Sight Research Articles

Pilot Study on Reading and Writing Characteristics in Children with Attention-Deficit Hyperactivity Disorder.

This study surveys the factors regarding reading and writing difficulties in children with attentiondeficit hyperactivity disorder (ADHD) and uses these results to provide support. We conducted surveys on 16 elementary school children from 3rd to 6th grades who were diagnosed with ADHD by a physician based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). We conducted a Hiragana reading test, a Kanji reading and writing test, and a visual perception test on the subject children. We requested that the parents of the children complete a reading and writing symptom checklist, the Developmental Coordination Disorder Questionnaire (DCDQ), the Strength and Difficulties Questionnaire (SDQ), the ADHD-Rating Scale (ADHD-RS), and the Autism-Spectrum Quotient (AQ). We conducted a multiple regression analysis using the Kanji reading and writing test as the dependent variable. Results revealed that the positional task score in the visual perception test was a factor that influences Kanji writing. Meanwhile, gender, reading-related items on the reading and writing symptom checklist, and performance such as misreading of hiragana were factors that influenced Kanji reading. Based on the results, we suggested that when teaching Kanji writing, children's visual cognition ability should be assessed, and ways to incorporate learning methods that supplement visual perception, such as color masses and electronic learning materials, should be devised. When teaching reading, we suggest using teaching materials that present not only text but also audio as needed, and support the awareness of parents.

Unsupervised Illumination Adaptation for Low-Light Vision.

Insufficient lighting poses challenges to both human and machine visual analytics. While existing low-light enhancement methods prioritize human visual perception, they often neglect machine vision and high-level semantics. In this paper, we make pioneering efforts to build an illumination enhancement model for high-level vision. Drawing inspiration from camera response functions, our model could enhance images from the machine vision perspective despite being lightweight in architecture and simple in formulation. We also introduce two approaches that leverage knowledge from base enhancement curves and self-supervised pretext tasks to train for different downstream normal-to-low-light adaptation scenarios. Our proposed framework overcomes the limitations of existing algorithms without requiring access to labeled data in low-light conditions. It facilitates more effective illumination restoration and feature alignment, significantly improving the performance of downstream tasks in a plug-and-play manner. This research advances the field of low-light machine analytics and broadly applies to various high-level vision tasks, including classification, face detection, optical flow estimation, and video action recognition.

Stepping Up Psychosis: The Use of Virtual Reality in Pre-registration Mental Health Nursing Education

BackgroundThere has been growing interest in the development of mental health-related simulation packages for pre-registration mental health nursing education. The authors will present the creation of a simulation package created for pre-registration mental health nurses during their final year. MethodThe simulated experience consisted of a five-minute Virtual Reality (VR) recording which shares the experience of living with symptoms of psychoses. The package, not only looked at the hearing of voices, but in addition enhances the user's experience by simulating visual perception and placing the student within a secluded environment. This was delivered to students in their final six months of the program. ResultsStudents noted the increase in empathy for patients experiencing these symptoms and how it would enhance the care they gave. ConclusionThe results of this innovation demonstrate how virtual reality (VR) could be used to standardize student nurses' education in the field of mental health.

Visual-Motor Integration (VMI) Is Also Relevant for Computer, Smartphone, and Tablet Use by Adults: Introducing the Brief Box Clicking Test.

Visual-motor integration (VMI) is typically examined in children to promote handwriting, but it may also be relevant for adults' capacity for technology use. To examine the reliability and validity of speed of completion of the box clicking test, a web-based test of VMI. Participants in the Understanding America Study completed online surveys on a regular basis, including a very brief (less than 30 s) self-administered box clicking test. For validity testing, we examined whether box clicking speed was associated with constructs relevant to visual-perceptual skills and motor coordination, the skills underlying VMI. Test-retest reliability was examined by computation of intraclass correlation coefficients. A total of 11,114 adults. Measures included the completion time for the box clicking task and measures relevant to visual perception (e.g., perceptual speed) and motor coordination (e.g., self-reported functional limitation). Results suggested that the box clicking test was a VMI task. Slower test performance was associated with lower visual-perceptual speed and a greater likelihood of reporting difficulties with dressing, a motor coordination relevant task. Box clicking tests taken within at least 2 yr of one another had moderate test-retest stability, but future studies are needed to examine test-retest reliabilities over brief (e.g., 2-wk) time intervals. The box clicking test may serve both as a tool for research and to clinically observe whether clients have VMI difficulties that interfere with computer, smartphone, or tablet use. Plain-Language Summary: Use of devices such as smartphones and computers is increasingly becoming integral for daily functioning. Visual-motor integration (VMI) has often been addressed by occupational therapists to support handwriting of children, but it may also be important for technology use by adults. Prior literature supports the relevance of VMI to technology use, and adults with various chronic conditions have been found to have decrements in VMI. We tested the psychometric properties of a brief box clicking test of VMI that could be used to examine VMI underlying technology use among adults. Overall, results suggested that the box clicking test was a VMI task. Just as speed of gait has been used as an index of functional mobility, speed on the box clicking task seemed serviceable as an index of VMI ability. The box clicking test may also be used for clinical observation of whether VMI interferes with technology use.

