Lung Ultrasound Abnormalities in Patients Without Pulmonary Pathology Prior to Surgery.

Background: The occurrence of lung ultrasound abnormalities in patients without lung disease remains uncertain, while patients with respiratory disease often exhibit such abnormalities. Objectives: The primary aim was to identify pathological ultrasonographic pulmonary findings and their correlation with baseline diseases and static lung compliance in patients without any pre-existing respiratory conditions. Methods: This prospective observational study enrolled a series of surgical patients with no history of pulmonary pathology (n = 104). Baseline diseases and patients' physical status classification, based on the American Society of Anesthesiologists (ASA), were documented by reviewing medical records. Prior to surgery, a lung ultrasound was performed to assess pulmonary changes. During surgery with general anesthesia, static lung compliance was measured. The Spearman correlation coefficient was employed to determine the correlation between the two variables. Results: Twenty-four patients (23.07%) exhibited 1 - 2 B-lines in certain lung fields. Seven patients (6.7%) had an ultrasound B-line score > 0 (indicating ≥ 3 B-lines). Among these patients, the average number of lung fields with ≥ 3 B-lines was 3.71 ± 2.43. Patients with systemic diseases (ASA ≥ 2) displayed a higher number of B-lines compared to ASA I patients (P-value = 0.039). Pleural irregularities were found in 10 patients (9.6%), while atelectasis and pleural effusion were observed in five (4.8%) and four (3.8%) patients, respectively. The mean lung compliance value was 56.78 ± 15.33. No correlation was observed between the total score of the B-lines and lung compliance (Spearman's correlation: rho = -0.028, P-value = 0.812). Conclusions: Patients without pulmonary pathology may exhibit ultrasound pulmonary abnormalities, which tend to increase with higher ASA scores and do not appear to have a correlation with static lung compliance.

Liraglutide restores impaired associative learning in individuals with obesity.

Survival under selective pressure is driven by the ability of our brain to use sensory information to our advantage to control physiological needs. To that end, neural circuits receive and integrate external environmental cues and internal metabolic signals to form learned sensory associations, consequently motivating and adapting our behaviour. The dopaminergic midbrain plays a crucial role in learning adaptive behaviour and is particularly sensitive to peripheral metabolic signals, including intestinal peptides, such as glucagon-like peptide 1 (GLP-1). In a single-blinded, randomized, controlled, crossover basic human functional magnetic resonance imaging study relying on a computational model of the adaptive learning process underlying behavioural responses, we show that adaptive learning is reduced when metabolic sensing is impaired in obesity, as indexed by reduced insulin sensitivity (participants: N = 30 with normal insulin sensitivity; N = 24 with impaired insulin sensitivity). Treatment with the GLP-1 receptor agonist liraglutide normalizes impaired learning of sensory associations in men and women with obesity. Collectively, our findings reveal that GLP-1 receptor activation modulates associative learning in people with obesity via its central effects within the mesoaccumbens pathway. These findings provide evidence for how metabolic signals can act as neuromodulators to adapt our behaviour to our body's internal state and how GLP-1 receptor agonists work in clinics.

Habitual daily intake of a sweet and fatty snack modulates reward processing in humans.

Western diets rich in fat and sugar promote excess calorie intake and weight gain; however, the underlying mechanisms are unclear. Despite a well-documented association between obesity and altered brain dopamine function, it remains elusive whether these alterations are (1) pre-existing, increasing the individual susceptibility to weight gain, (2) secondary to obesity, or (3) directly attributable to repeated exposure to western diet. To close this gap, we performed a randomized, controlled study (NCT05574660) with normal-weight participants exposed to a high-fat/high-sugar snack or a low-fat/low-sugar snack for 8 weeks in addition to their regular diet. The high-fat/high-sugar intervention decreased the preference for low-fat food while increasing brain response to food and associative learning independent of food cues or reward. These alterations were independent of changes in body weight and metabolic parameters, indicating a direct effect of high-fat, high-sugar foods on neurobehavioral adaptations that may increase the risk for overeating and weight gain.

