Dentate nucleus connectivity in adult patients with multiple sclerosis: functional changes at rest and correlation with clinical features

Background and objective: The dentate nucleus, which is the largest of the cerebellar nuclei, plays a critical role in movement and cognition. The aim of our study was to assess any changes in dentate functional connectivity (FC) in adult relapsing remitting multiple sclerosis (RR-MS) patients and to investigate possible clinical correlates. Materials and methods: In all, 54 patients and 24 healthy subjects (HS) underwent multimodal magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI), three-dimensional-T1-weighted and resting state (RS) functional images; they also underwent a cognitive evaluation, that is, attention and information processing speed, by means of the Paced Auditory Serial Addition Test (PASAT). Patients were also scored according to Expanded Disability Status Scale (EDSS). RS-MRI data were analysed using FMRIB Software Library (FSL) tools, with the seed-based method to identify dentate FC. Results: When compared with HS, patients exhibited brain atrophy and widespread DTI abnormalities, as well as greater FC between the dentate nucleus and cortical areas, particularly in the frontal and parietal lobes. Within these areas, FC in patients correlated inversely with clinical impairment. Finally, FC correlated inversely with lesion load and microstructural brain damage. Conclusion: Our findings indicate that dentate FC at rest is altered in MS patients. Whether these functional changes are induced by the disease and play a compensatory role remains to be established

Functional connectivity changes and their relationship with clinical disability and white matter integrity in patients with relapsing-remitting multiple sclerosis

Background and objective: To define the pathological substrate underlying disability in multiple sclerosis by evaluating the relationship of resting-state functional connectivity with microstructural brain damage, as assessed by diffusion tensor maging, and clinical impairments. Methods: Thirty relapsing–remitting patients and 24 controls underwent 3T-MRI; motor abilities were evaluated by using measures of walking speed, hand dexterity and balance capability, while information processing speed was evaluated by a paced auditory serial addiction task. Independent component analysis and tract-based spatial statistics were applied to RS-fMRI and diffusion tensor imaging data using FSL software. Group differences, after dual regression, and clinical correlations were modelled with GeneralLinear-Model and corrected for multiple comparisons. Results: Patients showed decreased functional connectivity in 5 of 11 resting-state-networks (cerebellar, executive-control, medial-visual, basal ganglia and sensorimotor), changes in inter-network correlations and widespread white matter microstructural damage. In multiple sclerosis, corpus callosum microstructural damage positively correlated with functional connectivity in cerebellar and auditory networks. Moreover, functional connectivity within the medial-visual network inversely correlated with information processing speed. White matter widespread microstructural damage inversely correlated with both the paced auditory serial addiction task and hand dexterity. Conclusions: Despite the within-network functional connectivity decrease and the widespread microstructural damage, the inter-network functional connectivity changes suggest a global brain functional rearrangement in multiple sclerosis. The correlation between functional connectivity alterations and callosal damage uncovers a link between functional and structural connectivity. Finally, functional connectivity abnormalities affect information processing speed rather than motor abilities

Abnormal resting-state functional connectivity in progressive supranuclear palsy and corticobasal syndrome

Background: Pathological and MRI-based evidence suggests that multiple brain structures are likely to be involved in functional disconnection between brain areas. Few studies have investigated resting-state functional connectivity (rsFC) in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). In this study, we investigated within- and between-network rsFC abnormalities in these two conditions. Methods: Twenty patients with PSP, 11 patients with CBS, and 16 healthy subjects (HS) underwent a resting-state fMRI study. Resting-state networks (RSNs) were extracted to evaluate within- and between-network rsFC using the Melodic and FSLNets software packages. results: Increased within-network rsFC was observed in both PSP and CBS patients, with a larger number of RSNs being involved in CBS. Within-network cerebellar rsFC positively correlated with mini-mental state examination scores in patients with PSP. Compared to healthy volunteers, PSP and CBS patients exhibit reduced functional connectivity between the lateral visual and auditory RSNs, with PSP patients additionally showing lower functional connectivity between the cerebellar and insular RSNs. Moreover, rsFC between the salience and executive-control RSNs was increased in patients with CBS compared to HS. conclusion: This study provides evidence of functional brain reorganization in both PSP and CBS. Increased within-network rsFC could represent a higher degree of synchronization in damaged brain areas, while between-network rsFC abnormalities may mainly reflect degeneration of long-range white matter fibers

