A translational and multidisciplinary approach to studying the Garcia effect, a higher form of learning with deep evolutionary roots.

Animals, including humans, learn and remember to avoid a novel food when its ingestion is followed, hours later, by sickness - a phenomenon initially identified during World War II as a potential means of pest control. In the 1960s, John Garcia (for whom the effect is now named) demonstrated that this form of conditioned taste aversion had broader implications, showing that it is a rapid but long-lasting taste-specific food aversion with a fundamental role in the evolution of behaviour. From the mid-1970s onward, the principles of the Garcia effect were translated to humans, showing its role in different clinical conditions (e.g. side-effects linked to chemotherapy). However, in the last two decades, the number of studies on the Garcia effect has undergone a considerable decline. Since its discovery in rodents, this form of learning was thought to be exclusive to mammals; however, we recently provided the first demonstration that a Garcia effect can be formed in an invertebrate model organism, the pond snail Lymnaea stagnalis. Thus, in this Commentary, after reviewing the experiments that led to the first characterization of the Garcia effect in rodents, we describe the recent evidence for the Garcia effect in L. stagnalis, which may pave the way for future studies in other invertebrates and mammals. This article aims to inspire future translational and ecological studies that characterize the conserved mechanisms underlying this form of learning with deep evolutionary roots, which can be used to address a range of different biological questions.

The dynamic interaction between symptoms and pharmacological treatment in patients with major depressive disorder: the role of network intervention analysis.

INTRODUCTION: The Major Depressive Disorder (MDD) is a mental health disorder that affects millions of people worldwide. It is characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities that were once enjoyable. MDD is a major public health concern and is the leading cause of disability, morbidity, institutionalization, and excess mortality, conferring high suicide risk. Pharmacological treatment with Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin Noradrenaline Reuptake Inhibitors (SNRIs) is often the first choice for their efficacy and tolerability profile. However, a significant percentage of depressive individuals do not achieve remission even after an adequate trial of pharmacotherapy, a condition known as treatment-resistant depression (TRD). METHODS: To better understand the complexity of clinical phenotypes in MDD we propose Network Intervention Analysis (NIA) that can help health psychology in the detection of risky behaviors, in the primary and/or secondary prevention, as well as to monitor the treatment and verify its effectiveness. The paper aims to identify the interaction and changes in network nodes and connections of 14 continuous variables with nodes identified as "Treatment" in a cohort of MDD patients recruited for their recent history of partial response to antidepressant drugs. The study analyzed the network of MDD patients at baseline and after 12 weeks of drug treatment. RESULTS: At baseline, the network showed separate dimensions for cognitive and psychosocial-affective symptoms, with cognitive symptoms strongly affecting psychosocial functioning. The MoCA tool was identified as a potential psychometric tool for evaluating cognitive deficits and monitoring treatment response. After drug treatment, the network showed less interconnection between nodes, indicating greater stability, with antidepressants taking a central role in driving the network. Affective symptoms improved at follow-up, with the highest predictability for HDRS and BDI-II nodes being connected to the Antidepressants node. CONCLUSION: NIA allows us to understand not only what symptoms enhance after pharmacological treatment, but especially the role it plays within the network and with which nodes it has stronger connections.

A network study to differentiate suicide attempt risk profiles in male and female patients with major depressive disorder.

Suicide attempts are a possible consequence of Major Depressive Disorder (MDD), although their prevalence varies across different epidemiological studies. Suicide attempt is a significant predictor of death by suicide, highlighting its importance in understanding and preventing tragic outcomes. Researchers are increasingly recognizing the need to study the differences between males and females, as several distinctions emerge in terms of the characteristics, types and motivations of suicide attempts. These differences emphasize the importance of considering gender-specific factors in the study of suicide attempts and developing tailored prevention strategies. We conducted a network analysis to represent and investigate which among multiple neurocognitive, psychosocial, demographic and affective variables may prove to be a reliable predictor for identifying the 'suicide attempt risk' (SAR) in a sample of 81 adults who met DSM-5 criteria for MDD. Network analysis resulted in differences between males and females regarding the variables that were going to interact and predict the SAR; in particular, for males, there is a stronger link toward psychosocial aspects, while for females, the neurocognitive domain is more relevant in its mnestic subcomponents. Network analysis allowed us to describe otherwise less obvious differences in the risk profiles of males and females that attempted to take their own lives. Different neurocognitive and psychosocial variables and different interactions between them predict the probability of suicide attempt unique to male and female patients.

