Do social support and community engagement act as mechanisms in the association between neighbourhood income inequality and the mental health of mothers in Calgary,Canada? A mediation analysis

PurposeAccording to the social determinants of health framework, income inequality is a potential risk factor for adverse mental health. However, few studies have explored the mechanisms suspected to mediate this relationship. The current study addresses this gap through a mediation analysis to determine if social support and community engagement act as mediators linking neighbourhood income inequality to maternal anxiety and depressive symptoms within a cohort of new mothers living in the City of Calgary, Canada.MethodsData collected at three years postpartum from mothers belonging to the All Our Families (AOF) cohort were used in the current study. Maternal data were collected between 2012 and 2015 and linked to neighbourhood socioeconomic data from the 2006 Canadian Census. Income inequality was measured using Gini coefficients derived from 2006 after-tax census data. Generalized structural equation models were used to quantify the associations between income inequality and mental health symptoms, and to assess the potential direct and indirect mediating effects of maternal social support and community engagement.ResultsIncome inequality was not significantly associated with higher depressive symptoms (β = 0.32, 95%CI = −0.067, 0.70), anxiety symptoms (β = 0.11, 95%CI = −0.39, 0.60), or lower social support. Income inequality was not associated with community engagement. For the depression models, higher social support was significantly associated with lower depressive symptoms (β = −0.13, 95%CI = −0.15, −0.097), while community engagement was not significantly associated with depressive symptoms (β = 0.059, 95%CI = −0.15, 0.27). Similarly, for the anxiety models, lower anxiety symptoms were significantly associated with higher levels of social support (β = −0.17, 95%CI = −0.20, −0.13) but not with higher levels of community engagement (β = 0.14, 95%CI = −0.14, 0.41).ConclusionThe current study did not find clear evidence for social support or community engagement mediating the relationship between neighbourhood income inequality and maternal mental health. Future investigations should employ a broader longitudinal approach to capture changes in income inequality, potential mediators, and mental health symptomatology over time.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Intensified electrical stimulation targeting lateral and medial prefrontal cortices for the treatment of social anxiety disorder: A randomized,double-blind,parallel-group,dose-comparison study

BackgroundSocial Anxiety Disorder (SAD) is the most common anxiety disorder while remains largely untreated. Disturbed amygdala-frontal network functions are central to the pathophysiology of SAD, marked by hypoactivity of the lateral prefrontal cortex (PFC), and hypersensitivity of the medial PFC and the amygdala. The objective of this study was to determine whether modulation of the dorsolateral and medial PFC activity with a novel intensified stimulation protocol reduces SAD core symptoms, improves treatment-related variables, and reduces attention bias to threatening stimuli.MethodsIn this randomized, sham-controlled, double-blind trial, we assessed the efficacy of an intensified stimulation protocol (20 min, twice-daily sessions with 20 min intervals, 5 consecutive days) in two intensities (1 vs 2 mA) compared to sham stimulations. 45 patients with SAD were randomized in three tDCS arms (1-mA, 2-mA, sham). SAD symptoms, treatment-related variables (worries, depressive state, emotion regulation, quality of life), and attention bias to threatening stimuli (dot-probe paradigm) were assessed before and right after the intervention. SAD symptoms were also assessed at 2-month follow-up.ResultsBoth 1-mA and 2-mA protocols significantly reduced fear/avoidance symptoms, worries and improved, emotion regulation and quality of life after the intervention compared to the sham group. Improving effect of the 2-mA protocol on avoidance symptoms, worries and depressive state was significantly larger than the 1-mA group. Only the 2-mA protocol reduced attention bias to threat-related stimuli, the avoidance symptom at follow-up, and depressive states, as compared to the sham group.ConclusionsModulation of lateral-medial PFC activity with intensified stimulation can improve cognitive control, motivation and emotion networks in SAD and might thereby result in therapeutic effects. These effects can be larger with 2-mA vs 1-mA intensities, though a linear relationship between intensity and efficacy should not be concluded. Our results need replication in larger trials.