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Background Vascular cognitive impairment (VCI) is a complex spectrum encompassing post-stroke cognitive impairment (PSCI) and small vessel disease-related cognitive impairment. Despite the growing health, social, and economic burden of VCI, to date, no specific treatment is available, prompting the introduction of the concept of a disease modifier. Consensus and suggestions Within this clinical spectrum, VCI and PSCI remain advancing conditions as neurodegenerative diseases with progression of both vascular and degenerative lesions accounting for cognitive decline. Disease-modifying strategies should integrate both pharmacological and non-pharmacological multimodal approaches, with pleiotropic effects targeting (1) endothelial and brain–blood barrier dysfunction; (2) neuronal death and axonal loss; (3) cerebral plasticity and compensatory mechanisms; and (4) degenerative-related protein misfolding. Moreover, pharmacological and non-pharmacological treatment in PSCI or VCI requires valid study designs clearly stating the definition of basic methodological issues, such as the instruments that should be used to measure eventual changes, the biomarker-based stratification of participants to be investigated, and statistical tests, as well as the inclusion and exclusion criteria that should be applied. Conclusion A consensus emerged to propose the development of a disease-modifying strategy in VCI and PSCI based on pleiotropic pharmacological and non-pharmacological approaches.

Identifying modifiable risk factors for Alzheimer's disease (AD) is critical for research. Data mining may be a useful tool for finding new AD associated factors. We included all patients over 49 years of age, hospitalized in France in 2008 (without dementia) and in 2014. Dependent variable was AD or AD dementia diagnosis in 2014. We recoded the diagnoses of hospital stays (in ICD-10) into 137 explanatory variables.To avoid overweighting the \"age\" variable, we divided the population into 7 sub-populations of 5 years. We analyzed 1,390,307 patients in the PMSI in 2008 and 2014: 55,997 patients had coding for AD or AD dementia in 2014 (4.04%). We associated Alzheimer disease in 2014 with about 20 variables including male sex, stroke, diabetes mellitus, mental retardation, bipolar disorder, intoxication, Parkinson disease, depression, anxiety disorders, alcohol, undernutrition, fall and 3 less explored variables: intracranial hypertension (odd radio [95% confidence interval]: 1.16 [1.12-1.20] in 70-80 years group), psychotic disorder (OR: 1.09 [1.07-1.11] in 70-75 years group) and epilepsy (OR: 1.06 [1.05-1.07] after 70 years). We analyzed 137 variables in the PMSI identified some well-known risk factors for AD, and highlighted a possible association with intracranial hypertension, which merits further investigation. Better knowledge of associations could lead to better targeting (identifying) at-risk patients, and better prevention of AD, in order to reduce its impact.

BackgroundCognitive and emotional disorders frequently persist after recovery from the acute symptoms of COVID-19; possible explanations include pneumonia-induced hypoxia, infection of the central nervous system, and microstrokes. The objective of the present study was to characterize the impact of hypoxia on the cognitive and psychological profile following COVID-19.MethodsSixty-two patients with COVID-19 were enrolled in a cross-sectional study and divided into two groups based on disease severity: outpatients with no pulmonary complications vs. inpatients with hypoxemic pneumonia having received oxygen therapy. All the participants underwent a comprehensive neuropsychological evaluation that included depression, anxiety, fatigue, sleepiness, attentional, memory and executive processes, and social cognition. For the inpatients, we also collected laboratory data (blood gas, blood glucose, fibrin, fibrinogen, D-dimer, and C-reactive protein).ResultsCognitive disorders was found in patients with COVID-19: at least 18% had an impairment of memory and 11% had attentional dysfunctions. A high level of fatigue (90% of the patients), anxiety (52%), and depression (50%) was also observed. The impairments in attentional (p < 0.001 for omission and commission in CPT 3) and memory (p < 0.003 for Index Cue Efficiency from free and cue selected reminding test) functions were greater in COVID-19 inpatients that in COVID-19 outpatients. In contrast, levels of fatigue, depression, and anxiety were similarly high in both groups.ConclusionsThese findings might help to improve the management of COVID-19 patients as a function of the disease severity in particular for patients with hypoxia.

Background. Poststroke fatigue (PSF) is a frequent, disabling symptom that lacks a consensual definition and a standardized evaluation method. The (multiple) causes of PSF have not been formally characterized. Objective. To identify factors associated with PSF. Method. A systematic review of articles referenced in MEDLINE. Only original studies having measured PSF and potentially associated factors were included. Data was extracted from articles using predefined data fields. Results. Although PSF tends to be more frequent in female patients and older patients, sociodemographic factors do not appear to have a major impact. There are strong associations between PSF and emotional disturbances (such as depression and anxiety). PSF may also be linked to attentional disturbances (mainly slowing in processing speed). The literature data have failed to demonstrate a clear impact of the type and severity of stroke. It has been suggested that PSF results from alterations in the frontothalamostriatal system and/or inflammatory processes. Pain, sleep disorders, and prestroke fatigue also appeared to be associated with PSF. Implications. A better understanding of PSF may improve stroke patient care and facilitate the development of effective treatments.

