Antipsychotics and COVID-19 Outcomes—The Potential Role of the Clinical Setting?

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Not understanding others. The RdoC approach to Theory of mind and empathy deficits in Schizophrenia, Borderline Personality Disorder and Mood Disorders

The Research Domani Criteria framework (RdoC) encourages research on specific impairments present across traditional nosological categories and suggests a list of biological and behavioral measures for assessing them. After a description of RdoC, in this article we focus on impairments of the ability of understanding others, specifically in Theory of Mind and empathy. We illustrate recent evidence on brain anomalies correlating with these deficits in Schizophrenia, Addiction Disorders and Mood Disorders populations. In the last section, we zoom out and consider this kind of research vis-à-vis the objection of being reductionistic that is, in favoring mechanistic accounts of mental disorders. We argue that metaphysical reductionism and explanatory reductionism are not conceptually entailed by the RdoC framework

Joint European policy on the COVID-19 risks for people with mental disorders: An umbrella review and evidence- and consensus-based recommendations for mental and public health

As COVID-19 becomes endemic, identifying vulnerable population groups for severe infection outcomes and defining rapid and effective preventive and therapeutic strategies remains a public health priority. We performed an umbrella review, including comprehensive studies (meta-analyses and systematic reviews) investigating COVID-19 risk for infection, hospitalization, intensive care unit (ICU) admission, and mortality in people with psychiatric disorders, and outlined evidence- and consensus-based recommendations for overcoming potential barriers that psychiatric patients may experience in preventing and managing COVID-19, and defining optimal therapeutic options and current research priorities in psychiatry. We searched Web of Science, PubMed, and Ovid/PsycINFO databases up to 17 January 2022 for the umbrella review. We synthesized evidence, extracting when available pooled odd ratio estimates for the categories “any mental disorder” and “severe mental disorders.” The quality of each study was assessed using the AMSTAR-2 approach and ranking evidence quality. We identified four systematic review/meta-analysis combinations, one meta-analysis, and three systematic reviews, each including up to 28 original studies. Although we rated the quality of studies from moderate to low and the evidence ranged from highly suggestive to non-significant, we found consistent evidence that people with mental illness are at increased risk of COVID-19 infection, hospitalization, and most importantly mortality, but not of ICU admission. The risk and the burden of COVID-19 in people with mental disorders, in particular those with severe mental illness, can no longer be ignored but demands urgent targeted and persistent action. Twenty-two recommendations are proposed to facilitate this process

Fronto-limbic effective connectivity as possible predictor of antidepressant response to SSRI administration

The timely selection of the optimal treatment for depressed patients is critical to improve remission rates. The detection of pre-treatment variables able to predict differential treatment response may provide novel approaches for treatment selection. Selective serotonin reuptake inhibitors (SSRIs) modulate the fronto-limbic functional response and connectivity, an effect preceding the overt clinical antidepressant effects. Here we investigated whether the cortico-limbic connectivity associated with emotional bias measured before SSRI administration predicts the efficacy of antidepressant treatment in MDD patients. fMRI and Dynamic Causal Modeling (DCM) were combined to study if effective connectivity might differentiate healthy controls (HC) and patients affected by major depression who later responded (RMDD, n=21), or failed to respond (nRMDD, n=12), to 6 weeks of escitalopram administration. Sixteen DCMs exploring connectivity between anterior cingulate cortex (ACC), ventrolateral prefrontal cortex (VLPFC), Amygdala (Amy), and fusiform gyrus (FG) were constructed. Analyses revealed that nRMDD had reduced endogenous connectivity from Amy to VLPFC and to ACC, with an increased connectivity and modulation of the ACC to Amy connectivity when processing of fearful emotional stimuli compared to HC. RMDD and HC did not significantly differ among themselves. Pre-treatment effective connectivity in fronto-limbic circuitry could be an important factor affecting antidepressant response, and highlight the mechanisms which may be involved in recovery from depression. These results suggest that fronto-limbic connectivity might provide a neural biomarker to predict the clinical outcome to SSRIs administration in major depression

Predicting differential diagnosis between bipolar and unipolar depression with multiple kernel learning on multimodal structural neuroimaging

