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Cognitive regulation of emotions is a fundamental prerequisite for intact social functioning which impacts on both well being and psychopathology. The neural underpinnings of this process have been studied intensively in recent years, without, however, a general consensus. We here quantitatively summarize the published literature on cognitive emotion regulation using activation likelihood estimation in fMRI and PET (23 studies/479 subjects). In addition, we assessed the particular functional contribution of identified regions and their interactions using quantitative functional inference and meta-analytic connectivity modeling, respectively. In doing so, we developed a model for the core brain network involved in emotion regulation of emotional reactivity. According to this, the superior temporal gyrus, angular gyrus and (pre) supplementary motor area should be involved in execution of regulation initiated by frontal areas. The dorsolateral prefrontal cortex may be related to regulation of cognitive processes such as attention, while the ventrolateral prefrontal cortex may not necessarily reflect the regulatory process per se, but signals salience and therefore the need to regulate. We also identified a cluster in the anterior middle cingulate cortex as a region, which is anatomically and functionally in an ideal position to influence behavior and subcortical structures related to affect generation. Hence this area may play a central, integrative role in emotion regulation. By focusing on regions commonly active across multiple studies, this proposed model should provide important a priori information for the assessment of dysregulated emotion regulation in psychiatric disorders. •We quantitatively summarize the literature on emotion regulation (ER) using ALE.•Using MACM and quantitative functional inference we develop a neural model of ER.•DLPFC is related to higher order “cold” regulatory processes.•VLPFC evaluates salience and indicates need to regulate.•STG, angular gyrus and SMA are associated to execution of regulation.

High sensitivity C-reactive protein (hsCRP) is a marker of systemic inflammation that has been associated with persistent depressive symptoms. Depression and anxiety are frequently associated with a chronic inflammatory state, yet the nature of this relationship has not been rigorously examined in diverse Hispanic/Latino populations. We aimed to study the association of anxiety and depressive symptoms as well as comorbid presentations, with circulating high sensitivity C-reactive protein (hsCRP) levels in a large Latino cohort of diverse heritages. We hypothesized a significant positive associations of both anxiety and depressive symptoms and hsCRP levels and potential variations among the heritage groups. Depressive symptoms and anxiety were measured by the Center for Epidemiological Studies Depression Scale (CES-D) and State-Trait Anxiety Inventory (STAI), respectively. Serum hsCRP (hsCRP) levels of 15,448 participants (age 18 to 75 years; 52.3% women) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) were measured and categorized based on the established cardiovascular disease (CVD) risk reference values (< 1mg/L, low; 1-<3 mg/L, intermediate; ≥ 3mg/L, high). Mean CES-D, STAI scores, and hsCRP levels were 7.0 (SD = 5.9), 17.0 (SD = 5.7), and 3.84 (SD = 7.85), respectively. Generalized linear modeling, adjusted for sociodemographic characteristics revealed significant associations between depression (exp(β) = 1.12; p<0.01) and anxiety symptoms (exp(β) = 1.10; p<0.05) with continuous hsCRP levels. For categorical values of hsCRP, one SD increase in CES-D and STAI scores was associated with a 10% and 8% increase in the RRRs of high vs. low hsCRP, respectively. However, these relationships between CES-D or STAI and hsCRP were no longer statistically significant after adjustment for CVD risk factors and medications. We found modest associations between anxiety and depressive symptoms and systemic inflammation measured by hsCRP among diverse Hispanics/Latinos that did not appreciably differ between heritage groups.

Background Major Depressive Disorder (MDD) has a 50–80% recurrence rate highlighting the urgent need for more efficient recurrence prevention programs. Currently, recurrences are often identified too late, while existing preventive strategies may not sufficiently address ethio-patho-physiological mechanisms for recurrence. Negative memory bias (the tendency to better remember negative than positive events), negative attention bias (selective attention favoring mood-congruent information), and cognitive control deficits are important factors involved in the onset, maintenance, and recurrence of depressive episodes. Methods Here we describe the protocol for the Smartphone-based Monitoring and cognition Modification Against Recurrence of Depression (SMARD) study, aiming to investigate different forms of cognitive training programs administered via smartphones, in order to develop a second-generation recurrence prevention program. In addition, we will gather Experience Sampling Method (ESM) assessments during a 6-day period, and during the follow-up period we will obtain behavioral data on (social) activities with BEHAPP, a smartphone-based Mobile Passive Monitoring application for remote behavioral monitoring to identify behavioral changes indicative of an imminent depressive episode. In a randomized controlled trial, SMARD will compare the effects of a smartphone-based Memory Bias Modification Training (MBT), Cognitive Control Training (CCT), and Attention Bias Modification Training (ABT) versus cognitive domain-specific (active-) sham trainings in 120 remitted MDD-patients with recurrent-MDD. Over the course of three weeks, participants receive multiple daily training sessions. Thereafter, participants will be followed up for 1.5 years with 3-monthly interviews to assess recurrences. Discussion The SMARD study aims to 1. assess the effects of the cognitive training programs versus their training-specific (active-) sham conditions on changes in memory, cognitive control dysfunction and attention; 2. relate training effects to neural networks previously identified in (recurrence of) MDD (therefore we obtain functional Magnetic Resonance Imaging ((f)MRI) scans before and after the training in a subset of participants); 3. link baseline and change in memory, cognitive control, attention and neural functioning, and ESM data to prospective recurrences; 4. examine whether passive smartphone-use monitoring can be used for prediction of recurrences. Trial registration NL-OMON26184 and NL-OMON27513. Registered 12 August 2021—Retrospectively registered,  https://onderzoekmetmensen.nl/en/trial/26184 en https://onderzoekmetmensen.nl/en/trial/27513 .

