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

Obstetric complications and clinical presentation in first episode of psychosis

Verdolini N, Mezquida G, Valli I, Garcia-Rizo C, Cuesta M, Vieta E, Bioque M, Lobo A, González-Pinto A, Pina-Camacho L, Corripio I, Garriga M, Baeza I, Martínez-Sadurní L, Bitanihirwe B, Cannon M, Bernardo M; PEPs GROU

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

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

The effect of family environment and psychiatric family history on psychosocial functioning in first-episode psychosis at baseline and after 2 years

The aim of the present study was to evaluate the contribution of family environment styles and psychiatric family history on functioning of patients presenting first-episode psychosis (FEP). Patients with FEP and healthy controls (HC) were assessed at baseline and after 2 years. The Functional Assessment Short Test (FAST) was used to assess functional outcome and the Family Environment Scale (FES) to evaluate family environment. Linear regressions evaluated the effect that family environment exerts on functioning at baseline and at 2-year follow-up, when FEP patients were diagnosed according to non-affective (NA-PSYCH) or affective psychoses (A-PSYCH). The influence of a positive parents’ psychiatric history on functioning was evaluated through one-way between-groups analysis of covariance (ANCOVA) models, after controlling for family environmental styles. At baseline, FEP patients presented moderate functioning impairment, significantly worse than HC (28.65±16.17 versus 3.25±7.92; p<0.001, g = 1.91). At 2-year follow-up, the functioning of NA-PSYCH patients was significantly worse than in A-PSYCH (19.92±14.83 versus 12.46±14.86; p = 0.020, g = 0.50). No specific family environment style was associated with functioning in FEP patients and HC. On the contrary, a positive psychiatric father''s history influenced functioning of FEP patients. After 2 years, worse functioning in NA-PSYCH patients was associated with lower rates of active-recreational and achievement orientated family environment and with higher rates of moral-religious emphasis and control. In A-PSYCH, worse functioning was associated with higher rates of conflict in the family. Both family environment and psychiatric history influence psychosocial functioning, with important implications for early interventions, that should involve both patients and caregivers

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

Patterns of pharmacotherapy for bipolar disorder: A GBC survey

Objectives: To understand treatment practices for bipolar disorders (BD), this study leveraged the Global Bipolar Cohort collaborative network to investigate pharmacotherapeutic treatment patterns in multiple cohorts of well-characterized individuals with BD in North America, Europe, and Australia. Methods: Data on pharmacotherapy, demographics, diagnostic subtypes, and comorbidities were provided from each participating cohort. Individual site and regional pooled proportional meta-analyses with generalized linear mixed methods were conducted to identify prescription patterns. Results: This study included 10,351 individuals from North America (n = 3985), Europe (n = 3822), and Australia (n = 2544). Overall, participants were predominantly female (60%) with BD-I (60%; vs. BD-II = 33%). Cross-sectionally, mood-stabilizing anticonvulsants (44%), second-generation antipsychotics (42%), and antidepressants (38%) were the most prescribed medications. Lithium was prescribed in 29% of patients, primarily in the Australian (31%) and European (36%) cohorts. First-generation antipsychotics were prescribed in 24% of the European versus 1% in the North American cohort. Antidepressant prescription rates were higher in BD-II (47%) compared to BD-I (35%). Major limitations were significant differences among cohorts based on inclusion/exclusion criteria, data source, and time/year of enrollment into cohort. Conclusions: Mood-stabilizing anticonvulsants, second-generation antipsychotics, and antidepressants were the most prescribed medications suggesting prescription patterns that are not necessarily guideline concordant. Significant differences exist in the prescription practices across different geographic regions, especially the underutilization of lithium in the North American cohorts and the higher utilization of first-generation antipsychotics in the European cohorts. There is a need to conduct future longitudinal studies to further explore these differences and their impact on outcomes, and to inform and implement evidence-based guidelines to help improve treatment practices in BD

Atlas of Gray Matter Volume Differences Across Psychiatric Conditions: A Systematic Review With a Novel Meta-Analysis That Considers Co-Occurring Disorders

Background Regional gray matter volume (GMV) differences between individuals with mental disorders and comparison participants may be confounded by co-occurring disorders. To disentangle disorder-specific GMV correlates, we conducted a large-scale multidisorder meta-analysis using a novel approach that explicitly models co-occurring disorders. Methods We systematically reviewed voxel-based morphometry studies indexed in PubMed and Scopus up to January 2023 that compared adults with major mental disorders (anorexia nervosa, schizophrenia spectrum, anxiety, bipolar, major depressive, obsessive-compulsive, and posttraumatic stress disorders plus attention-deficit/hyperactivity, autism spectrum, and borderline personality disorders) with comparison participants. Two authors independently extracted data and assessed quality using the Newcastle-Ottawa Scale. We derived GMV correlates for each disorder using: 1) a multidisorder meta-analysis that accounted for all co-occurring mental disorders simultaneously and 2) separate standard meta-analyses for each disorder in which co-occurring disorders were ignored. We assessed the alterations’ extent, intensity (effect size), and specificity (interdisorder correlations and transdiagnostic alterations) for both approaches. Results We included 433 studies (499 datasets) involving 19,718 patients and 16,441 comparison participants (51% female, ages 20–67 years). We provide GMV correlate maps for each disorder using both approaches. The novel approach, which accounted for co-occurring disorders, produced GMV correlates that were more focal and disorder specific (less correlated across disorders and fewer transdiagnostic abnormalities)