One of the greatest challenges in providing early effective treatment in mood disorders is the early differential diagnosis between major depression (MDD) and bipolar disorder (BD). A remarkable need exists to identify reliable biomarkers for these disorders. We integrate structural neuroimaging techniques (i.e. Tract-based Spatial Statistics, TBSS, and Voxel-based morphometry) in a multiple kernel learning procedure in order to define a predictive function of BD against MDD diagnosis in a sample of 148 patients. We achieved a balanced accuracy of 73.65% with a sensitivity for BD of 74.32% and specificity for MDD of 72.97%. Mass-univariates analyses showed reduced grey matter volume in right hippocampus, amygdala, parahippocampal, fusiform gyrus, insula, rolandic and frontal operculum and cerebellum, in BD compared to MDD. Volumes in these regions and in anterior cingulate cortex were also reduced in BD compared to healthy controls (n = 74). TBSS analyses revealed widespread significant effects of diagnosis on fractional anisotropy, axial, radial, and mean diffusivity in several white matter tracts, suggesting disruption of white matter microstructure in depressed patients compared to healthy controls, with worse pattern for MDD. To best of our knowledge, this is the first study combining grey matter and diffusion tensor imaging in predicting BD and MDD diagnosis. Our results prompt brain quantitative biomarkers and multiple kernel learning as promising tool for personalized treatment in mood disorders

Time moderates the interplay between 5-HTTLPR and stress on depression risk: gene x environment interaction as a dynamic process

Abstract The serotonin-transporter-linked promoter region (5-HTTLPR) has been widely investigated as contributing to depression vulnerability. Nevertheless, empirical research provides wide contrasting findings regarding its involvement in the etiopathogenesis of the disorder. Our hypothesis was that such discrepancy can be explained considering time as moderating factor. We explored this hypothesis, exploiting a meta analytic approach. We searched PubMed, PsychoINFO, Scopus and EMBASE databases and 1096 studies were identified and screened, resulting in 22 studies to be included in the meta-analyses. The effect of the 5-HTTLPR x stress interaction on depression risk was found to be moderated by the following temporal factors: the duration of stress (i.e. chronic vs. acute) and the time interval between end of stress and assessment of depression (i.e. within 1 year vs. more than 1 year). When stratifying for the duration of stress, the effect of the 5-HTTLPR x stress interaction emerged only in the case of chronic stress, with a significant subgroup difference (p = 0.004). The stratification according to time interval revealed a significant interaction only for intervals within 1 year, though no difference between subgroups was found. The critical role of time interval clearly emerged when considering only chronic stress: a significant effect of the 5-HTTLPR and stress interaction was confirmed exclusively within 1 year and a significant subgroup difference was found (p = 0.01). These results show that the 5-HTTLPR x stress interaction is a dynamic process, producing different effects at different time points, and indirectly confirm that s-allele carriers are both at higher risk and more capable to recover from depression. Overall, these findings expand the current view of the interplay between 5-HTTLPR and stress adding the temporal dimension, that results in a three-way interaction: gene x environment x time

Multi-scale assessment of harmonization efficacy on resting-state functional connectivity

Raising trends of neuroimaging data sharing among different research centers, including resting-state functional magnetic resonance imaging (rs-fMRI) measurements, have driven to accessible large-scale sample and improvement of reliability and consistency of downstream analyses. However, in this context several concerns arise for non-biological confounding factors mainly related to differences in magnetic resonance scanners and imaging parameters among sites. Until now, there is limited knowledge of the impact of site-to-site variations in rsfMRI functional connectivity (FC) measures and the most suitable harmonization approach for mitigating such impact. In this study, we aimed to quantitatively evaluate the site-to-site variations in rs-fMRI FC patterns and how the widely used ComBat harmonization performs in removing them. A multi-scale analytical approach was adopted, from single pairs of regions to resting-state networks (RSNs) and to the entire brain. Our findings show that ComBat removes unwanted site effects from rs-fMRI FC measures while improving signal-to-noise ratio (SNR) in the data and RSNs identifiability. Further, we identify and visualized specific FC links highly affected by site, highlighting differences in such effects among RSNs. Overall, our findings demonstrate that ComBat is effective in harmonizing rs-fMRI FC measures, emphasizing also the overall RSNs identifiability and the enhancement of the majority of single RSNs in the entire brain connectome