The effect of mindfulness intervention on high school students’ eating habits and body image perceptions

The effect of mindfulness intervention on high school students’ eating habits and body image perceptions

Research on Leisure Tourism Destination Image Innovation Driven by Artificial Intelligence

As the public’s demand for tourism experience continues to improve, the demand for tourism products gradually develops in the direction of personalization, customization and differentiation. In order to mine the image of leisure tourism destinations, this paper conducts theme mining on the review texts of leisure tourism destinations by means of LDA model. An improved neural network language model CBOW and a Glove model are used to train the review word vectors of leisure tourism destination texts. Based on the deep fully connected neural network model, the perceptual features of tourist destinations are studied and analyzed. Taking Sanya tourist attractions as a research case, by analyzing the perception of tourist attractions, the cognitive image vocabulary of Sanya tourist attractions includes three major categories. Among them, tourism attractions, tourism infrastructure and services, tourism service atmosphere, and tourists’ travel behavior accounted for 53.1%, 22.3%, 5.1%, and 19.5% respectively. Then the emotional vocabulary in the perception of tourism destination image in Sanya City was categorized in terms of positive, neutral and negative, and from the neutral vocabulary, the word frequency number of words such as waiting (101), understanding (185), fun (61), and relaxation (56) was high. Regression analysis was used to explore the factors influencing the effect of tourists’ perception of destination image. Among them, tangibility (0.43), reliability (0.463), and responsiveness (0.434) were positively and significantly related to tourist satisfaction. Improving the image of leisure tourism destination can focus on these three aspects.

Body image perception and social support are important predictors of quality of life in bladder cancer patients after cystectomy with urinary diversion.

Systematic evaluations focusing on the perception of body image and social support in relation to quality of life (QoL) outcomes in patients RC with UD are currently lacking. This study investigated the relationship between body image perception, social support, and QoL in bladder cancer patients who underwent radical cystectomy (RC) with urinary diversion (UD). A cross-sectional survey was conducted using validated general oncology tools to assess QoL in relation to newly implemented tools assessing body image perception and social support. Body image perception was assessed with the Self-Image Scale, and social support was assessed using the Illness-Specific Social Support Scale. Logistic regression models were used to analyze factors associated with QoL and body image respectively. The survey revealed a significant association of body image perception with QoL, as well as social support with body image perception. This is the first study to systematically evaluate these psychosocial factors in the context of QoL for RC patients, highlighting their critical role in patient-reported outcomes. Body image perception and social support are important psychosociological factors that affect QoL of bladder cancer patients post-RC. Targeted psychosocial interventions could be promising for improving QoL patients post-RC.

Bridging concepts of drishti: Shalakya tantra and modern science

Introduction: Shalakya Tantra, a key aspect of traditional Indian medicine, focuses on the intricacies of human vision or 'drishti' in Ayurvedic literature. This ancient discipline offers a comprehensive exploration of 'Urdhvajatrugata' parts, encompassing the eyes, ears, nose, throat, and head, which modern research has yet to match in depth. The study of 'drishti' and its related concepts, such as 'mandala,' 'patala,' and 'sandhi,' reveals a profound understanding that warrants further exploration, particularly in contrast with contemporary findings. The Sushruta Samhita details 'Drishti' extensively, describing its anatomical and functional aspects, including its composition of 'Dravyas' (substances) and 'Mahabhutas' (elements), with a notable presence of 'Teja' or fire as 'Alochaka Pitta.' Aim and Objective: · To describe 'Drishti' from both Ayurvedic and modern perspectives. · To provide a detailed analysis of the various aspects of 'Drishti.' Materials and Methods: Materials include Ayurvedic scriptures (Sushruta Samhita, Charak Samhita, Bhela Samhita), modern textbooks, medical journals, and published research papers. The study is a review based on literature from authentic Ayurvedic sources and modern texts. Results and Discussion: The concept of 'Drishti' is analysed from anatomical and physiological viewpoints. Ayurvedic texts describe the eye as a layered entity with six junctions and thin membranes crucial for its function. Different interpretations of 'Drishti' include the retina, vision, pupil, and crystalline lens. Modern science aligns with these descriptions through detailed processes involving light refraction, image focusing, and neural processing. Conclusion: The study highlights the synergy between Ayurvedic and modern scientific perspectives on 'Drishti,' emphasizing the relevance of traditional wisdom in contemporary healthcare. Integrating these approaches offers a richer understanding of ocular health and underscores the potential for future research to enhance medical treatments and promote holistic well-being.