Monitoring PHB production in Synechocystis sp. with hyperspectral images.

Microalgae wastewater treatment systems have the potential for producing added-value products. More specifically, cyanobacteria are able to accumulate polyhydroxybutyrates (PHBs), which can be extracted and used for bioplastics production. Nonetheless, PHB production requires proper culture conditions and continue monitoring, challenging the state-of-the-art technologies. The aim of this study was to investigate the application of hyperspectral technologies to monitor cyanobacteria population growth and PHB production. We have established a ground-breaking measurement method able to discern spectral reflectance changes from light emitted to cyanobacteria in different phases. All in all, enabling to distinguish between cyanobacteria growth phase and PHB accumulation phase. Furthermore, first tests of classification algorithms used for machine learning and image recognition technologies had been applied to automatically recognize the different cyanobacteria species from a complex microbial community containing cyanobacteria and microalgae cultivated in pilot-scale photobioreactors (PBRs). We have defined three main indicators for monitoring PHB production: (i) cyanobacteria specific-strain density, (ii) differentiate between growth and PHB-accumulation and (iii) chlorosis progression. The results presented in this study represent an interesting alternative for traditional measurements in cyanobacteria PHB production and its application in pilot-scale PBRs. Although not directly determining the amount of PHB production, they would give insights on the undergoing processes.

Auditory mismatch responses are differentially sensitive to changes in muscarinic acetylcholine versus dopamine receptor function.

The auditory mismatch negativity (MMN) has been proposed as a biomarker of NMDA receptor (NMDAR) dysfunction in schizophrenia. Such dysfunction may be caused by aberrant interactions of different neuromodulators with NMDARs, which could explain clinical heterogeneity among patients. In two studies (N = 81 each), we used a double-blind placebo-controlled between-subject design to systematically test whether auditory mismatch responses under varying levels of environmental stability are sensitive to diminishing and enhancing cholinergic vs. dopaminergic function. We found a significant drug × mismatch interaction: while the muscarinic acetylcholine receptor antagonist biperiden delayed and topographically shifted mismatch responses, particularly during high stability, this effect could not be detected for amisulpride, a dopamine D2/D3 receptor antagonist. Neither galantamine nor levodopa, which elevate acetylcholine and dopamine levels, respectively, exerted significant effects on MMN. This differential MMN sensitivity to muscarinic versus dopaminergic receptor function may prove useful for developing tests that predict individual treatment responses in schizophrenia.

Interoception of breathing and its relationship with anxiety.

Interoception, the perception of internal bodily states, is thought to be inextricably linked to affective qualities such as anxiety. Although interoception spans sensory to metacognitive processing, it is not clear whether anxiety is differentially related to these processing levels. Here we investigated this question in the domain of breathing, using computational modeling and high-field (7 T) fMRI to assess brain activity relating to dynamic changes in inspiratory resistance of varying predictability. Notably, the anterior insula was associated with both breathing-related prediction certainty and prediction errors, suggesting an important role in representing and updating models of the body. Individuals with low versus moderate anxiety traits showed differential anterior insula activity for prediction certainty. Multi-modal analyses of data from fMRI, computational assessments of breathing-related metacognition, and questionnaires demonstrated that anxiety-interoception links span all levels from perceptual sensitivity to metacognition, with strong effects seen at higher levels of interoceptive processes.

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry.

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

A Hilbert-based method for processing respiratory timeseries.

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation is publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (https://translationalneuromodeling.org/tapas).

Cholinergic and dopaminergic effects on prediction error and uncertainty responses during sensory associative learning.

