closely associated with a variety of complex diseases and have emerged as drug targets. Identification of microbe-related drugs is becoming a key issue in drug development and precision medicine. It can also provide guidance for solving the increasingly serious problem of drug resistance enhancement in viruses. METHODS: In this paper, we have proposed a novel model of layer attention graph convolutional network for microbe-drug association prediction. First, multiple biological data have been integrated into a heterogeneous network. Then, the heterogeneous network has been incorporated into a graph convolutional network to determine the embedded microbe and drug. Finally, the microbe-drug association scores have been obtained by decoding the embedding of microbe and drug based on the layer attention mechanism. RESULTS: To evaluate the performance of our proposed model, leave-one-out crossvalidation (LOOCV) and 5-fold cross-validation have been implemented on the two datasets of aBiofilm and MDAD. As a result, based on the aBiofilm dataset, our proposed model has attained areas under the curve (AUC) of 0.9178 and 0.9022 on global LOOCV and local LOOCV, respectively. Based on aBiofilm dataset, the proposed model has attained an AUC value of 0.9018 and 0.8902 on global LOOCV and local LOOCV, respectively. In addition, the average AUC and standard deviation of the proposed model for 5- fold cross-validation on the aBiofilm and MDAD datasets were 0.9141±6.8556e-04 and 0.8982±7.5868e-04, respectively. Also, two kinds of case studies have been further conducted to evaluate the proposed models. CONCLUSION: Traditional methods for microbe-drug association prediction are timeconsuming and laborious. Therefore, the computational model proposed was used to predict new microbe-drug associations. Several evaluation results have shown the proposed model to achieve satisfactory results and that it can play a role in drug development and precision medicine.

An Explainable Graph Neural Framework to Identify Cancer-Associated Intratumoral Microbial Communities.

Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a MIcrobial Cancer-association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer-associated microbial communities is presented. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph transformer to holistically capture relationships between intratumoral microbes and cancer tissues, which improves the explainability of the associations between identified microbial communities and cancers. MICAH is applied to intratumoral bacterial data across 5 cancer types and 5 fungi datasets, and its generalizability and reproducibility are demonstrated. After experimentally testing a representative observation using a mouse model of tumor-microbe-immune interactions, a result consistent with MICAH's identified relationship is observed. Source tracking analysis reveals that the primary known contributor to a cancer-associated microbial community is the organs affected by the type of cancer. Overall, this graph neural network framework refines the number of microbes that can be used for follow-up experimental validation from thousands to tens, thereby helping to accelerate the understanding of the relationship between tumors and intratumoral microbiomes.

Corrigendum: Neurocognitive and cerebellar function in ADHD, autism and spinocerebellar ataxia.

[This corrects the article DOI: 10.3389/fnsys.2023.1168666.].

Evaluations of aggressive chasing interactions by 7-month-old infants.

Recent theories of socio-moral development assume that humans evolved a capacity to evaluate others' social actions in different kinds of interactions. Prior infant studies found both reaching and visual preferences for the prosocial over the antisocial agents. However, whether the attribution of either positive or negative valence to agents' actions involved in an aggressive chasing interaction can be inferred by both reaching behaviors and visual attention deployment (i.e., disengagement of visual attention) is still an open question. Here we presented 7-month-old infants (N = 92) with events displaying an aggressive chasing interaction. By using preferential reaching and an attentional task (i.e., overlap paradigm), we assessed whether and how infants evaluate aggressive chasing interactions. The results demonstrated that young infants prefer to reach the victim over the aggressor, but neither agent affects visual attention. Moreover, such reaching preferences emerged only when dynamic cues and emotional face-like features were congruent with agents' social roles. Overall, these findings suggested that infants' evaluations of aggressive interactions are based on infants' sensitivity to some kinematic cues that characterized agents' actions and, especially, to the congruency between such motions and the face-like emotional expressions of the agents.

Sharing Our World: Impact of Group Motor Skill Learning on Joint Attention in Children with Autism Spectrum Disorder.

