Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.

Myeloperoxidase-ANCA IgG induces different forms of small vessel vasculitis based on type of synergistic immune stimuli.

Anti-neutrophil cytoplasmic autoantibody (ANCA) vasculitis has diverse patterns of injury including microscopic polyangiitis (MPA), granulomatosis with polyangiitis (GPA), and eosinophilic granulomatosis with polyangiitis (EGPA). Necrotizing and crescentic glomerulonephritis (NCGN) occurs in all syndromes and as renal limited vasculitis (RLV). Single-dose intravenous ANCA IgG-specific for mouse myeloperoxidase (MPO) causes RLV in mice. Although multiple mouse models have elucidated ANCA-IgG induced necrotizing and crescentic glomerulonephritis (NCGN), pathogenesis of ANCA-induced granulomatosis and vasculitis outside the kidney has not been clarified. To investigate this, we used intravenous MPO-ANCA IgG in the same strain of mice to induce different patterns of lung disease mirroring patients with granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA). Repeated intravenous MPO-ANCA IgG induced GPA with NCGN, lung capillaritis, arteritis and granulomatosis. Lung leukocyte phenotypes were evaluated by immunohistochemical image analysis and by flow cytometry. ANCA lung capillaritis and microabscesses began within one day and evolved into granulomas in under seven days. Influenza plus single-dose MPO-ANCA IgG induced MPA with NCGN, lung capillaritis and arteritis, but no granulomatosis. Allergic airway disease caused by house dust mites or ovalbumin plus single-dose intravenous MPO-ANCA IgG induced EGPA with eosinophilic bronchiolitis, NCGN, capillaritis, arteritis, and granulomatosis. Thus, our study shows that the occurrence and pattern of lung lesions are determined by the same ANCA IgG accompanied by different synergistic immune factors.

Brain clocks capture diversity and disparity in aging and dementia.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer's disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R2=0.37, F2=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer's disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer's disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.

Retaining Interest in Child and Adolescent Psychiatry Among General Residency Trainees.

OBJECTIVES: This study aimed to identify factors affecting current general psychiatry residents' interest in child and adolescent psychiatry (CAP) at Lehigh Valley Health Network (LVHN). Furthermore, it aimed to identify areas for improvement in clinical education to address the shortage of child psychiatrists at the institution at the time of this study. METHODS: An electronic anonymous pre-implementation survey was sent to all the current general psychiatry residents at LVHN. It assessed the most important factors for trainees in deciding their career paths into CAP, their comfort level with children and families, and overall CAP and related systems-based knowledge. Interventions based on the survey results were implemented in the LVHN psychiatry residency program. The residents then completed a post-intervention survey to assess the impact of these interventions on their perspectives toward CAP. RESULTS: CAP rotation experience and work with families were strong influencers for general psychiatry residents at LVHN in pursing CAP. Systems-based knowledge was particularly lacking compared to overall CAP knowledge. Educational interventions that were implemented at LVHN led to improvements in residents' sense of competence working with children and families with no net loss of interest in CAP. CONCLUSIONS: Educational modifications enhanced attitudes toward CAP among LVHN general psychiatry residents. Implementing such modifications at other residency programs may be likewise effective in retaining interest in CAP among their general psychiatry residents.

Neutrophils sing "IL[-10] be seeing you" in the lungs during pneumonia.

Mechanisms of regulating the beneficial and harmful capabilities of neutrophils include IL-10/IL-10RA signaling in neutrophils that limits clearance of Streptococcus pneumoniae and accumulation of neutrophils in pneumonic lung tissue.

Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study.

Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands (δ and θ) and high-frequency bands (α and ß) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically-validated frequency bands, and feature selection. Following, we assess the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively.Clinical relevance- We present an end-to-end pipeline to extract clinically relevant features from rs-EEG recordings that can facilitate the analysis and detection of PD. Further, we provide an ML system that shows a good performance in detecting PD, even in the presence of centers with different acquisition protocols. Our results show the relevance of harmonizing features and provide a good starting point for future studies focusing on rs-EEG analysis and multi-center data.

High quality implementation of 4Rs + MTP increases classroom emotional support and reduces absenteeism.