The BDNF Val66Met gene polymorphism is a relevant factor explaining inter-individual differences to TMS responses in studies of the motor system. However, whether this variant also contributes to TMS-induced memory effects, as well as their underlying brain mechanisms, remains unexplored. In this investigation, we applied rTMS during encoding of a visual memory task either over the left frontal cortex (LFC; experimental condition) or the cranial vertex (control condition). Subsequently, individuals underwent a recognition memory phase during a functional MRI acquisition. We included 43 young volunteers and classified them as 19 Met allele carriers and 24 as Val/Val individuals. The results revealed that rTMS delivered over LFC compared to vertex stimulation resulted in reduced memory performance only amongst Val/Val allele carriers. This genetic group also exhibited greater fMRI brain activity during memory recognition, mainly over frontal regions, which was positively associated with cognitive performance. We concluded that BDNF Val66Met gene polymorphism, known to exert a significant effect on neuroplasticity, modulates the impact of rTMS both at the cognitive as well as at the associated brain networks expression levels. This data provides new insights on the brain mechanisms explaining cognitive inter-individual differences to TMS, and may inform future, more individually-tailored rTMS interventions.

BackgroundCognitive impairment is frequent after stroke, and several studies have suggested that biological and imaging characteristics present before stroke are associated with the development of post-stroke cognitive impairment.ObjectiveThe aim of our study was to systematically review biological and imaging predictors of cognitive impairment after stroke.MethodStudies were identified from bibliographic databases and reference lists, and were included if conducted in patients with acute stroke, with at least 30 patients, and a follow-up of at least 3 months. We included articles on potential biomarkers of cognitive impairment that pre-existed to stroke.ResultsWe identified 22,169 articles, including 20,349 with abstract. After analysis, 66 studies conducted in 42 cohorts met selection criteria. They included 30–9522 patients [median 170; interquartile range (IQR) 104–251] with a median follow-up of 12 months (IQR 3–36). All studies met quality criteria for description of the study population and standardization of biomarkers. Twenty-nine studies met all quality criteria. There was no convincing evidence that any biological marker may predict cognitive impairment. The most consistent predictors of cognitive impairment after stroke were global atrophy and medial temporal lobe atrophy.ConclusionPre-existing cerebral atrophy is the most consistent predictor of cognitive impairment that can be identified in patients with an acute stroke.

Several observational studies have found a link between the long-term use of benzodiazepines and dementia, which remains controversial. Our study was designed to assess (i) whether the long-term use of benzodiazepines, at two different doses, has an irreversible effect on cognition, (ii) and whether there is an age-dependent effect. One hundred and five C57Bl/6 male mice were randomly assigned to the 15 mg/kg/day, the 30 mg/kg/day diazepam-supplemented pellets, or the control group. Each group comprised mice aged 6 or 12 months at the beginning of the experiments and treated for 16 weeks. Two sessions of behavioral assessment were conducted: after 8 weeks of treatment and after treatment completion following a 1-week wash-out period. The mid-treatment test battery included the elevated plus maze test, the Y maze spontaneous alternation test, and the open field test. The post-treatment battery was upgraded with three additional tests: the novel object recognition task, the Barnes maze test, and the touchscreen-based paired-associated learning task. At mid-treatment, working memory was impaired in the 15 mg/kg diazepam group compared to the control group (p = 0.005). No age effect was evidenced. The post-treatment assessment of cognitive functions (working memory, visual recognition memory, spatial reference learning and memory, and visuospatial memory) did not significantly differ between groups. Despite a cognitive impact during treatment, the lack of cognitive impairment after long-term treatment discontinuation suggests that benzodiazepines alone do not cause irreversible deleterious effects on cognitive functions and supports the interest of discontinuation in chronically treated patients.