Impaired joint attention is a common feature of autism spectrum disorder (ASD), affecting social interaction and communication. We explored if group basketball learning could enhance joint attention in autistic children, and how this relates to brain changes, particularly white matter development integrity. Forty-nine autistic children, aged 4-12 years, were recruited from special education centers. The experimental group underwent a 12-week basketball motor skill learning, while the control group received standard care. Eye-tracking and brain scans were conducted. The 12-week basketball motor skill learning improved joint attention in the experimental group, evidenced by better eye tracking metrics and enhanced white matter integrity. Moreover, reduced time to first fixation correlated positively with decreased mean diffusivity of the left superior corona radiata and left superior fronto-occipital fasciculus in the experimental group. Basketball-based motor skill intervention effectively improved joint attention in autistic children. Improved white matter fiber integrity related to sensory perception, spatial and early attention function may underlie this effect. These findings highlight the potential of group motor skill learning within clinical rehabilitation for treating ASD.

The subcortical role in executive functions: Neural mechanisms of executive inhibition in the flanker task.

While executive functions (EFs) have traditionally been linked to the cerebral cortex, our understanding of EFs has evolved with increasing evidence pointing to the involvement of cortico-subcortical networks. Despite the importance of investigating EFs within this broader context, the functional contributions of subcortical regions to these processes remain largely unexplored. This study addresses this gap by specifically examining the involvement of subcortical regions in executive inhibition, as measured by the classic Eriksen flanker task. In this study, we used a stereoscope to differentiate between subcortical (monocular) and cortical (mostly binocular) visual pathways in EF processes. Our findings indicate that monocular visual pathways play a crucial role in representing executive conflict, which necessitates cortical involvement. The persistence of a monoptic advantage in conflict representation highlights the substantial contribution of subcortical regions to these executive processes. This exploration of subcortical involvement in executive inhibition provides valuable insights into the intricate relationships between cortical and subcortical regions in EFs.

Unraveling the mechanisms of high-level gait control in functional gait disorders.

Functional gait disorders (FGDs) are a disabling subset of Functional Neurological Disorders in which presenting symptoms arise from altered high-level motor control. The dual-task paradigm can be used to investigate mechanisms of high-level gait control. The study aimed to determine the objective measures of gait that best discriminate between individuals with FGDs and healthy controls and the relationship with disease severity and duration. High-level spatiotemporal gait outcomes were analyzed in 87 patients with FGDs (79.3% women, average age 41.9±14.7 years) and 48 healthy controls (60.4% women, average age 41.9±15.7 years) on single and motor, cognitive, and visual-fixation dual tasks. The area under the curve (AUC) from the receiver operator characteristic plot and the dual-task effect (DTE) were calculated for each measure. Dual-task interference on the top single-task gait characteristics was determined by two-way repeated measures ANOVA. Stride time variability and its standard deviation (SD) failed to discriminate between the two groups in single and dual-task conditions (AUC<0.80 for all). Significant group x task interactions were observed for swing time SD and stride time on the cognitive dual tasks (p<0.035 for all). Longer disease duration was associated with poor gait performance and unsteadiness in motor and cognitive DTE (p<0.003) but improvement in stride length and swing time on the visual dual tasks (p<0.041). Our preliminary findings shed light on measures of gait automaticity as a diagnostic and prognostic gait biomarker and underline the importance of early diagnosis and management in individuals with FGDs.

CCMNet: Cross-scale correlation-aware mapping network for 3D lung CT image registration.

The lung is characterized by high elasticity and complex structure, which implies that the lung is capable of undergoing complex deformation and the shape variable is substantial. Large deformation estimation poses significant challenges to lung image registration. The traditional U-Net architecture is difficult to cover complex deformation due to its limited receptive field. Moreover, the relationship between voxels weakens as the number of downsampling times increases, that is, the long-range dependence issue. In this paper, we propose a novel multilevel registration framework which enhances the correspondence between voxels to improve the ability of estimating large deformations. Our approach consists of a convolutional neural network (CNN) with a two-stream registration structure and a cross-scale mapping attention (CSMA) mechanism. The former extracts the robust features of image pairs within layers, while the latter establishes frequent connections between layers to maintain the correlation of image pairs. This method fully utilizes the context information of different scales to establish the mapping relationship between low-resolution and high-resolution feature maps. We have achieved remarkable results on DIRLAB (TRE 1.56 ± 1.60) and POPI (NCC 99.72% SSIM 91.42%) dataset, demonstrating that this strategy can effectively address the large deformation issues, mitigate long-range dependence, and ultimately achieve more robust lung CT image registration.

