В. Липинський про бюрократію ("Листи до братів-хліборобів"): до проблеми визначення теоретичного підґрунтя поглядів

Розглянуто формування поглядів В. Липинського на бюрократію під впливом ідей Ж. Сореля, Р. Міхельса, К. Маркса, А. де Токвіля. Стверджено, що основу його поглядів становлять ідеї про бюрократію, запозичені у Ж. Сореля та Р. Міхельса. Підкреслено, що під час розгляду проблеми бюрократії вітчизняний мислитель не звертався до ідей М. Вебера.Deals with the formation of attitudes of Viacheslav Lypynsky on bureaucracy under the influence of Georges Sorel, Robert Michels, Karl Marx, Alexis de Tocqueville. It is alleged that his views were based on ideas of the bureaucracy borrowed from Georges Sorel and Robert Michels. Emphasizing that, in considering the problem of bureaucracy Ukrainian thinker did not approach the ideas of Max Weber

Prediction of Cognitive Recovery after Stroke:The Value of Diffusion-Weighted Imaging–Based Measures of Brain Connectivity

Background and Purpose: Prediction of long-term recovery of a poststroke cognitive disorder (PSCD) is currently inaccurate. We assessed whether diffusion-weighted imaging (DWI)–based measures of brain connectivity predict cognitive recovery 1 year after stroke in patients with PSCD in addition to conventional clinical, neuropsychological, and imaging variables. Methods: This prospective monocenter cohort study included 217 consecutive patients with a clinical diagnosis of ischemic stroke, aged ≥50 years, and Montreal Cognitive Assessment score below 26 during hospitalization. Five weeks after stroke, patients underwent DWI magnetic resonance imaging. Neuropsychological assessment was performed 5 weeks and 1 year after stroke and was used to classify PSCD as absent, modest, or marked. Cognitive recovery was operationalized as a shift to a better PSCD category over time. We evaluated 4 DWI-based measures of brain connectivity: global network efficiency and mean connectivity strength, both weighted for mean diffusivity and fractional anisotropy. Conventional predictors were age, sex, level of education, clinical stroke characteristics, neuropsychological variables, and magnetic resonance imaging findings (eg, infarct size). DWI-based measures of brain connectivity were added to a multivariable model to assess additive predictive value. Results: Of 135 patients (mean age, 71 years; 95 men [70%]) with PSCD 5 weeks after ischemic stroke, 41 (30%) showed cognitive recovery. Three of 4 brain connectivity measures met the predefined threshold of P<0.1 in univariable regression analysis. There was no added value of these measures to a multivariable model that included level of education and infarct size as significant predictors of cognitive recovery. Conclusions: Current DWI-based measures of brain connectivity appear to predict recovery of PSCD but at present have no added value over conventional predictors

Impaired emotion recognition after left hemispheric stroke:A case report and brief review of the literature

Impaired recognition of emotion after stroke can have important implications for social competency, social participation, and consequently quality of life. We describe a case of left hemispheric ischemic stroke with impaired recognition of specifically faces expressing fear. Three months later, the patient's spouse reports that the patient was irritable and slow in communication, which may be caused by the impaired emotion recognition. The case is discussed in relation to the literature concerning emotion recognition and its neural correlates. Our case supports the notion that emotion recognition, including fear recognition, is regulated by a network of interconnected brain regions located in both hemispheres. We conclude that impaired emotion recognition is not uncommon after stroke and can be caused by dysfunction of this emotion-network

A role for new brain magnetic resonance imaging modalities in daily clinical practice; protocol of the prediction of cognitive recovery after stroke (PROCRAS) study.

Background: Cognitive impairment is common after acute ischemic stroke, affecting up to 75% of the patients. About half of the patients will show recovery, whereas the others will remain cognitively impaired or deteriorate. It is difficult to predict these different cognitive outcomes. Objective: The objective of this study is to investigate whether diffusion tensor imaging-based measures of brain connectivity predict cognitive recovery after 1 year, in addition to patient characteristics and stroke severity. A specific premise of the Prediction of Cognitive Recovery After Stroke (PROCRAS) study is that it is conducted in a daily practice setting. Methods: The PROCRAS study is a prospective, mono-center cohort study conducted in a large teaching hospital in the Netherlands. A total of 350 patients suffering from an ischemic stroke who screen positive for cognitive impairment on the Montreal Cognitive Assessment (MoCA<26) in the acute stage will undergo a 3Tesla-Magnetic Resonance Imaging (3T-MRI) with a diffusion-weighted sequence and a neuropsychological assessment. Patients will be classified as being unimpaired, as having a mild vascular cognitive disorder, or as having a major vascular cognitive disorder. One year after stroke, patients will undergo follow-up neuropsychological assessment. The primary endpoint is recovery of cognitive function 1 year after stroke in patients with a confirmed poststroke cognitive disorder. The secondary endpoint is deterioration of cognitive function in the first year after stroke. Results: The study is already ongoing for 1.5 years, and thus far, 252 patients have provided written informed consent. Final results are expected in June 2019. Conclusions: The PROCRAS study will show the additional predictive value of diffusion tensor imaging-based measures of brain connectivity for cognitive outcome at 1 year in patients with a poststroke cognitive disorder in a daily clinical practice setting

Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration

Introduction Many consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease. Methods Surveys, teleconferences, and an in-person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis. Results A framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness-neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository. Conclusions The HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease

Impact of thresholding on the consistency and sensitivity of diffusion MRI‐based brain networks in patients with cerebral small vessel disease

Abstract Introduction Thresholding of low‐weight connections of diffusion MRI‐based brain networks has been proposed to remove false‐positive connections. It has been previously established that this yields more reproducible scan–rescan network architecture in healthy subjects. In patients with brain disease, network measures are applied to assess inter‐individual variation and changes over time. Our aim was to investigate whether thresholding also achieves improved consistency in network architecture in patients, while maintaining sensitivity to disease effects for these applications. Methods We applied fixed‐density and absolute thresholding on brain networks in patients with cerebral small vessel disease (SVD, n = 86; ≈24 months follow‐up), as a clinically relevant exemplar condition. In parallel, we applied the same methods in healthy young subjects (n = 44; scan–rescan interval ≈4 months) as a frame of reference. Consistency of network architecture was assessed with dice similarity of edges and intraclass correlation coefficient (ICC) of edge‐weights and hub‐scores. Sensitivity to disease effects in patients was assessed by evaluating interindividual variation, changes over time, and differences between those with high and low white matter hyperintensity burden, using correlation analyses and mixed ANOVA. Results Compared to unthresholded networks, both thresholding methods generated more consistent architecture over time in patients (unthresholded: dice = .70; ICC: .70–.78; thresholded: dice = .77; ICC: .73–.83). However, absolute thresholding created fragmented nodes. Similar observations were made in the reference group. Regarding sensitivity to disease effects in patients, fixed‐density thresholds that were optimal in terms of consistency (densities: .10–.30) preserved interindividual variation in global efficiency and node strength as well as the sensitivity to detect effects of time and group. Absolute thresholding produced larger fluctuations of interindividual variation. Conclusions Our results indicate that thresholding of low‐weight connections, particularly when using fixed‐density thresholding, results in more consistent network architecture in patients with longer rescan intervals, while preserving sensitivity to disease effects

Disruption of cerebral networks and cognitive impairment in Alzheimer disease

Objective: To examine the relation between measures of whole-brain white matter connectivity and cognitive performance in patients with early Alzheimer disease (AD) using a network-based approach and to assess whether network parameters provide information that is complementary to conventional MRI markers of AD. Methods: Fifty patients (mean age 78.8 +/- 7.1 years) with early AD were recruited via a memory clinic. In addition, 15 age-, sex-, and education-matched control participants were used as a reference group. All participants underwent a 3-T MRI scan and cognitive assessment. Diffusion tensor imaging-based tractography was used to reconstruct the brain network of each individual, followed by graph theoretical analyses. Overall network efficiency was assessed by measures of local (clustering coefficient, local efficiency) and global (path length, global efficiency) connectivity. Age-, sex-, and education-adjusted cognitive scores were related to network measures and to conventional MRI parameters (i.e., degree of cerebral atrophy and small-vessel disease). Results: The structural brain network of patients showed reduced local efficiency compared to controls. Within the patient group, worse performance in memory and executive functioning was related to decreased local efficiency (r = 0.434; p = 0.002), increased path length (r = -0.538; p < 0.001), and decreased global efficiency (r = 0.431; p = 0.005). Measures of network efficiency explained up to 27% of the variance in cognitive functioning on top of conventional MRI markers (p < 0.01). Conclusion: This study shows that network-based analysis of brain white matter connections provides a novel way to reveal the structural basis of cognitive dysfunction in AD