Multivariate brain functional connectivity through regularized estimators

Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. Here, we propose extending connectivity to multivariate functions relating to the temporal dynamics of a region with the rest of the brain. The main technical challenges of such an approach are multidimensionality and its associated risk of overfitting or even the non-uniqueness of model solutions. To minimize these risks, and as an alternative to the more common dimensionality reduction methods, we propose using two regularized multivariate connectivity models. On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. On the other hand, a more flexible approach to avoid linearity and additivity assumptions was implemented through random forest regression. Similarities and differences between both methods and with simple averages of bivariate correlations (i.e., weighted global brain connectivity) were evaluated on a resting state sample of N = 173 healthy subjects. Results revealed distinct connectivity patterns from the two proposed methods, which were especially relevant in the age-related analyses where both ridge and random forest regressions showed significant patterns of age-related disconnection, almost completely absent from the much less sensitive global brain connectivity maps. On the other hand, the greater flexibility provided by the random forest algorithm allowed detecting sex-specific differences. The generic framework of multivariate connectivity implemented here may be easily extended to other types of regularized models

Assessment of vocal cord nodules: A case study in speech processing by using Hilbert-Huang Transform

Vocal cord nodules represent a pathological condition for which the growth of unnatural masses on vocal folds affects the patients. Among other effects, changes in the vocal cords' overall mass and stiffness alter their vibratory behaviour, thus changing the vocal emission generated by them. This causes dysphonia, i.e. abnormalities in the patients' voice, which can be analysed and inspected via audio signals. However, the evaluation of voice condition through speech processing is not a trivial task, as standard methods based on the Fourier Transform, fail to fit the non-stationary nature of vocal signals. In this study, four audio tracks, provided by a volunteer patient, whose vocal fold nodules have been surgically removed, were analysed using a relatively new technique: the Hilbert-Huang Transform (HHT) via Empirical Mode Decomposition (EMD); specifically, by using the CEEMDAN (Complete Ensemble EMD with Adaptive Noise) algorithm. This method has been applied here to speech signals, which were recorded before removal surgery and during convalescence, to investigate specific trends. Possibilities offered by the HHT are exposed, but also some limitations of decomposing the signals into so-called intrinsic mode functions (IMFs) are highlighted. The results of these preliminary studies are intended to be a basis for the development of new viable alternatives to the softwares currently used for the analysis and evaluation of pathological voice

A Patient-Specific in silico Model of Inflammation and Healing Tested in Acute Vocal Fold Injury

The development of personalized medicine is a primary objective of the medical community and increasingly also of funding and registration agencies. Modeling is generally perceived as a key enabling tool to target this goal. Agent-Based Models (ABMs) have previously been used to simulate inflammation at various scales up to the whole-organism level. We extended this approach to the case of a novel, patient-specific ABM that we generated for vocal fold inflammation, with the ultimate goal of identifying individually optimized treatments. ABM simulations reproduced trajectories of inflammatory mediators in laryngeal secretions of individuals subjected to experimental phonotrauma up to 4 hrs post-injury, and predicted the levels of inflammatory mediators 24 hrs post-injury. Subject-specific simulations also predicted different outcomes from behavioral treatment regimens to which subjects had not been exposed. We propose that this translational application of computational modeling could be used to design patient-specific therapies for the larynx, and will serve as a paradigm for future extension to other clinical domains

Digitalization and the Anthropocene

Great claims have been made about the benefits of dematerialization in a digital service economy. However, digitalization has historically increased environmental impacts at local and planetary scales, affecting labor markets, resource use, governance, and power relationships. Here we study the past, present, and future of digitalization through the lens of three interdependent elements of the Anthropocene: (a) planetary boundaries and stability, (b) equity within and between countries, and (c) human agency and governance, mediated via (i) increasing resource efficiency, (ii) accelerating consumption and scale effects, (iii) expanding political and economic control, and (iv) deteriorating social cohesion. While direct environmental impacts matter, the indirect and systemic effects of digitalization are more profoundly reshaping the relationship between humans, technosphere and planet. We develop three scenarios: planetary instability, green but inhumane, and deliberate for the good. We conclude with identifying leverage points that shift human–digital–Earth interactions toward sustainability

Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique

The extreme variability in symptom presentation reveals that individuals diagnosed with a first-episode psychosis (FEP) may encompass different sub-populations with potentially different illness courses and, hence, different treatment needs. Previous studies have shown that sociodemographic and family environment factors are associated with more unfavorable symptom trajectories. The aim of this study was to examine the dimensional structure of symptoms and to identify individuals’ trajectories at early stage of illness and potential risk factors associated with poor outcomes at follow-up in non-affective FEP. One hundred and forty-four non-affective FEP patients were assessed at baseline and at 2-year follow-up. A Principal component analysis has been conducted to identify dimensions, then an unsupervised machine learning technique (fuzzy clustering) was performed to identify clinical subgroups of patients. Six symptom factors were extracted (positive, negative, depressive, anxiety, disorganization and somatic/cognitive). Three distinct clinical clusters were determined at baseline: mild; negative and moderate; and positive and severe symptoms, and five at follow-up: minimal; mild; moderate; negative and depressive; and severe symptoms. Receiving a low-dose antipsychotic, having a more severe depressive symptomatology and a positive family history for psychiatric disorders were risk factors for poor recovery, whilst having a high cognitive reserve and better premorbid adjustment may confer a better prognosis. The current study provided a better understanding of the heterogeneous profile of FEP. Early identification of patients who could likely present poor outcomes may be an initial step for the development of targeted interventions to improve illness trajectories and preserve psychosocial functioning

Obstetric complications and clinical presentation in first episode of psychosis

Objective: Psychotic disorders exhibit a complex aetiology that combines genetic and environmental factors. Among the latter, obstetric complications (OCs) have been widely studied as risk factors, but it is not yet well understood how OCs relate to the heterogeneous presentations of psychotic disorders. We assessed the clinical phenotypes of individuals with a first episode of psychosis (FEP) in relation to the presence of OCs. Methods: Two-hundred seventy-seven patients with an FEP were assessed for OCs using the Lewis–Murray scale, with data stratified into three subscales depending on the timing and the characteristics of the obstetric event, namely: complications of pregnancy, abnormal foetal growth and development and difficulties in delivery. We also considered other two groups: any complications during the pregnancy period and all OCs taken altogether. Patients were clinically evaluated with the Positive and Negative Syndrome Scale for schizophrenia. Results: Total OCs and difficulties in delivery were related to more severe psychopathology, and this remained significant after co-varying for age, sex, traumatic experiences, antipsychotic dosage and cannabis use. Conclusions: Our results highlight the relevance of OCs for the clinical presentation of psychosis. Describing the timing of the OCs is essential in understanding the heterogeneity of the clinical presentation

Down-Regulated NOD2 by Immunosuppressants in Peripheral Blood Cells in Patients with SLE Reduces the Muramyl Dipeptide-Induced IL-10 Production

Pattern recognition receptors (PRRs) such as Toll-like receptors are aberrantly expressed of peripheral blood mononuclear cells (PBMCs) in systemic lupus erythematosus (SLE) patients, for playing immunopathological roles. basal productions of cytokines (IL-6, IL-8 and IL-10) were significantly increased in immunosuppressant naïve patients and patients with active disease despite immunosuppressants compared with HCs. Upon MDP stimulaiton, relative induction (%) of cytokines (IL-1β) from PBMC was significantly increased in immunosuppressant naïve patients with inactive disease, and patients with active disease despite immunosuppressant treatment compared with HCs. Immunosuppressant usage was associated with a decreased basal production and MDP induced relative induction (%) of IL-10 in patients with inactive disease compared with immunosuppressant naïve patients and HCs.Bacterial exposure may increase the NOD2 expression in monocytes in immunosuppressant naïve SLE patients which can subsequently lead to aberrant activation of PBMCs to produce proinflammatory cytokines, implicating the innate immune response for extracellular pathogens in the immunopathological mechanisms in SLE. Immunosuppressant therapy may downregulate NOD2 expression in CD8+ T lymphocytes, monocytes, and DCs in SLE patients which subsequently IL-10 reduction, contributing towards the regulation of immunopathological mechanisms of SLE, at the expense of increasing risk of bacterial infection

Material Cycles, Industry and Service Provisioning: A Review of Low Energy and Material Demand Modelling and Scenarios

Developing transformative pathways for industry’s compliance with international climate targets requires model-based insights on how supply- and demand-side measures affect industry, material cycles, global supply chains, socio-economic activities and service provisioning supporting societal wellbeing. Herein, we review the recent literature modelling the industrial system for Low Energy and Materials Demand (LEMD) futures, resulting in lowered environmental pressures without relying on negative emissions. We identify 77 innovative studies drawing on nine distinct industry modelling traditions and critically assess system definitions and scopes, biophysical and thermodynamic consistency, granularity and heterogeneity, and operationalization of demand and service provision. We find large potentials of combined supply- and demand-side measures to reduce current economy-wide material use by -56%, energy use by -40 to -60%, and GHG emissions by -70% to net-zero. We call for strengthening interdisciplinary collaborations between industry modelling traditions and demand-side research, to produce more insightful scenarios and discuss research challenges and recommendations