Unsupervised low-light image enhancement by data augmentation and contrastive learning

ABSTRACT Today, with the increasing demand for visual perception and high-level computational vision tasks, the field of low-light enhancement is rapidly developing. However, models trained on existing datasets often fail or suffer significant performance degradation in real-world low-light scenarios. This performance degradation is frequently due to the limitations of current databases, which typically contain small quantities of paired images of a single type. This article proposes an unsupervised model with a unique data augmentation technique that transforms a regular image database into a paired image database. By adjusting image parameters during training to change exposure, a regular image database can be converted into a paired one. The model restores low-exposure images by extracting lighting features through comparative learning. Evaluations of the LOL and DIV2K datasets demonstrate the proposed model's effectiveness, achieving notable results in low-light image enhancement. This method removes dataset restrictions, broadening the model's range of applications.

ConIQA: A deep learning method for perceptual image quality assessment with limited data.

Effectively assessing the realism and naturalness of images in virtual (VR) and augmented (AR) reality applications requires Full Reference Image Quality Assessment (FR-IQA) metrics that closely align with human perception. Deep learning-based IQAs that are trained on human-labeled data have recently shown promise in generic computer vision tasks. However, their performance decreases in applications where perfect matches between the reference and the distorted images should not be expected, or whenever distortion patterns are restricted to specific domains. Tackling this issue necessitates training a task-specific neural network, yet generating human-labeled FR-IQAs is costly, and deep learning typically demands substantial labeled data. To address these challenges, we developed ConIQA, a deep learning-based IQA that leverages consistency training and a novel data augmentation method to learn from both labeled and unlabeled data. This makes ConIQA well-suited for contexts with scarce labeled data. To validate ConIQA, we considered the example application of Computer-Generated Holography (CGH) where specific artifacts such as ringing, speckle, and quantization errors routinely occur, yet are not explicitly accounted for by existing IQAs. We developed a new dataset, HQA1k, that comprises 1000 natural images each paired with an image rendered using various popular CGH algorithms, and quality-rated by thirteen human participants. Our results show that ConIQA achieves superior Pearson (0.98), Spearman (0.965), and Kendall's tau (0.86) correlations over fifteen FR-IQA metrics by up to 5%, showcasing significant improvements in aligning with human perception on the HQA1k dataset.

An Elderly-Oriented Form Design of Low-Speed New Energy Vehicles Based on Rough Set Theory and Support Vector Regression

With the current trend of social aging, the travel needs of the elderly are increasingly prominent. As a means of urban transportation, low-speed new energy vehicles (NEVs) are widely used among the elderly. Many studies are devoted to exploring the function of cars and the travel modes that meet the needs of older people. However, in addition to product performance, the Kansei needs of users also play a key role in communication between enterprises and users. Therefore, the problem of how to improve car shapes in the initial stage of design to meet the Kansei needs of elderly users remains to be solved. In order to fill this gap, the design of low-speed NEVs are selected as the study objects so as to explore the relationship between the visual perception of elderly users and car design; thus, a design method for the form of elderly-oriented cars is proposed. Firstly, using the research framework of Kansei engineering, factor analysis is used to cluster elderly-oriented Kansei factors. Second, the cars’ appearances are deconstructed by morphological analysis, and the key design features affecting elderly-oriented satisfaction are identified by a rough set attribute reduction algorithm. Finally, support vector regression is used to establish a mapping model of elderly-oriented Kansei factors and the key design features to predict the elderly-oriented form design of optimal low-speed NEVs. The research results show that selecting “Hub6”, “Headlight9”, “Car side view2”, “Rearview mirror9”, and “Front door10” in the form deconstruction table for low-speed NEVs can elicit optimal emotions in elderly users. The research results enable enterprises to more effectively understand the emotional cognition of elderly users related to the form of low-speed NEVs and improve the purchase desire and satisfaction of elderly users, providing references and guidance for the elderly-oriented design and development of intelligent transportation tools.