Emotional, Behavioral, and Physical Health Consequences in Caregivers of Children with Cancer: A Network Analysis Differentiation in Mothers' and Fathers' Reactivity.

BACKGROUND: Pediatric cancer presents mental and physical challenges for patients and their caregivers. However, parental distress has been understudied despite its negative impact on quality of life, disability, and somatic disorders. Parents of oncopediatric patients experience high levels of suffering with their resilience tested throughout their children's illness. Identifying at-risk parents and offering specific treatments is crucial and urgent to prevent or alleviate negative outcomes. METHODS: This study used statistical and network analyses to examine symptom patterns assessed by the Kellner Symptom Questionnaire in 16 fathers and 23 mothers at different time points: diagnosis, treatment, and discharge. RESULTS: The results indicated significantly higher distress levels in parents of oncopediatric children compared to the control reference population. Gender-specific differences in symptom profiles were observed at each time point, and symptoms showed a gradual but non-significant decrease over time. CONCLUSIONS: The network analysis yielded valuable insights that, when applied in clinical practice, can guide the implementation of timely treatments to prevent and manage parental distress, thus addressing long-term, stress-related issues in primary caregivers of children diagnosed and treated for cancer.

Predictors of functional outcome in patients with major depression and bipolar disorder: A dynamic network approach to identify distinct patterns of interacting symptoms.

The purpose of this study is to use a dynamic network approach as an innovative way to identify distinct patterns of interacting symptoms in patients with Major Depressive Disorder (MDD) and patients with Bipolar Type I Disorder (BD). More precisely, the hypothesis will be testing that the phenotype of patients is driven by disease specific connectivity and interdependencies among various domains of functioning even in the presence of underlying common mechanisms. In a prospective observational cohort study, hundred-forty-three patients were recruited at the Psychiatric Clinic "Villa dei Gerani" (Catania, Italy), 87 patients with MDD and 56 with BD with a depressive episode. Two nested sub-groups were treated for a twelve-week period, which allowed us to explore differences in the pattern of symptom distribution (central vs. peripheral) and their connectedness (strong vs weak) before (T0) and after (T1) treatment. All patients underwent a complete neuropsychological evaluation at baseline (T0) and at T1. A network structure was computed for MDD and BD patients at T0 and T1 from a covariance matrix of 17 items belonging to three domains-neurocognitive, psychosocial, and mood-related (affective) to identify what symptoms were driving the networks. Clinically relevant differences were observed between MDD and BD, at T0 and after 12 weeks of pharmacological treatment. At time T0, MDD patients displayed an affective domain strongly connected with the nodes of psychosocial functioning, while direct connectivity of the affective domain with the neurocognitive cluster was absent. The network of patients with BD, in contrast, revealed a cluster of highly interconnected psychosocial nodes but was guided by neurocognitive functions. The nodes related to the affective domain in MDD are less connected and placed in the periphery of the networks, whereas in BD they are more connected with psychosocial and neurocognitive nodes. Noteworthy is that, from T0 to T1 the "Betweenness" centrality measure was lower in both disorders which means that fewer "shortest paths" between nodes pass through the affective domain. Moreover, fewer edges were connected directly with the nodes in this domain. In MDD patients, pharmacological treatment primarily affected executive functions which seem to improve with treatment. In contrast, in patients with BD, treatment resulted in improvement of overall connectivity and centrality of the affective domain, which seems then to affect and direct the overall network. Though different network structures were observed for MDD and BD patients, data suggest that treatment should include tailored cognitive therapy, because improvement in this central domain appeared to be fundamental for better outcomes in other domains. In sum, the advantage of network analysis is that it helps to predict the trajectory of future phenotype related disease manifestations. In turn, this allows new insights in how to balance therapeutic interventions, involving different fields of function and combining pharmacological and non-pharmacological treatment modalities.