Navigating the physical world requires learning probabilistic associations between sensory events and their change in time (volatility). Bayesian accounts of this learning process rest on hierarchical prediction errors (PEs) that are weighted by estimates of uncertainty (or its inverse, precision). In a previous fMRI study we found that low-level precision-weighted PEs about visual outcomes (that update beliefs about associations) activated the putative dopaminergic midbrain; by contrast, precision-weighted PEs about cue-outcome associations (that update beliefs about volatility) activated the cholinergic basal forebrain. These findings suggested selective dopaminergic and cholinergic influences on precision-weighted PEs at different hierarchical levels. Here, we tested this hypothesis, repeating our fMRI study under pharmacological manipulations in healthy participants. Specifically, we performed two pharmacological fMRI studies with a between-subject double-blind placebo-controlled design: study 1 used antagonists of dopaminergic (amisulpride) and muscarinic (biperiden) receptors, study 2 used enhancing drugs of dopaminergic (levodopa) and cholinergic (galantamine) modulation. Pooled across all pharmacological conditions of study 1 and study 2, respectively, we found that low-level precision-weighted PEs activated the midbrain and high-level precision-weighted PEs the basal forebrain as in our previous study. However, we found pharmacological effects on brain activity associated with these computational quantities only when splitting the precision-weighted PEs into their PE and precision components: in a brainstem region putatively containing cholinergic (pedunculopontine and laterodorsal tegmental) nuclei, biperiden (compared to placebo) enhanced low-level PE responses and attenuated high-level PE activity, while amisulpride reduced high-level PE responses. Additionally, in the putative dopaminergic midbrain, galantamine compared to placebo enhanced low-level PE responses (in a body-weight dependent manner) and amisulpride enhanced high-level precision activity. Task behaviour was not affected by any of the drugs. These results do not support our hypothesis of a clear-cut dichotomy between different hierarchical inference levels and neurotransmitter systems, but suggest a more complex interaction between these neuromodulatory systems and hierarchical Bayesian quantities. However, our present results may have been affected by confounds inherent to pharmacological fMRI. We discuss these confounds and outline improved experimental tests for the future.

A Computational Theory of Mindfulness Based Cognitive Therapy from the "Bayesian Brain" Perspective.

Mindfulness Based Cognitive Therapy (MBCT) was developed to combine methods from cognitive behavioral therapy and meditative techniques, with the specific goal of preventing relapse in recurrent depression. While supported by empirical evidence from multiple clinical trials, the cognitive mechanisms behind the effectiveness of MBCT are not well understood in computational (information processing) or biological terms. This article introduces a testable theory about the computational mechanisms behind MBCT that is grounded in "Bayesian brain" concepts of perception from cognitive neuroscience, such as predictive coding. These concepts regard the brain as embodying a model of its environment (including the external world and the body) which predicts future sensory inputs and is updated by prediction errors, depending on how precise these error signals are. This article offers a concrete proposal how core concepts of MBCT-(i) the being mode (accepting whatever sensations arise, without judging or changing them), (ii) decentering (experiencing thoughts and percepts simply as events in the mind that arise and pass), and (iii) cognitive reactivity (changes in mood reactivate negative beliefs)-could be understood in terms of perceptual and metacognitive processes that draw on specific computational mechanisms of the "Bayesian brain." Importantly, the proposed theory can be tested experimentally, using a combination of behavioral paradigms, computational modelling, and neuroimaging. The novel theoretical perspective on MBCT described in this paper may offer opportunities for finessing the conceptual and practical aspects of MBCT.

Effects of hunger, satiety and oral glucose on effective connectivity between hypothalamus and insular cortex.