Deciphering Motorists’ Perceptions of Scenic Road Visual Landscapes: Integrating Binocular Simulation and Image Segmentation

Traditional scenic road visual landscape assessment methods struggle to quantify drivers’ subjective visual perceptions. This study aims to develop a new method to decipher Scenic Road Visual Landscape Evaluation (SRVLE) of motorists’ visual field, reconciling the longstanding subjectivity–objectivity dichotomy in landscape quality research. By adopting binocular visual simulation and image segmentation, this paper conceptualizes a novel “non-scale semantic differential approach” to quantify landscape qualities across the dimensions of naturalness–artificiality (NA), diversity–coherence (DC), and openness–deepness (OD), constructing a three-dimensional visual landscape quality evaluation system. Taking the Nujiang Beautiful Road in Yunnan as a case study, the results show the following: (1) The three indicators reveal the scenic road’s distinctive visual landscape characteristics, marked by high naturalness, coherence, and relative openness. (2) SRVLE is found to vary between the two driving directions and different sections. (3) The three-dimensional evaluation cube intuitively displays the comprehensive characteristics of landscape quality, providing a basis for scenic road planning. This method offers a new approach to resolving the subjective–objective divide in SRVLE and can assist road administrations in enhancing policy planning, construction, and management, thereby promoting the high-quality development of scenic roads.

From “Made In” to Mukokuseki: Exploring the Visual Perception of National Identity in Robots

People read human characteristics into the design of social robots, a visual process with socio-cultural implications. One factor may be nationality, a complex social characteristic that is linked to ethnicity, culture, and other factors of identity that can be embedded in the visual design of robots. Guided by social identity theory (SIT), we explored the notion of “mukokuseki,” a visual design characteristic defined by the absence of visual cues to national and ethnic identity in Japanese cultural exports. In a two-phase categorization study (n=212), American (n=110) and Japanese (n=92) participants rated a random selection of nine robot stimuli from America and Japan, plus multinational Pepper. We found evidence of made-in and two kinds of mukokuseki effects. We offer suggestions for the visual design of mukokuseki robots that may interact with people from diverse backgrounds. Our findings have implications for robots and social identity, the viability of robotic exports, and the use of robots internationally.

Presaccadic attention enhances and reshapes the Contrast Sensitivity Function differentially around the visual field.

Contrast sensitivity, which constrains human vision, decreases from fovea to periphery, from the horizontal to the vertical meridian, and from the lower vertical to the upper vertical meridian. The Contrast Sensitivity Function (CSF) depicts how contrast sensitivity depends on spatial frequency (SF). To compensate for these visual constraints, we constantly make saccadic eye movements to foveate on relevant objects in the scene. Already before saccade onset, presaccadic attention shifts to the saccade target and enhances perception. However, it is unknown whether and how it modulates the interplay between contrast sensitivity and SF, and if this effect varies around polar angle locations. Contrast sensitivity enhancement may result from a horizontal or vertical shift of the CSF, increase in bandwidth, or any combination. In addition, presaccadic attention could enhance contrast sensitivity similarly around the visual field, or it could benefit perception more at locations with poorer performance (i.e., vertical meridian). Here, we investigated these possibilities by extracting key attributes of the CSF of human observers. The results reveal that presaccadic attention (1) increases contrast sensitivity across SF, (2) increases the most preferred and the highest discernable SF, and (3) narrows the bandwidth. Therefore, presaccadic attention helps bridge the gap between presaccadic and post-saccadic input by increasing visibility at the saccade target. Counterintuitively, the presaccadic enhancement in contrast sensitivity was more pronounced where perception is better -along the horizontal than the vertical meridian- exacerbating polar angle asymmetries. Our results call for an investigation of the differential neural modulations underlying presaccadic perceptual changes for different saccade directions.Significance statement The contrast sensitivity function (CSF) describes how our ability to perceive contrast depends on spatial frequency. Contrast sensitivity is highest at the fovea and decreases in the periphery, especially at locations along the vertical meridian. We thus make saccadic eye movements to view objects in detail. Already before moving our eyes, presaccadic attention enhances perception at the target location. But how does it influence the interplay between contrast sensitivity and spatial frequency, and does its effect vary around the visual field? Using Hierarchical Bayesian Modeling, we show that presaccadic attention enhances and reshapes the CSF to prepare the periphery for upcoming fixation. Interestingly, it does so more at horizontal locations where vision is stronger, suggesting smoother perception across horizontal than vertical eye movements.

