Patients with fibromyalgia are characterised by having, along with persistent chronic pain, cognitive impairments, mainly in working memory capacity. It has been suggested that abnormalities in fronto-parietal neural circuits might account for this dysfunction. However, limited body of neuroimaging research and mixed or inconsistent results have led to inconclusive evidence. Our investigation aimed to explore the neural links between working memory dysfunction and brain grey matter volume in fibromyalgia using voxel-based morphometry (VBM), considering the influence of pain and affective symptoms. Thirty female patients with fibromyalgia and twenty-seven healthy female individuals participated in the present investigation. Working memory functioning was assessed using the Wechsler Adult Intelligence Scale III (WAIS-III) and the Wechsler Memory Scale III (WMS-III). VBM data (global and regional grey matter volume) were also analysed. As expected, fibromyalgia patients scored lower on Arithmetic, Letter-Number Sequencing and Working Memory Index than healthy individuals. Nonetheless, we found no differences in grey matter volume between groups. Moderation analyses highlighted the importance of considering affective symptoms of fibromyalgia, such as depression, to characterize associations between local grey matter volumes in the insula and prefrontal cortices (orbitofrontal and ventrolateral prefrontal cortex) with working memory functioning. These results suggest that moderate to severe symptoms of depression appear to be critical for understanding the emergence of associations between grey matter volume and working memory, offering valuable new insights into this complex relationship. Further research based on multimodal imaging approaches is needed to refine current findings and explore neural circuits underlying cognitive impairment in fibromyalgia.

Fibromyalgia involves widespread musculoskeletal pain and hypersensitivity, often accompanied by neurological, cognitive, and affective disturbances. Resting-state electroencephalography studies have revealed abnormal brain activity in chronic pain conditions, with anxiety and symptom duration potentially exacerbating these alterations. This study applied multivariate pattern analysis to differentiate inter-trial resting-state electroencephalography signals between fibromyalgia patients and healthy controls across frequency bands associated with pain processing, incorporating state and trait anxiety scores. It also examined differences between patients with short and long duration symptoms and identified the most relevant scalp regions contributing to the models. Fifty-one female participants (25 fibromyalgia patients, 26 controls; aged 35-65) were included. Patients were classified into short-term (12) and long-term (13) groups. Normalized power spectral density values were extracted from electroencephalography data and used to train machine learning classifiers, with Haufe-transformed weights computed to determine key scalp contributions. The models distinguished patients from controls with area under the curve values exceeding 0.75 across all frequency bands, reaching 0.99 in beta and gamma bands when anxiety was included. Symptom duration was also a relevant factor, as the model differentiated short-from long-term fibromyalgia patients with area under the curve values up to 0.96 in beta and gamma bands. Alterations in theta power within frontal and parietal regions, along with frequency-specific contributions, highlight disrupted pain processing in fibromyalgia and suggest cumulative effects of prolonged symptom duration. Future resting-state studies leveraging multivariate pattern analysis may support the development of potential biomarkers to improve diagnosis and guide treatment strategies in clinical settings.

Background/Objectives: Although COVID-19 may cause cognitive impairments for up to six months, the long-term effects of mild cases remain unclear. Given their high exposure and critical role in public health, assessing this impact on healthcare workers is essential. Aim: The present study aimed to examine the cognitive and emotional effects of mild COVID-19 in 92 healthcare workers one year after infection. Methods: In total, 50 had experienced mild COVID-19, while 42 had not been infected. Participants completed a neuropsychological assessment evaluating attention, memory, and executive functions, along with self-reported measures of anxiety, depression, post-traumatic stress, occupational stress, and burnout. Results: No significant cognitive differences were observed between the groups. However, both exhibited moderate-to-severe psychological distress, with the COVID-19 group showing higher trait anxiety (p = 0.032). Emotional symptoms were significantly associated with neuropsychological performance—higher burnout (ρ from −0.20 to −0.28, p < 0.05) and stress (ρ from −0.25 to −0.33, p < 0.01) correlated with slower responses and more errors in tasks such as the D2 variation index, TESEN execution speed, Rey–Osterrieth Figure recall, and Digit Span forward span. Conclusions: These findings suggest no long-term cognitive impairment after mild COVID-19 but highlight the substantial emotional toll of the pandemic on healthcare workers. Future research should explore cognitive reserve as a protective factor.

This chapter presents an artificial intelligence-based approach for the early detection and classification of neurocognitive disorders using structural magnetic resonance imaging. The work focuses on the development of a convolutional neural network capable of distinguishing between healthy individuals, mild cognitive impairment, and major dementia subtypes, including Alzheimer's disease, vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. The proposed system leverages deep learning techniques to identify subtle neuroanatomical patterns associated with early-stage neurodegeneration. Additionally, an intuitive application is presented, allowing clinicians to upload MRI scans and obtain probabilistic diagnostic predictions. The results demonstrate high classification accuracy and highlight the potential of integrating artificial intelligence into clinical workflows. This chapter per the authors contributes to intelligent neurocognitive healthcare systems by providing a scalable and clinically applicable solution for early diagnosis and decision support.

Introduction: Distress during pediatric magnetic resonance imaging (MRI) neuroimaging can compromise scan quality and negatively impact children’s experiences. This review aimed to systematically synthesize biomarkers and psychological factors associated with distress in children, adolescents, and young adults undergoing neuroimaging. Methods: This systematic review was conducted according to PRISMA and AMSTAR-2 guidelines and preregistered in OSF. A systematic search was performed in six electronic databases, including observational articles published between 2000 and 2025 that assessed distress during MRI and functional MRI (fMRI). Data extraction and risk of bias assessment (QUIPS tool) were performed independently by two reviewers. Results: Ten studies (n = 558) examining distress during neuroimaging were included in this review. Distress was assessed through subjective self- and parent-reports, objective physiological measures, and qualitative interviews. Overall, distress levels were low to moderate; most participants tolerated scans well, though younger age, male sex, parental anxiety, procedure length, and chronic illness were associated with greater discomfort. Noise, immobility, and boredom emerged as the most frequent triggers, while strategies such as distraction, age-appropriate information, and reducing waiting times were perceived as helpful. Among participants with cancer, scan-related anxiety was closely linked to fear of recurrence and perceived stress. Risk of bias across studies was moderate to high, particularly in domains of attrition and statistical reporting. Conclusions: Distress during scanning is driven by anticipatory and parental anxiety, procedure length, and chronic illness. Biomarkers (e.g., cortisol, blood pressure) showed inconsistent links with subjective distress, highlighting the need for integrated measures.

Background/Objectives: Although COVID-19 may cause cognitive impairments for up to six months, the long-term effects of mild cases remain unclear. Given their high exposure and critical role in public health, assessing this impact on healthcare workers is essential. Aim: This study examined the cognitive and emotional effects of mild COVID-19 in 92 healthcare workers one year after infection. Methods: Fifty had experienced mild COVID-19, while 42 had not been infected. Participants completed a neuropsychological assessment evaluating attention, memory, and executive functions, along with self-reported measures of anxiety, depression, post-traumatic stress, occupational stress, and burnout. Results: No significant cognitive differences were observed between groups. However, both exhibited moderate to severe psychological distress, with the COVID-19 group showing higher trait anxiety. Emotional symptoms were significantly associated with neuropsychological performance: higher burnout and stress correlated with more errors and slower responses on tasks such as the D2 variance index, TESEN, Rey-Osterrieth Figure, and Digit Span. Conclusions: These findings suggest no long-term cognitive impairment after mild COVID-19 but highlight the substantial emotional toll of the pandemic on healthcare workers. Future research should explore cognitive reserve as a protective factor.