School-based social and emotional learning (SEL) programs are associated with improvements in children's SEL and academic outcomes, and the quality of classroom interactions. The magnitude of these effects increases at high levels of program implementation quality. This study aimed to (1) identify teachers' profiles of quality of implementation, (2) explore teachers and classroom characteristics contributing to their propensity to comply with high quality of implementation, and (3) examine the relations between school assignment to an SEL program, quality of classroom interactions, and child SEL and academic outcomes at different levels of teachers' compliance propensity. This study drew upon data from a cluster-randomized controlled trial evaluating the efficacy of 4Rs + MTP, a literacy-based SEL program, on third and fourth grade teachers (n = 330) and their students (n = 5,081) across 60 New York City public elementary schools. Latent profile analysis indicated that measures of teacher responsiveness and amount of exposure to implementation supports contributed to the differentiation of profiles of high and low quality of implementation. Random forest analysis showed that more experienced teachers with low levels of professional burnout had high propensity to comply with high quality of implementation. Multilevel moderated mediation analysis indicated that 4Rs + MTP teachers with high compliance propensity were associated with higher classroom emotional support and lower children's school absences than their counterparts in the control group. These findings may inform debates in policy research about the importance of providing the supports teachers need to implement SEL school programs with high quality.

Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis.

OBJECTIVE: This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach. METHODS: We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature. RESULTS: For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs. CONCLUSIONS: Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD. SIGNIFICANCE: Rs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG.

Delirium in oncological palliative care and clinical cardiology units: A comparative analysis.

OBJECTIVE: The aim of this study was to compare the sociodemographic and clinical characteristics of delirium in patients treated in a clinical cardiology unit (CCU) and an oncological palliative care unit (OPCU) at a high-complexity institution. CONTEXT: Delirium is a neuropsychiatric syndrome with multicausal etiology, associated with increased morbidity and mortality. METHOD: This was a cross-sectional, analytical observational study. CCU and OPCU patients were evaluated for 480 days. The diagnosis was made according to DSM-V. Sociodemographic characteristics, the Karnofsky index, and the Charlson index were evaluated. Possible etiologies were verified. Severity was assessed with the Delirium Severity Scale (DRS-R98). RESULTS: A total of 1,986 patients were evaluated, 205 were eligible, and 110 were included in the study (CCU: 61, OPCU: 49). Delirium prevalence was 11.35% in the CCU and 9.87% in the OPCU. CCU patients were 12 years older (p < 0.03) and a history of dementia (41 vs. 8.2%; p < 0.001). Organ failure was the most frequent etiology of delirium in the CCU (41.0%), and in the OPCU, the etiologies were neoplasms (28.6%), side effect of medication (22.4%), and infections (2.5%). Differences were found in the clinical characteristics of delirium evaluated by DRS-R98, with the condition being more severe and with a higher frequency of psychotic symptoms in OPCU patients. CONCLUSION: Delirium was a common condition in hospitalized patients in the CCU and the OPCU. The clinical characteristics were similar in both groups; however, significant differences were found in OPCU patients in terms of age, personal history of dementia, and opioid use, as well as the severity of delirium and a greater association with psychotic symptoms. These findings have implications for the early implementation of diagnostic and therapeutic strategies.

Management of hospitalized patients with mild to moderate diabetic ketoacidosis using a continuous insulin infusion protocol on a medical surgical ward and observation level of care: A retrospective cohort study.

Although the practice of using rapid-acting subcutaneous insulin for the management of mild-to-moderate diabetic ketoacidosis is becoming increasingly popular, the continuous insulin infusion remains widely utilized, and its real-world applicability and safety on a medical surgical unit (Med Surg) and observation level of care are unclear. We assessed whether a continuous insulin infusion protocol for mild-to-moderate diabetic ketoacidosis on Med Surg/observation level of care over a 6.5-year period was associated with adverse outcomes. A retrospective cohort study of adults hospitalized with mild-to-moderate diabetic ketoacidosis was conducted at 2 community hospitals in Northern California, USA, from January 2014 to May 2020. Demographic and clinical variables were collected using an electronic health record. Admission to Med Surg/observation was compared to intensive care unit admission for the outcomes of 30-day readmission, presence of hypoglycemia, rate of hypoglycemic episodes, in-hospital and 30-day mortality, and length of stay using bivariate analysis. Among 227 hospital encounters (mean age 41 years, 52.9% women, 79.3% type 1 diabetes, 97.4% utilization of continuous insulin infusion), 19.4% were readmitted within 30 days, and 20.7% developed hypoglycemia. For Med Surg/observation encounters compared to the intensive care unit, there were no statistically significant differences in the risk of readmission (RR 1.48, 95% CI, 0.86-2.52), hypoglycemia (RR 1.17, 95% CI, 0.70-1.95), or increased length of stay (RR 0.71, 95% CI, 0.55-1.02); there was a lower risk of hypoglycemic events during hospitalization (RR 0.69, 95% CI, 0.54-0.96). Continuous insulin infusion utilization may be a safe option for treatment of mild-to-moderate diabetic ketoacidosis on Med Surg/observation level of care. Further investigation is needed.