A circuit motif for color in the human foveal retina

The neural pathways that start human color vision begin in the complex synaptic network of the foveal retina where signals originating in long (L), middle (M), and short (S) wavelength-sensitive cone photoreceptor types are compared through antagonistic interactions, referred to as opponency. In nonhuman primates, two cone opponent pathways are well established: an L vs. M cone circuit linked to the midget ganglion cell type, often called the red-green pathway, and an S vs. L + M cone circuit linked to the small bistratified ganglion cell type, often called the blue-yellow pathway. These pathways have been taken to correspond in human vision to cardinal directions in a trichromatic color space, providing the parallel inputs to higher-level color processing. Yet linking cone opponency in the nonhuman primate retina to color mechanisms in human vision has proven particularly difficult. Here, we apply connectomic reconstruction to the human foveal retina to trace parallel excitatory synaptic outputs from the S-ON (or "blue-cone") bipolar cell to the small bistratified cell and two additional ganglion cell types: a large bistratified ganglion cell and a subpopulation of ON-midget ganglion cells, whose synaptic connections suggest a significant and unique role in color vision. These two ganglion cell types are postsynaptic to both S-ON and L vs. M opponent midget bipolar cells and thus define excitatory pathways in the foveal retina that merge the cardinal red-green and blue-yellow circuits, with the potential for trichromatic cone opponency at the first stage of human vision.

WaveFusionNet: Infrared and visible image fusion based on multi-scale feature encoder–decoder and discrete wavelet decomposition

To merge complementary information from multimodal images, such as thermal saliency from infrared images and texture details from visible images, traditional multi-scale transform-based methods have been extensively studied, with deep learning-based methods gaining significant popularity in recent years. However, there has been limited research on optimally combining the advantages of these two categories in fusion. In this paper, we propose a novel infrared and visible image fusion (IVIF) framework, WaveFusionNet, which integrates precise frequency feature decomposition from the discrete wavelet transform (DWT) with the comprehensive feature extraction from the multi-scale encoder. Firstly, we train an encoder–decoder network for multi-scale feature extraction and image reconstruction. DWT is used for down-sampling with minimal information loss by decomposing extracted features into low and high-frequency sub-bands. Next, a dual-band feature fusion (DBFF) module is trained to merge these sub-bands by integrating a spatial feature transform-based sub-network for low-frequency fusion and a maximum absolute value selection strategy for fusing high-frequencies. Finally, all fused sub-bands are fed into the pre-trained decoder to reconstruct the final image. Experimental results on three benchmark datasets (TNO, Roadscene, and MSRS) demonstrate that the proposed fusion method outperforms recent IVIF methods in both quantitative assessment and visual perception while maintaining competitive time complexity.

Brighteness-dependent visual attractiveness of the human body for horse flies (Diptera: Tabanidae): a field experiment.

The landing patterns of blood-sucking females (Diptera: Tabanidae) are largely influenced by their visual perceptions. When attacking humans, the shape of different body parts and overall brightness of the body could be the factors determining the place of blood-sucking. The visual attractiveness of the human body for tabanids was investigated through a black and a white mannequin in the Danube floodplain, Slovakia. The mannequins were covered by glue once a week. On both of the mannequins, 332 horse flies were stuck. The trapped horse flies on the mannequins belong to the genera Tabanus Linnaeus, 1758, Hybomitra Enderlein, 1922, Chrysops Meigen, 1803, Haematopota Linnaeus, 1758 and Atylotus (Linnaeus, 1767). Based on the trapped tabanids, the black mannequin is 6.06 times more attractive than the white one. The most tabanid carcasses were found on the lower limbs (56.32%) and the least occurred on the head (2.1%), the difference in their occurrence between the lower limbs and head of both mannequins was significant (P < 0.001). A similar significant difference was observed on certain parts of both mannequins on the taxonomical level of the genera Tabanus, Hybomitra, and Atylotus, the carcasses of which aggregated mostly on the lower limbs, rather than on the upper limbs and head.