The misuse of benzodiazepines and opioid medications is frequent in students. To improve our understanding of this behavior, we aimed to identify factors associated with separate and concomitant use of these substances. Anonymous self-reported questionnaires were e-mailed to students enrolled at a French university between March and July 2021, covering: sociodemographic characteristics, academics, psychoactive substance use, ADHD symptomatology (adulthood and childhood), and psychiatric/psychological or addiction follow-up. Factors associated with the use of benzodiazepines and opioid medications included female sex (OR = 1.41 [1.08; 1.86]) and OR = 1.38 [1.06; 1.79], respectively), older age (OR = 1.65 [1.04; 2.6] and OR = 2.17 [1.4; 3.36], respectively), current psychiatric/psychological follow-up (OR = 6.53 [5.18; 8.24] and OR= 1.5 [1.12; 2.0], respectively), ADHD symptomatology (OR= 2.33 [1.71;3.16] and OR= 1.61 [1.15; 2.24], respectively), polyconsumption (tobacco use for benzodiazepine users, OR = 1.38 [1.04; 1.82]; alcohol use OR = 1.67 [1.17; 2.39] and tobacco use OR = 1.62 [1.23; 2.14] for opioid users). These factors were even more strongly associated with the concomitant use of benzodiazepines and opioid medications: older age (OR = 3.64 [2.22; 5.99]), female sex (OR = 1.54 [1.1; 2.14]), grade repetition (OR = 1.7 [1.14; 2.54]), psychiatric/psychological follow-up (OR = 4.51 [3.35;6.06]), ADHD symptomatology (OR = 5.3 [3.69; 7.63]), polyconsumption (tobacco use OR = 2.05 [1.39; 3] and cannabis use, OR = 2.07 [1.97; 4.16]. The factors associated with the use of benzodiazepines and prescription opioids identified in this study could lead to the development of targeted prevention methods.

Large-scale longitudinal multi-site MRI brain morphometry studies are becoming increasingly crucial to characterize both normal and clinical population groups using fully automated segmentation tools. The test–retest reproducibility of morphometry data acquired across multiple scanning sessions, and for different MR vendors, is an important reliability indicator since it defines the sensitivity of a protocol to detect longitudinal effects in a consortium. There is very limited knowledge about how across-session reliability of morphometry estimates might be affected by different 3T MRI systems. Moreover, there is a need for optimal acquisition and analysis protocols in order to reduce sample sizes. A recent study has shown that the longitudinal FreeSurfer segmentation offers improved within session test–retest reproducibility relative to the cross-sectional segmentation at one 3T site using a nonstandard multi-echo MPRAGE sequence. In this study we implement a multi-site 3T MRI morphometry protocol based on vendor provided T1 structural sequences from different vendors (3D MPRAGE on Siemens and Philips, 3D IR-SPGR on GE) implemented in 8 sites located in 4 European countries. The protocols used mild acceleration factors (1.5–2) when possible. We acquired across-session test–retest structural data of a group of healthy elderly subjects (5 subjects per site) and compared the across-session reproducibility of two full-brain automated segmentation methods based on either longitudinal or cross-sectional FreeSurfer processing. The segmentations include cortical thickness, intracranial, ventricle and subcortical volumes. Reproducibility is evaluated as absolute changes relative to the mean (%), Dice coefficient for volume overlap and intraclass correlation coefficients across two sessions. We found that this acquisition and analysis protocol gives comparable reproducibility results to previous studies that used longer acquisitions without acceleration. We also show that the longitudinal processing is systematically more reliable across sites regardless of MRI system differences. The reproducibility errors of the longitudinal segmentations are on average approximately half of those obtained with the cross sectional analysis for all volume segmentations and for entorhinal cortical thickness. No significant differences in reliability are found between the segmentation methods for the other cortical thickness estimates. The average of two MPRAGE volumes acquired within each test–retest session did not systematically improve the across-session reproducibility of morphometry estimates. Our results extend those from previous studies that showed improved reliability of the longitudinal analysis at single sites and/or with non-standard acquisition methods. The multi-site acquisition and analysis protocol presented here is promising for clinical applications since it allows for smaller sample sizes per MRI site or shorter trials in studies evaluating the role of potential biomarkers to predict disease progression or treatment effects. •We implemented a multi-site 3T MRI protocol for brain morphometry on 8EU sites.•We acquired across-session test-retest data on 40 healthy elderly subjects.•We calculated the reproducibility of cortical and volumetric FreeSurfer estimates.•Longitudinal segmentation was more reliable than cross-sectional on all sites.

Dynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks. In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks –including Rapid Visual Processing (RVP, assessing sustained visual attention)– and dFC speed quantified at the level of functional sub-networks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module. •Sleep Deprivation (SD) slows down the random walk in FC space implemented by Dynamic Functional Connectivity (dFC) at rest.•Whole-brain level slowing of dFC speed does not selectively correlate with fine and task-specific changes in performance.•We quantify dFC speed separately for different link-based modules coordinated by distinct regional “meta-hubs”.•Modular dFC speed variations capture subtle and task-specific variations of cognitive performance induced by SD.