Harmonized-Multinational qEEG norms (HarMNqEEG).

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

The Brain Dynamics of Syllable Duration and Semantic Predictability in Spanish.

This study examines the neural dynamics underlying the prosodic (duration) and the semantic dimensions in Spanish sentence perception. Specifically, we investigated whether adult listeners are aware of changes in the duration of a pretonic syllable of words that were either semantically predictable or unpredictable from the preceding sentential context. Participants listened to the sentences with instructions to make prosodic or semantic judgments, while their EEG was recorded. For both accuracy and RTs, the results revealed an interaction between duration and semantics. ERP analysis exposed an interactive effect between task, duration and semantic, showing that both processes share neural resources. There was an enhanced negativity on semantic process (N400) and an extended positivity associated with anomalous duration. Source estimation for the N400 component revealed activations in the frontal gyrus for the semantic contrast and in the parietal postcentral gyrus for duration contrast in the metric task, while activation in the sub-lobar insula was observed for the semantic task. The source of the late positive components was located on posterior cingulate. Hence, the ERP data support the idea that semantic and prosodic levels are processed by similar neural networks, and the two linguistic dimensions influence each other during the decision-making stage in the metric and semantic judgment tasks.

Automatic Classification of Subjects of the PSEN1-E280A Family at Risk of Developing Alzheimer's Disease Using Machine Learning and Resting State Electroencephalography.

BACKGROUND: The study of genetic variant carriers provides an opportunity to identify neurophysiological changes in preclinical stages. Electroencephalography (EEG) is a low-cost and minimally invasive technique which, together with machine learning, provide the possibility to construct systems that classify subjects that might develop Alzheimer's disease (AD). OBJECTIVE: The aim of this paper is to evaluate the capacity of the machine learning techniques to classify healthy Non-Carriers (NonCr) from Asymptomatic Carriers (ACr) of PSEN1-E280A variant for autosomal dominant Alzheimer's disease (ADAD), using spectral features from EEG channels and brain-related independent components (ICs) obtained using independent component analysis (ICA). METHODS: EEG was recorded in 27 ACr and 33 NonCr. Statistical significance analysis was applied to spectral information from channels and group ICA (gICA), standardized low-resolution tomography (sLORETA) analysis was applied over the IC as well. Strategies for feature selection and classification like Chi-square, mutual informationm and support vector machines (SVM) were evaluated over the dataset. RESULTS: A test accuracy up to 83% was obtained by implementing a SVM with spectral features derived from gICA. The main findings are related to theta and beta rhythms, generated in the parietal and occipital regions, like the precuneus and superior parietal lobule. CONCLUSION: Promising models for classification of preclinical AD due to PSEN-1-E280A variant can be trained using spectral features, and the importance of the beta band and precuneus region is highlighted in asymptomatic stages, opening up the possibility of its use as a screening methodology.

TBK1 Is Required for Host Defense Functions Distinct from Type I IFN Expression and Myeloid Cell Recruitment in Murine Streptococcus pneumoniae Pneumonia.