A novel image semantic communication method via dynamic decision generation network and generative adversarial network.

Effectively compressing transmitted images and reducing the distortion of reconstructed images are challenges in image semantic communication. This paper proposes a novel image semantic communication model that integrates a dynamic decision generation network and a generative adversarial network to address these challenges as efficiently as possible. At the transmitter, features are extracted and selected based on the channel's signal-to-noise ratio (SNR) using semantic encoding and a dynamic decision generation network. This semantic approach can effectively compress transmitted images, thereby reducing communication traffic. At the receiver, the generator/decoder collaborates with the discriminator network, enhancing image reconstruction quality through adversarial and perceptual losses. The experimental results on the CIFAR-10 dataset demonstrate that our scheme achieves a peak SNR of 26 dB, a structural similarity of 0.9, and a compression ratio (CR) of 81.5% in an AWGN channel with an SNR of 3 dB. Similarly, in the Rayleigh fading channel, the peak SNR is 23 dB, structural similarity is 0.8, and the CR is 80.5%. The learned perceptual image patch similarity in both channels is below 0.008. These experiments thoroughly demonstrate that the proposed semantic communication is a superior deep learning-based joint source-channel coding method, offering a high CR and low distortion of reconstructed images.

A text guided cross modal joint inpainting algorithm for ancient murals

Existing deep learning algorithms for ancient mural inpainting only consider prior information of mural images to guide the inpainting of damaged areas, lacking guidance from textual semantic information, which often leads to problems such as inconsistent semantics and disordered textures in the inpainting results. In this work, we propose a text guided cross modal joint inpainting algorithm for ancient murals. Firstly, a cross modal joint inpainting network for ancient murals was constructed, which introduced text semantic information as the control guidance for mural inpainting, providing contextual information for mural image inpainting and guiding the inpainting of damaged areas. Then, design a text encoder and an image encoder to extract text and image features respectively, including word and sentence features. Input the sentence features into the conditional enhancement module, resample the text sentences, and solve the problem of text sparsity. Secondly, design a text decoupling module that utilizes a multimodal attention mechanism to extract the semantic information of the missing parts of damaged murals, and design a text semantic enhancement module to improve the semantic representation ability of the text, achieve the repair of local details of murals, and solve the problem of texture disorder. Finally, the reconstruction and repair of murals are completed through a residual discriminator game and a spectral normalized Markov dual discriminator against the mural generation network. Through inpainting experiments on real Dunhuang murals, it has been shown that the proposed method is more effective than comparative methods in repairing damaged murals, achieving better visual perception and coordination in reconstruction, and outperforming comparative algorithms in both subjective and objective evaluations.

Understanding the Associations between Executive Function and Psychological Variables in Fibromyalgia Syndrome: A Network Analysis Approach.

The aim of this study was to quantify the multivariate relationships between clinical, cognitive performance, executive functioning, and psychological outcomes in women with fibromyalgia (FMS) using network analyses. Demographic (age, height, weight), clinical (pain history, pain intensity, and related disability), neurocognitive (D2 Attention test, Rey-Osterrieth Complex Figure for visual perception, "Digits D/R/I" tests of the WAIS-IV battery for working memory, the 5-Digit Test for mental inhibition, the Symbol Search for processing speed and the Zoo Test for planning/decision making) and psychological (depressive symptoms, anxiety levels, sleep quality, pain hypervigilance) variables were collected in 129 women with FMS and 111 healthy women. Network analyses were conducted separately for each group to quantify the adjusted correlations between the modeled variables and to assess their centrality indices (i.e., connectivity with other symptoms in the network and their importance in the network). The network identified 74 associations in FMS women and 46 associations in controls with small differences. The strongest correlations in both groups were found between different attention variables: d2_CON with d2_C, d2_O with d_2TR, and d2_CON with d2_TA. The most central variables were d2_TA, d2_C, and d2_CON (highest strength centrality in both groups) and anxiety levels and pain hypervigilance (highest harmonic centrality in FMS women). The strength centrality of the network was stable for women with FMS (CScor0.7: 0.68) but not for healthy women (CScor0.7: 0.28). This study found that attention variables are most relevant within a neurocognitive network and that psychological variables are most important for the treatment of women with FMS. The clinical implications of the current findings, such as the development of treatments targeting these variables, are discussed.