In the last few years the involvement of the medial prefrontal cortex (mPFC) in memory processing has received increased attention. It has been shown to be centrally involved when we use prior knowledge (schemas) to improve learning of related material. With the mPFC also being one of the core hubs of the default mode network (DMN) and the DMN’s role in memory retrieval, we decided to investigate whether the mPFC in a schema paradigm acts independent of the DMN. We tested this with data from a cross-sectional developmental study with a schema paradigm. During retrieval of schema items, the mPFC decoupled from the DMN with the degree of decoupling predicting memory performance. This finding suggests that a demand specific reconfiguration of the DMN supports schema memory. Additionally, we found that in the control condition, which relied on episodic memory, activity in the parahippocampal gyrus was positively related to memory performance. We interpret these results as a demand specific network reconfiguration of the DMN: a decoupling of the mPFC to support schema memory and a decoupling of the parahippocampal gyrus facilitating episodic memory. •The medial prefrontal cortex (mPFC) is central to schema memory based learning.•The mPFC is also one of the core hubs of the default mode network (DMN).•We found network reconfiguration of mPFC during schema memory benefits performance.•Parahippocampal network reconfiguration in non-schema trials supports episodic memory.

Long COVID is a chronic condition characterized by symptoms such as fatigue, dyspnea, and cognitive impairment that persist or relapse months after an acute infection with the SARS-CoV-2 virus. Many distinct symptoms have been attributed to Long COVID; however, little is known about the potential clustering of these symptoms and risk factors that may predispose patients to certain clusters. In this study, an electronic survey was sent to patients in the UC San Diego Health (UCSDH) system who tested positive for COVID-19, querying if patients were experiencing symptoms consistent with Long COVID. Based on survey results, along with patient demographics reported in the electronic health record (EHR), linear and logistic regression models were used to examine putative risk factors, and exploratory factor analysis was performed to determine symptom clusters. Among 999 survey respondents, increased odds of Long COVID (n = 421; 42%) and greater Long COVID symptom burden were associated with female sex (OR = 1.73, 99% CI: 1.16–2.58; β = 0.48, 0.22–0.75), COVID-19 hospitalization (OR = 4.51, 2.50–8.43; β = 0.48, 0.17–0.78), and poorer pre-COVID self-rated health (OR = 0.75, 0.57–0.97; β = −0.19, −0.32–−0.07). Over one-fifth of Long COVID patients screened positive for depression and/or anxiety, the latter of which was associated with younger age (OR = 0.96, 0.94–0.99). Factor analysis of 16 self-reported symptoms suggested five symptom clusters—gastrointestinal (GI), musculoskeletal (MSK), neurocognitive (NC), airway (AW), and cardiopulmonary (CP), with older age (β = 0.21, 0.11–0.30) and mixed race (β = 0.27, 0.04–0.51) being associated with greater MSK symptom burden. Greater NC symptom burden was associated with increased odds of depression (OR = 5.86, 2.71–13.8) and anxiety (OR = 2.83, 1.36–6.14). These results can inform clinicians in identifying patients at increased risk for Long COVID-related medical issues, particularly neurocognitive symptoms and symptom clusters, as well as informing health systems to manage operational expectations on a population-health level.

Background Though theoretically superior to standard 2D visualization, 3D video systems have not yet achieved a breakthrough in laparoscopy. The latest 3D monitors, including autostereoscopic displays and high-definition (HD) resolution, are designed to overcome the existing limitations. Methods We performed a randomized study on 48 individuals with different experience levels in laparoscopy. Three different 3D displays (glasses-based 3D monitor, autostereoscopic display, and a mirror-based theoretically ideal 3D display) were compared to a 2D HD display by assessing multiple performance and mental workload parameters and rating the subjects during a laparoscopic suturing task. Electromagnetic tracking provided information on the instruments’ pathlength, movement velocity, and economy. The usability, the perception of visual discomfort, and the quality of image transmission of each monitor were subjectively rated. Results Almost all performance parameters were superior with the conventional glasses-based 3D display compared to the 2D display and the autostereoscopic display, but were often significantly exceeded by the mirror-based 3D display. Subjects performed a task faster and with greater precision when visualization was achieved with the 3D and the mirror-based display. Instrument pathlength was shortened by improved depth perception. Workload parameters (NASA TLX) did not show significant differences. Test persons complained of impaired vision while using the autostereoscopic monitor. The 3D and 2D displays were rated user-friendly and applicable in daily work. Experienced and inexperienced laparoscopists profited equally from using a 3D display, with an improvement in task performance about 20 %. Conclusion Novel 3D displays improve laparoscopic interventions as a result of faster performance and higher precision without causing a higher mental workload. Therefore, they have the potential to significantly impact the further development of minimally invasive surgery. However, as shown by the custom-built 3D mirror display, this effect can be improved, thus stimulating further research.