Effect of prenap coffee on daytime sleepiness in university students

Background: Daytime sleepiness impairs academic performance in college students. Napping is a counter to daytime sleepiness, but often causes sleep inertia on waking up. Caffeine absorption from beverages peaks 30 minutes after their ingestion presenting a window of opportunity to have a short nap such that the time of waking up is in synchrony with onset of action of caffeine; thereby abolishing post-nap inertia and achieving synergistic mitigation of fatigue.Objective of this study to assess effect of nap, coffee, ‘coffee and nap’ and ‘wakeful break without coffee’ on daytime sleepiness using Psychomotor Vigilance Tests (PVTs) and Karolinska Sleepiness Scale (KSS) score.Methods: After Institutional Review Board clearance, 10 subjects (aged 19-21 years) were selected using their Epworth Sleepiness Scale score (ESS >5) and called to the study site 8 times on different days to be exposed to these four conditions twice - only coffee (standardized), only nap (30min), coffee immediately followed by 30min nap, wakeful break (30min) without coffee or nap. Pre and post scores were recorded for electronic PVT (Reaction Time and Motor Responsiveness) and KSS for each attempt.Results: Test outcome was associated with intervention used (p=0.00001). ‘Nap only’ group was associated with deterioration in outcomes (p=0.00001), accounting for highest percentage (41%) of all deteriorated test outcomes. ‘Coffee only’ group was associated with improvement in test scores (p=0.00001), responsible for highest share (38.8%) of all improved test outcomes. ‘Nap only’ and ‘Coffee-nap’ group showed improvement in 11.67% and 21.67% of outcomes respectively. Conclusions: Pre-nap coffee is a proactive counter-measure to post nap sleep inertia

Corticobasal syndrome: neuroimaging and neurophysiological advances

Corticobasal degeneration (CBD) is a neurodegenerative condition characterized by 4R-tau protein deposition in several brain regions that clinically manifests itself as a heterogeneous atypical parkinsonism typically expressing in the adulthood. The prototypical clinical phenotype of CBD is corticobasal syndrome (CBS). Important insights into the pathophysiological mechanisms underlying motor and higher cortical symptoms in CBS have been gained by using advanced neuroimaging and neurophysiological techniques. Structural and functional neuroimaging studies often showed asymmetric cortical and subcortical abnormalities, mainly involving perirolandic and parietal regions and basal ganglia structures. Neurophysiological investigations including electroencephalography and somatosensory evoked potentials provided useful information on the origin of myoclonus and on cortical sensory loss. Transcranial magnetic stimulation demonstrated heterogeneous and asymmetric changes in the excitability and plasticity of primary motor cortex and abnormal hemispheric connectivity. Neuroimaging and neurophysiological abnormalities in multiple brain areas reflect the asymmetric neurodegeneration, leading to the asymmetric motor and higher cortical symptoms in CBS. This article is protected by copyright. All rights reserved

Temporal dynamics of the default mode network characterise meditation induced alterations in consciousness

Current research suggests that human consciousness is associated with complex, synchronous interactions between multiple cortical networks. In particular, the default mode network (DMN) of the resting brain is thought to be altered by changes in consciousness, including the meditative state. However, it remains unclear how meditation alters the fast and ever-changing dynamics of brain activity within this network. Here we addressed this question using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to compare the spatial extents and temporal dynamics of the DMN during rest and meditation. Using fMRI, we identified key reductions in the posterior cingulate hub of the DMN, along with increases in right frontal and left temporal areas, in experienced meditators during rest and during meditation, in comparison to healthy controls (HCs). We employed the simultaneously recorded EEG data to identify the topographical microstate corresponding to activation of the DMN. Analysis of the temporal dynamics of this microstate revealed that the average duration and frequency of occurrence of DMN microstate was higher in meditators compared to HCs. Both these temporal parameters increased during meditation, reflecting the state effect of meditation. In particular, we found that the alteration in the duration of the DMN microstate when meditators entered the meditative state correlated negatively with their years of meditation experience. This reflected a trait effect of meditation, highlighting its role in producing durable changes in temporal dynamics of the DMN. Taken together, these findings shed new light on short and long-term consequences of meditation practice on this key brain network

