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Background: Alzheimer's disease (AD) is characterized by progressive deterioration of cognitive functions. Some individuals with subjective cognitive decline (SCD) are in the early phase of the disease and subsequently progress through the AD continuum. Although neuroimaging biomarkers could be used for the accurate and early diagnosis of preclinical AD, the findings in SCD samples have been heterogeneous. This study established the morphological differences in brain magnetic resonance imaging (MRI) findings between individuals with SCD and those without cognitive impairment based on a clinical sample of patients defined according to SCD-Initiative recommendations. Moreover, we investigated baseline structural changes in the brains of participants who remained stable or progressed to mild cognitive impairment or dementia., Methods: This study included 309 participants with SCD and 43 healthy controls (HCs) with high-quality brain MRI at baseline. Among the 99 subjects in the SCD group who were followed clinically, 32 progressed (SCDp) and 67 remained stable (SCDnp). A voxel-wise statistical comparison of gray and white matter (WM) volume was performed between the HC and SCD groups and between the HC, SCDp, and SCDnp groups. XTRACT ATLAS was used to define the anatomical location of WM tract damage. Region-of-interest (ROI) analyses were performed to determine brain volumetric differences. White matter lesion (WML) burden was established in each group., Results: Voxel-based morphometry (VBM) analysis revealed that the SCD group exhibited gray matter atrophy in the middle frontal gyri, superior orbital gyri, superior frontal gyri, right rectal gyrus, whole occipital lobule, and both thalami and precunei. Meanwhile, ROI analysis revealed decreased volume in the left rectal gyrus, bilateral medial orbital gyri, middle frontal gyri, superior frontal gyri, calcarine fissure, and left thalamus. The SCDp group exhibited greater hippocampal atrophy (p < 0.001) than the SCDnp and HC groups on ROI analyses. On VBM analysis, however, the SCDp group exhibited increased hippocampal atrophy only when compared to the SCDnp group (p < 0.001). The SCD group demonstrated lower WM volume in the uncinate fasciculus, cingulum, inferior fronto-occipital fasciculus, anterior thalamic radiation, and callosum forceps than the HC group. However, no significant differences in WML number (p = 0.345) or volume (p = 0.156) were observed between the SCD and HC groups., Conclusions: The SCD group showed brain atrophy mainly in the frontal and occipital lobes. However, only the SCDp group demonstrated atrophy in the medial temporal lobe at baseline. Structural damage in the brain regions was anatomically connected, which may contribute to early memory decline., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Subjective cognitive decline (SCD) may represent a preclinical stage of Alzheimer's disease (AD). Predicting progression of SCD patients is of great importance in AD-related research but remains a challenge., Objective: To develop and implement an ensemble machine learning (ML) algorithm to identify SCD subjects at risk of conversion to mild cognitive impairment (MCI) or AD., Methods: Ninety-nine SCD patients were included. Thirty-two progressed to MCI/AD, while 67 remained stable. To minimize the effect of class imbalance, both classes were balanced, and sensitivity was taken as evaluation metric. Bagging and boosting ML models were developed by using socio-demographic and clinical information, Mini-Mental State Examination and Geriatric Depression Scale (GDS) scores (feature-set 1a); socio-demographic characteristics and neuropsychological tests scores (feature-set 1b) and regional magnetic resonance imaging grey matter volumes (feature-set 2). The most relevant variables were combined to find the best model., Results: Good prediction performances were obtained with feature-sets 1a and 2. The most relevant variables (variable importance exceeding 20%) were: Age, GDS, and grey matter volumes measured in four cortical regions of interests. Their combination provided the optimal classification performance (highest sensitivity and specificity) ensemble ML model, Extreme Gradient Boosting with over-sampling of the minority class, with performance metrics: sensitivity = 1.00, specificity = 0.92 and area-under-the-curve = 0.96. The median values based on fifty random train/test splits were sensitivity = 0.83 (interquartile range (IQR) = 0.17), specificity = 0.77 (IQR = 0.23) and area-under-the-curve = 0.75 (IQR = 0.11)., Conclusion: A high-performance algorithm that could be translatable into practice was able to predict SCD conversion to MCI/AD by using only six predictive variables.

Patients with haematological malignancies are considered to be a risk group for developing severe Coronavirus disease (Covid-19). Because of the limitations of therapeutic options, the development of new treatment strategies is mandatory, such as convalescent plasma (CP). Herein we report the use of CP therapy as an off-label indication in two lymphoma patients with relapsed COVID-19 in the setting of low gammaglobulin levels because of previous rituximab chemo-immunotherapy. Both were PCR positive for SARS-CoV-2 but had an absence of antibodies to the virus more than one month later of symptoms initiation. They developed important respiratory and neurological complications. After CP infusion, neutralising antibodies were detected and viral load dissapeared in both patients leading to clinical improvement with no more Covid-19 relapse., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Objectives: Since the beginning of the COVID-19 pandemic, the Spanish Society of Neurology has run a registry of patients with neurological involvement for the purpose of informing clinical neurologists. Encephalopathy and encephalitis were among the most frequently reported complications. In this study, we analyse the characteristics of these complications., Patients and Methods: We conducted a retrospective, descriptive, observational, multicentre study of patients with symptoms compatible with encephalitis or encephalopathy, entered in the Spanish Society of Neurology's COVID-19 Registry from 17 March to 6 June 2020., Results: A total of 232 patients with neurological symptoms were registered, including 51 cases of encephalopathy or encephalitis (21.9%). None of these patients were healthcare professionals. The most frequent syndromes were mild or moderate confusion (33%) and severe encephalopathy or coma (9.8%). The mean time between onset of infection and onset of neurological symptoms was 8.02 days. Lumbar puncture was performed in 60.8% of patients, with positive PCR results for SARS-CoV-2 in only one case. Brain MRI studies were performed in 47% of patients, with alterations detected in 7.8% of these. EEG studies were performed in 41.3% of cases, detecting alterations in 61.9%., Conclusions: Encephalopathy and encephalitis are among the complications most frequently reported in the registry. More than one-third of patients presented mild or moderate confusional syndrome. The mean time from onset of infection to onset of neurological symptoms was 8 days (up to 24hours earlier in women than in men). EEG was the most sensitive test in these patients, with very few cases presenting alterations in neuroimaging studies. All patients treated with boluses of corticosteroids or immunoglobulins progressed favourably., (Copyright © 2021 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.)