The hypothalamus and insular cortex play an essential role in the integration of endocrine and homeostatic signals and their impact on food intake. Resting-state functional connectivity alterations of the hypothalamus, posterior insula (PINS) and anterior insula (AINS) are modulated by metabolic states and caloric intake. Nevertheless, a deeper understanding of how these factors affect the strength of connectivity between hypothalamus, PINS and AINS is missing. This study investigated whether effective (directed) connectivity within this network varies as a function of prandial states (hunger vs. satiety) and energy availability (glucose levels and/or hormonal modulation). To address this question, we measured twenty healthy male participants of normal weight twice: once after 36 â€‹h of fasting (except water consumption) and once under satiated conditions. During each session, resting-state functional MRI (rs-fMRI) and hormone concentrations were recorded before and after glucose administration. Spectral dynamic causal modeling (spDCM) was used to assess the effective connectivity between the hypothalamus and anterior and posterior insula. Using Bayesian model selection, we observed that the same model was identified as the most likely model for each rs-fMRI recording. Compared to satiety, the hunger condition enhanced the strength of the forward connections from PINS to AINS and reduced the strength of backward connections from AINS to PINS. Furthermore, the strength of connectivity from PINS to AINS was positively related to plasma cortisol levels in the hunger condition, mainly before glucose administration. However, there was no direct relationship between glucose treatment and effective connectivity. Our findings suggest that prandial states modulate connectivity between PINS and AINS and relate to theories of interoception and homeostatic regulation that invoke hierarchical relations between posterior and anterior insula.

Modulation of midbrain neurocircuitry by intranasal insulin.

Insulin modulates dopamine neuron activity in midbrain and affects processes underlying food intake behaviour, including impulsivity and reward processing. Here, we used intranasal administration and task-free functional MRI in humans to assess time- and dose-dependent effects of insulin on functional connectivity of the dopaminergic midbrain - and how these effects varied depending on systemic insulin sensitivity as measured by HOMA-IR. Specifically, we used a repeated-measures design with factors dose (placebo, 40 IU, 100 IU, 160 IU), time (7 time points during a 90 min post-intervention interval), and group (low vs. high HOMA-IR). A factorial analysis identified a three-way interaction (with whole-brain significance) with regard to functional connectivity between midbrain and the ventromedial prefrontal cortex. This interaction demonstrates that systemic insulin sensitivity modulates the temporal course and dose-dependent effects of intranasal insulin on midbrain functional connectivity. It suggests that altered insulin sensitivity may impact on dopaminergic projections of the midbrain and might underlie the dysregulation of reward-related and motivational behaviour in obesity and diabetes. Perhaps most importantly, the time courses of midbrain functional connectivity we present may provide useful guidance for the design of future human studies that utilize intranasal insulin administration.

Revisión de la literatura sobre efectos nocivos de la exposición laboral a hidrocarburos en trabajadores en ambiente externo / Literature Review on the Harmful Effects of Occupational Exposure to Hydrocarbons on Workers in External Environments

ANTECEDENTES: Policías de tráfico, conductores y otros profesionales, están expuestos de forma aguda y crónica a hidrocarburos ambientales del tráfico que pueden conllevar un riesgo para la salud. Dichos tóxicos, están presentes en la contaminación ambiental. En la literatura revisada no hemos encontrado protocolos ni EPIs para estas profesiones laborales, poniendo de relieve que aún queda mucho por desarrollar en este campo. En este artículo, revisamos la evidencia existente en cuanto a efectos nocivos en la salud por la exposición laboral a hidrocarburos en ambiente exterior. OBJETIVOS: Determinar la evidencia científica existente en la literatura acerca de los efectos biológicos de la exposición crónica laboral a hidrocarburos ambientales en los trabajadores expuestos a tráfico (y/o rodeados de HAPs). Material y MÉTODOS: Búsqueda bibliográfica en Pubmed, Toxnet, Scopus, Embase, y webs institucionales de donde recopilamos 25 artículos. RESULTADOS: Se han evidenciado cambios y efectos biológicos nocivos por exposición a los hidrocarburos ambientales (en su mayoría debidos al tráfico), así como la presencia de metabolitos en análisis biológicos de trabajadores expuestos. Dichos efectos han afectado al sistema reproductor, al sistema cardiovascular e incluso a la reparación de DNA. CONCLUSIONES: Parecen existir efectos nocivos para el organismo debidos a la exposición laboral ambiental. Se encontró asociación estadística significativa en la disminución de la reparación del DNA y en el aumento de metabolitos relacionados con hidrocarburos en sangre y orina