Prior-guided attention fusion transformer for multi-lesion segmentation of diabetic retinopathy.

To solve the issue of diagnosis accuracy of diabetic retinopathy (DR) and reduce the workload of ophthalmologists, in this paper we propose a prior-guided attention fusion Transformer for multi-lesion segmentation of DR. An attention fusion module is proposed to improve the key generator to integrate self-attention and cross-attention and reduce the introduction of noise. The self-attention focuses on lesions themselves, capturing the correlation of lesions at a global scale, while the cross-attention, using pre-trained vessel masks as prior knowledge, utilizes the correlation between lesions and vessels to reduce the ambiguity of lesion detection caused by complex fundus structures. A shift block is introduced to expand association areas between lesions and vessels further and to enhance the sensitivity of the model to small-scale structures. To dynamically adjust the model's perception of features at different scales, we propose the scale-adaptive attention to adaptively learn fusion weights of feature maps at different scales in the decoder, capturing features and details more effectively. The experimental results on two public datasets (DDR and IDRiD) demonstrate that our model outperforms other state-of-the-art models for multi-lesion segmentation.

Effectiveness of virtual reality glasses versus white noise on dental anxiety in children with attention-deficit hyperactivity disorder: A randomised controlled clinical trial.

BACKGROUND: Management of children with attention-deficit hyperactivity disorder (ADHD) can be challenging due to their disruptive behaviour. Basic behaviour management techniques (BMTs) may not be sufficient, and adjunctive strategies such as virtual reality (VR) glasses or white noise can be employed. AIM: To assess and compare the effectiveness of VR, white noise and basic BMTs on dental anxiety and behaviour of children with ADHD. DESIGN: Forty-eight children with ADHD were recruited for this parallel, three-armed randomised controlled clinical trial, which involved three visits at one-week intervals, including examination, preventive measures and restorations. Children were randomly divided into three groups: VR, white noise and basic BMTs. Outcome measures were Faces Image Scale (FIS), Heart Rate (HR) and Venham's Behaviour Rating Scale (VBRS). RESULTS: No significant difference was found between the groups in FIS scores. White noise group had a significantly lower mean HR than control group in all visits. Scores of VBRS in VR and white noise groups were significantly lower than those in the control group during the restorative visit. CONCLUSIONS: VR and white noise could be beneficial in managing dental anxiety and improving behaviour in children with ADHD and could be used as adjunctive strategies to basic BMTs.

Task-Based Attentional Control: The Role of Anxiety and Age.

OBJECTIVE: Attentional Control Theory (ACT) posits that anxiety impacts cognitive functioning through interference in working memory and processing efficiency, resulting in performance deficits in set-shifting and inhibition. Few studies have examined the effects of anxiety on set-shifting and inhibition in clinical samples or how these relationships might be affected by age. The current study tested whether increased age, elevated anxiety, and their interaction were associated with reduced performance on measures of set-shifting and inhibition. METHOD: Symptom and neuropsychological testing data were obtained from outpatient participants presenting at an academic medical center (N = 521; mean age = 50.39 years, SD = 22.35, range = 18-90; 47.4% female; 78.3% White). The Trail Making Test Difference score was used to assess set-shifting and the Stroop Color-Word Test Interference score was used to assess inhibition. RESULTS: After controlling for demographic variables, ADHD diagnosis, depression symptoms, and Mild Cognitive Impairment (MCI), both age and anxiety were significant predictors of set-shifting (ß = 0.45 and ß = 0.18, respectively, ps < 0.001) and inhibition (ß = -0.37, p < 0.001 and ß = -0.19, p = 0.001, respectively). No interaction was found between age and anxiety in the prediction of set-shifting or inhibition. CONCLUSION: Congruent with ACT, anxiety was associated with worse performance on measures of set-shifting and inhibition. Older age was an independent predictor of worse set-shifting and inhibition but did not moderate the relationship between anxiety and attentional control, suggesting that anxiety adversely affected working memory and processing efficiency equivalently across the adult lifespan. The results highlight the importance of anxiety assessment in neuropsychological evaluation in patients of all ages.