Impact of COVID-19 on Healthcare Labor Market in the United States: Lower Paid Workers Experienced Higher Vulnerability and Slower Recovery

The resilience of the healthcare industry, often considered recession-proof, is being tested by the COVID-19 induced reductions in physical mobility and restrictions on elective and non-emergent medical procedures. We assess early COVID-19 effects on the dynamics of decline and recovery in healthcare labor markets in the United States. Descriptive analyses with monthly cross-sectional data on unemployment rates, employment, labor market entry/exit, and weekly work hours among healthcare workers in each healthcare industry and occupation, using the Current Population Survey from July 2019−2020 were performed. We found that unemployment rates increased dramatically for all healthcare industries, with the strongest early impacts on dentists’ offices (41.3%), outpatient centers (10.5%), physician offices (9.5%), and home health (7.8%). Lower paid workers such as technologists/technicians (10.5%) and healthcare aides (12.6%) were hit hardest and faced persistently high unemployment, while nurses (4%), physicians/surgeons (1.4%), and pharmacists (0.7%) were spared major disruptions. Unique economic vulnerabilities faced by low-income healthcare workers may need to be addressed to avoid serious disruptions from future events similar to COVID-19

Cardiovascular diseases prediction by machine learning incorporation with deep learning

It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures

Mapping development and health effects of cooking with solid fuels in low-income and middle-income countries, 2000-18 : a geospatial modelling study

Background More than 3 billion people do not have access to clean energy and primarily use solid fuels to cook. Use of solid fuels generates household air pollution, which was associated with more than 2 million deaths in 2019. Although local patterns in cooking vary systematically, subnational trends in use of solid fuels have yet to be comprehensively analysed. We estimated the prevalence of solid-fuel use with high spatial resolution to explore subnational inequalities, assess local progress, and assess the effects on health in low-income and middle-income countries (LMICs) without universal access to clean fuels.Methods We did a geospatial modelling study to map the prevalence of solid-fuel use for cooking at a 5 km x 5 km resolution in 98 LMICs based on 2.1 million household observations of the primary cooking fuel used from 663 population-based household surveys over the years 2000 to 2018. We use observed temporal patterns to forecast household air pollution in 2030 and to assess the probability of attaining the Sustainable Development Goal (SDG) target indicator for clean cooking. We aligned our estimates of household air pollution to geospatial estimates of ambient air pollution to establish the risk transition occurring in LMICs. Finally, we quantified the effect of residual primary solid-fuel use for cooking on child health by doing a counterfactual risk assessment to estimate the proportion of deaths from lower respiratory tract infections in children younger than 5 years that could be associated with household air pollution.Findings Although primary reliance on solid-fuel use for cooking has declined globally, it remains widespread. 593 million people live in districts where the prevalence of solid-fuel use for cooking exceeds 95%. 66% of people in LMICs live in districts that are not on track to meet the SDG target for universal access to clean energy by 2030. Household air pollution continues to be a major contributor to particulate exposure in LMICs, and rising ambient air pollution is undermining potential gains from reductions in the prevalence of solid-fuel use for cooking in many countries. We estimated that, in 2018, 205000 (95% uncertainty interval 147000-257000) children younger than 5 years died from lower respiratory tract infections that could be attributed to household air pollution.Interpretation Efforts to accelerate the adoption of clean cooking fuels need to be substantially increased and recalibrated to account for subnational inequalities, because there are substantial opportunities to improve air quality and avert child mortality associated with household air pollution. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.Peer reviewe