Bacterial pneumonia induces the rapid recruitment and activation of neutrophils and macrophages into the lung, and these cells contribute to bacterial clearance and other defense functions. TBK1 (TANK-binding kinase 1) performs many functions, including activation of the type I IFN pathway and regulation of autophagy and mitophagy, but its contribution to antibacterial defenses in the lung is unclear. We previously showed that lung neutrophils upregulate mRNAs for TBK1 and its accessory proteins during Streptococcus pneumoniae pneumonia, despite low or absent expression of type I IFN in these cells. We hypothesized that TBK1 performs key antibacterial functions in pneumonia apart from type I IFN expression. Using TBK1 null mice, we show that TBK1 contributes to antibacterial defenses and promotes bacterial clearance and survival. TBK1 null mice express lower concentrations of many cytokines in the infected lung. Conditional deletion of TBK1 with LysMCre results in TBK1 deletion from macrophages but not neutrophils. LysMCre TBK1 mice have no defect in cytokine expression, implicating a nonmacrophage cell type as a key TBK1-dependent cell. TBK1 null neutrophils have no defect in recruitment to the infected lung but show impaired activation of p65/NF-κB and STAT1 and lower expression of reactive oxygen species, IFNγ, and IL12p40. TLR1/2 and 4 agonists each induce phosphorylation of TBK1 in neutrophils. Surprisingly, neutrophil TBK1 activation in vivo does not require the adaptor STING. Thus, TBK1 is a critical component of STING-independent antibacterial responses in the lung, and TBK1 is necessary for multiple neutrophil functions.

Volume Reduction Techniques for the Classification of Independent Components of rs-fMRI Data: a Study with Convolutional Neural Networks.

In the last decade, neurosciences have had an increasing interest in resting state functional magnetic resonance imaging (rs-fMRI) as a result of its advantages, such as high spatial resolution, compared to other brain exploration techniques. To improve the technique, the elimination of artifacts through Independent Components Analysis (ICA) has been proposed, as this can separate neural signal and noise, opening possibilities for automatic classification. The main classification techniques have focused on processes based on typical machine learning. However, there are currently more robust approaches such as convolutional neural networks, which can deal with complex problems directly from the data without feature selection and even with data that does not have a simple interpretation, being limited by the amount of data necessary for training and its high computational cost. This research focused on studying four methods of volume reduction mitigating the computational cost for the training of 3 models based on convolutional neural networks. One of the reduction techniques is a novel approach that we call Reduction by Consecutive Binary Patterns (RCBP), which was shown to preserve the spatial features of the independent components. In addition, the RCBP showed networks in components associated with neuronal activity more clearly. The networks achieved accuracy above 98 % in classification, and one network was even found to be over 99 % accurate, outperforming most machine learning-based classification algorithms.

Neutrophil DREAM come true: The not-so-impossible quest for mechanisms of neutrophil function and heterogeneity.

Neutrophil functions and responses are heterogeneous, and the nature and categorization of this heterogeneity is achieving considerable interest. Work by Li et al. in this issue of JEM (2021. J. Exp. Med.https://doi.org/10.1084/jem.20211083) identifies how a transcriptional repressor, DREAM, regulates adhesion of neutrophils to endothelial cells and their transmigration into tissue. This study offers a mechanism for heterogeneity in this critical response of neutrophils to inflammatory stimuli.

A State-of-the-Art Survey of Tasks for Tree Design and Evaluation With a Curated Task Dataset.

In the field of information visualization, the concept of "tasks" is an essential component of theories and methodologies for how a visualization researcher or a practitioner understands what tasks a user needs to perform and how to approach the creation of a new design. In this article, we focus on the collection of tasks for tree visualizations, a common visual encoding in many domains ranging from biology to computer science to geography. In spite of their commonality, no prior efforts exist to collect and abstractly define tree visualization tasks. We present a literature review of tree visualization articles and generate a curated dataset of over 200 tasks. To enable effective task abstraction for trees, we also contribute a novel extension of the Multi-Level Task Typology to include more specificity to support tree-specific tasks as well as a systematic procedure to conduct task abstractions for tree visualizations. All tasks in the dataset were abstracted with the novel typology extension and analyzed to gain a better understanding of the state of tree visualizations. These abstracted tasks can benefit visualization researchers and practitioners as they design evaluation studies or compare their analytical tasks with ones previously studied in the literature to make informed decisions about their design. We also reflect on our novel methodology and advocate more broadly for the creation of task-based knowledge repositories for different types of visualizations. The Supplemental Material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TVCG.2021.3064037, will be maintained on OSF: https://osf.io/u5ehs/.