Background Depression and obesity are highly prevalent, often co-occurring conditions marked by inflammation. Microbiome perturbations are implicated in obesity-inflammation-depression interrelationships, but how the microbiome mechanistically contributes to pathology remains unclear. Metabolomic investigations into microbial neuroactive metabolites may offer mechanistic insights into host-microbe interactions. Using 16S sequencing and untargeted mass spectrometry of saliva, and blood monocyte inflammation regulation assays, we identified key microbes, metabolites and host inflammation in association with depressive symptomatology, obesity, and depressive symptomatology-obesity comorbidity. Results Gram-negative bacteria with inflammation potential were enriched relative to Gram-positive bacteria in comorbid obesity-depression, supporting the inflammation-oral microbiome link in obesity-depression interrelationships. Oral microbiome was more highly predictive of depressive symptomatology-obesity co-occurrences than of obesity or depressive symptomatology independently, suggesting specific microbial signatures associated with obesity-depression co-occurrences. Mass spectrometry analysis revealed significant changes in levels of signaling molecules of microbiota, microbial or dietary derived signaling peptides and aromatic amino acids among depressive symptomatology, obesity and comorbid obesity-depression. Furthermore, integration of the microbiome and metabolomics data revealed that key oral microbes, many previously shown to have neuroactive potential, co-occurred with potential neuropeptides and biosynthetic precursors of the neurotransmitters dopamine, epinephrine and serotonin. Conclusions Together, our findings offer novel insights into oral microbial-brain connection and potential neuroactive metabolites involved.

Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (TRD). Leveraging electronic medical records (EMR), this retrospective cohort study applied supervised machine learning (ML) to sociodemographic, clinical, and treatment-related data to predict depressive symptom response (>50% reduction on PHQ-9) and remission (PHQ-9 < 5) following rTMS in 232 patients with TRD (mean age: 54.5, 63.4% women) treated at the University of California, San Diego Interventional Psychiatry Program between 2017 and 2023. ML models were internally validated using nested cross-validation and Shapley values were calculated to quantify contributions of each feature to response prediction. The best-fit models proved reasonably accurate at discriminating treatment responders (Area under the curve (AUC): 0.689 [0.638, 0.740], p  < 0.01) and remitters (AUC 0.745 [0.692, 0.797], p  < 0.01), though only the response model was well-calibrated. Both models were associated with significant net benefits, indicating their potential utility for clinical decision-making. Shapley values revealed that patients with comorbid anxiety, obesity, concurrent benzodiazepine or antipsychotic use, and more chronic TRD were less likely to respond or remit following rTMS. Patients with trauma and former tobacco users were more likely to respond. Furthermore, delivery of intermittent theta burst stimulation and more rTMS sessions were associated with superior outcomes. These findings highlight the potential of ML-guided techniques to guide clinical decision-making for rTMS treatment in patients with TRD to optimize therapeutic outcomes.

Background In everyday life, negative emotions can be implicitly regulated by positive stimuli, without any conscious cognitive engagement; however, the effects of such implicit regulation on mood and related neuro-mechanisms, remain poorly investigated in literature. Yet, improving implicit emotional regulation could reduce psychological burden and therefore be clinically relevant for treating psychiatric disorders with strong affective symptomatology. Results Music training reduced the negative emotional state elicited by negative odours. However, such change was not reflected at the brain level. Conclusions In a context of affective rivalry a musical training enhances implicit regulatory processes. Our findings offer a first base for future studies on implicit emotion regulation in clinical populations.

3,4-Methylenedioxymethamphetamine (MDMA) increases sociality in humans and animals. Release of serotonin (5-HT) is thought to have an important role in the increase in social behaviors, but the mechanisms underlying these effects are poorly understood. Despite the advantages of nonhuman primate models, no studies have examined the mechanisms of the social effects of MDMA in nonhuman primates. The behavior and vocalizations of four group-housed squirrel monkeys were examined following administration of MDMA, its enantiomers, and methamphetamine. 5-HT receptor antagonists and agonists were given as drug pretreatments. Data were analyzed using linear mixed-effects models. MDMA and its enantiomers increased affiliative social behaviors and vocalizations, whereas methamphetamine had only modest effects on affiliative behaviors. Pretreatment with a 5-HT receptor antagonist and a 5-HT receptor agonist attenuated the MDMA-induced increase in social behaviors, while a 5-HT receptor antagonist did not alter affiliative vocalizations and increased MDMA-induced social contact. Nonhuman primates show MDMA-specific increases in affiliative social behaviors following MDMA administration, in concordance with human and rodent studies. MDMA-induced increases in social behaviors are 5-HT , but not 5-HT , receptor dependent. Understanding the neurochemical mechanisms mediating the prosocial effects of MDMA could help in the development of novel therapeutics with the unique social effects of MDMA but fewer of its limitations.