BACKGROUND: Traffic officers, drivers and other professionals previously exposed to other environmental traffic hydrocarbons, are acutely and chronically exposed to multiple hazardous substances that can affect health. These toxics are present in environmental pollution. In the literature reviewed, neither protocols nor PPEs have been found for these professions, which highlight the need to be taken into consideration. In this article, the existing evidence regarding the adverse health effects due to the exposure to hydrocarbons in external working environments is reviewed. OBJECTIVES: To determine the existing scientific evidence in literature about the biological effects of the work-related chronic exposure to environmental hydrocarbons in jobs exposed to traffic (and/or surrounded by PAHs). MATERIALS AND METHODS: 25 articles have been compiled from Bibliographic research in Pubmed, Toxnet, Scopus, Embase, and institutional websites. RESULTS: Changes and harmful biological effects due to exposure to environmental hydrocarbons (most of them caused by traffic) and the presence of metabolites in the biological analyses of exposed workers have been evidenced. Such effects have affected both the reproductive and cardiovascular systems and even DNA repair. CONCLUSIONS: Adverse effects on the organism due to environmental exposure in the workplace seem to take place. Significant statistical association was found in the decrease of DNA repair and in the increase of metabolites related with hydrocarbons in blood and urine

The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data.

BACKGROUND: Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies. NEW METHODS: We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps - from flexible read-in of data formats to GLM regressor/contrast creation - without any manual intervention. RESULTS: We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N=35). COMPARISON WITH EXISTING METHODS: The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data. CONCLUSIONS: Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.

Models of neuromodulation for computational psychiatry.

Psychiatry faces fundamental challenges: based on a syndrome-based nosology, it presently lacks clinical tests to infer on disease processes that cause symptoms of individual patients and must resort to trial-and-error treatment strategies. These challenges have fueled the recent emergence of a novel field-computational psychiatry-that strives for mathematical models of disease processes at physiological and computational (information processing) levels. This review is motivated by one particular goal of computational psychiatry: the development of 'computational assays' that can be applied to behavioral or neuroimaging data from individual patients and support differential diagnosis and guiding patient-specific treatment. Because the majority of available pharmacotherapeutic approaches in psychiatry target neuromodulatory transmitters, models that infer (patho)physiological and (patho)computational actions of different neuromodulatory transmitters are of central interest for computational psychiatry. This article reviews the (many) outstanding questions on the computational roles of neuromodulators (dopamine, acetylcholine, serotonin, and noradrenaline), outlines available evidence, and discusses promises and pitfalls in translating these findings to clinical applications. WIREs Cogn Sci 2017, 8:e1420. doi: 10.1002/wcs.1420 For further resources related to this article, please visit the WIREs website.

Translational Perspectives for Computational Neuroimaging.

Functional neuroimaging has made fundamental contributions to our understanding of brain function. It remains challenging, however, to translate these advances into diagnostic tools for psychiatry. Promising new avenues for translation are provided by computational modeling of neuroimaging data. This article reviews contemporary frameworks for computational neuroimaging, with a focus on forward models linking unobservable brain states to measurements. These approaches-biophysical network models, generative models, and model-based fMRI analyses of neuromodulation-strive to move beyond statistical characterizations and toward mechanistic explanations of neuroimaging data. Focusing on schizophrenia as a paradigmatic spectrum disease, we review applications of these models to psychiatric questions, identify methodological challenges, and highlight trends of convergence among computational neuroimaging approaches. We conclude by outlining a translational neuromodeling strategy, highlighting the importance of openly available datasets from prospective patient studies for evaluating the clinical utility of computational models.

Uncertainty in perception and the Hierarchical Gaussian Filter.

In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter (HGF) offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (in)stability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF's hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder-Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling) all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient-but at the same time intuitive-framework for the resolution of perceptual uncertainty in behaving agents.