Unraveling brain fog in post-COVID syndrome: Relationship between subjective cognitive complaints and cognitive function, fatigue, and neuropsychiatric symptoms.

BACKGROUND AND PURPOSE: "Brain fog" is a frequent and disabling symptom that can occur after SARS-CoV-2 infection. However, its clinical characteristics and the relationships among brain fog and objective cognitive function, fatigue, and neuropsychiatric symptoms (depression, anxiety) are still unclear. In this study, we aimed to examine the characteristics of brain fog and to understand how fatigue, cognitive performance, and neuropsychiatric symptoms and the mutual relationships among these variables influence subjective cognitive complaints. METHODS: A total of 170 patients with cognitive complaints in the context of post-COVID syndrome were evaluated using a comprehensive neuropsychological protocol. The FLEI scale was used to characterize subjective cognitive complaints. Correlation analysis, regression machine-learning algorithms, and mediation analysis were calculated. RESULTS: Cognitive complaints were mainly attention and episodic memory symptoms, while executive functions (planning) issues were less often reported. The FLEI scale, a mental ability questionnaire, showed high correlations with a fatigue scale and moderate correlations with the Stroop test, and anxiety and depressive symptoms. Random forest algorithms showed an R2 value of 0.409 for the prediction of FLEI score, with several cognitive tests, fatigue and depression being the best variables used in the prediction. Mediation analysis showed that fatigue was the main mediator between objective and subjective cognition, while the effect of depression was indirect and mediated through fatigue. CONCLUSIONS: Brain fog associated with COVID-19 is mainly characterized by attention and episodic memory, and fatigue, which is the main mediator between objective and subjective cognition. Our findings contribute to understanding the pathophysiology of brain fog and emphasize the need to unravel the main mechanisms underlying brain fog, considering several aspects.

Normative Data in Cross-Cultural Neuropsychological Instruments in Spaniards and Colombians Living in Spain.

BACKGROUND: Cross-Cultural Dementia Screening (CCD), Rowland Universal Dementia Assessment Scale (RUDAS), and European Cross-cultural Neuropsychological Test Battery (CNTB) are three novel neuropsychological instruments developed from a cross-cultural perspective to reduce the impact of culture in cognitive assessment and improve the assessment in diverse populations. OBJECTIVE: We aimed to collect and present normative data on these tests in a majority population sample (Spaniards living in Spain) and in a minority population sample (Colombians living in Spain). METHODS: CCD, RUDAS, and CNTB were administered to a group of 300 cognitively healthy participants (150 Spaniards and 150 Colombians). Linear regression modeling strategy was used to provide adjusted norms for demographic factors and to explore the influence of these factors on test performance. RESULTS: Most of the CCD and CNTB scores were predicted by age and years of education, with some tests only predicted by age or showing a ceiling effect. The comparison of normative data between the two samples confirmed the favorable cross-cultural properties of these instruments, with only some differences in processing speed and executive functioning scores. CONCLUSIONS: Our study finds a comparable influence of demographic factors in both populations on the performance of CCD, RUDAS, and CNTB, confirming their adequate cross-cultural properties. We provide normative data for these tests in Spaniards and Colombians living in Spain.

Transcranial direct current stimulation for post-COVID fatigue: a randomized, double-blind, controlled pilot study.

Fatigue is one of the most frequent and disabling symptoms of the post-COVID syndrome. In this study, we aimed to assess the effects of transcranial direct current stimulation on fatigue severity in a group of patients with post-COVID syndrome and chronic fatigue. We conducted a double-blind, parallel-group, sham-controlled study to evaluate the short-term effects of anodal transcranial direct current stimulation (2 mA, 20 min/day) on the left dorsolateral prefrontal cortex. The modified fatigue impact scale score was used as the primary endpoint. Secondary endpoints included cognition (Stroop test), depressive symptoms (Beck depression inventory) and quality of life (EuroQol-5D). Patients received eight sessions of transcranial direct current stimulation and were evaluated at baseline, immediately after the last session, and one month later. Forty-seven patients were enrolled (23 in the active treatment group and 24 in the sham treatment group); the mean age was 45.66 ± 9.49 years, and 37 (78.72%) were women. The mean progression time since the acute infection was 20.68 ± 6.34 months. Active transcranial direct current stimulation was associated with a statistically significant improvement in physical fatigue at the end of treatment and 1 month as compared with sham stimulation. No significant effect was detected for cognitive fatigue. In terms of secondary outcomes, active transcranial direct current stimulation was associated with an improvement in depressive symptoms at the end of treatment. The treatment had no effects on the quality of life. All the adverse events reported were mild and transient, with no differences between the active stimulation and sham stimulation groups. In conclusion, our results suggest that transcranial direct current stimulation on the dorsolateral prefrontal cortex may improve physical fatigue. Further studies are needed to confirm these findings and optimize stimulation protocols.

FDG-PET-based neural correlates of Addenbrooke's cognitive examination III scores in Alzheimer's disease and frontotemporal degeneration.

Introduction: The Addenbrooke's Cognitive Examination III (ACE-III) is a brief test useful for neuropsychological assessment. Several studies have validated the test for the diagnosis of Alzheimer's disease (AD) and frontotemporal dementia (FTD). In this study, we aimed to examine the metabolic correlates associated with the performance of ACE-III in AD and behavioral variant FTD. Methods: We enrolled 300 participants in a cross-sectional study, including 180 patients with AD, 60 with behavioral FTD (bvFTD), and 60 controls. An 18F-Fluorodeoxyglucose positron emission tomography study was performed in all cases. Correlation between the ACE-III and its domains (attention, memory, fluency, language, and visuospatial) with the brain metabolism was estimated. Results: The ACE-III showed distinct neural correlates in bvFTD and AD, effectively capturing the most relevant regions involved in these disorders. Neural correlates differed for each domain, especially in the case of bvFTD. Lower ACE-III scores were associated with more advanced stages in both disorders. The ACE-III exhibited high discrimination between bvFTD vs. HC, and between AD vs. HC. Additionally, it was sensitive to detect hypometabolism in brain regions associated with bvFTD and AD. Conclusion: Our study contributes to the knowledge of the brain regions associated with ACE-III, thereby facilitating its interpretation, and highlighting its suitability for screening and monitoring. This study provides further validation of ACE-III in the context of AD and FTD.

Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study.

Background. Primary progressive aphasia (PPA) is a neurodegenerative syndrome in which diagnosis is usually challenging. Biomarkers are needed for diagnosis and monitoring. In this study, we aimed to evaluate Electroencephalography (EEG) as a biomarker for the diagnosis of PPA. Methods. We conducted a cross-sectional study with 40 PPA patients categorized as non-fluent, semantic, and logopenic variants, and 20 controls. Resting-state EEG with 32 channels was acquired and preprocessed using several procedures (quantitative EEG, wavelet transformation, autoencoders, and graph theory analysis). Seven machine learning algorithms were evaluated (Decision Tree, Elastic Net, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gaussian Naive Bayes, and Multinomial Naive Bayes). Results. Diagnostic capacity to distinguish between PPA and controls was high (accuracy 75%, F1-score 83% for kNN algorithm). The most important features in the classification were derived from network analysis based on graph theory. Conversely, discrimination between PPA variants was lower (Accuracy 58% and F1-score 60% for kNN). Conclusions. The application of ML to resting-state EEG may have a role in the diagnosis of PPA, especially in the differentiation from controls. Future studies with high-density EEG should explore the capacity to distinguish between PPA variants.