Commentary: "Consistent Superiority of Selective Serotonin Reuptake Inhibitors Over Placebo in Reducing Depressed Mood in Patients with Major Depression"

Symptom networks analysis among people with Meniere's disease: Application for nursing care

Objectives

This study aimed to explore and visualize the relationships among multiple symptoms in patients with Meniere's disease (MD) and aid clinical nurses in the design of accurate, individualized interventions.

Methods

This study included 790 patients with MD at the Eye and ENT Hospital of Fudan University from October 2014 to December 2021. A self-designed symptom checklist was used to assess 15 MD-related symptoms and construct contemporaneous networks with all 15 symptoms in R software. Qgraph package and Fruchterman-Reingold layout were used for network visualization. Bootstrapping methods were performed to assess network accuracy and stability, and three centrality indices were adopted to describe relationships among symptoms.

Results

Symptom networks showed good accuracy and stability. "Anxiety and nervousness"(98.2%), "aural fullness"(84.4%) and "tinnitus"(82.7%) were the common symptom in MD patients, while "tinnitus", "aural fullness" and "decline in word recognition", were more serious. MD patients with longer disease duration had higher prevalence and severity for all symptoms (P < 0.05). Symptom networks showed good accuracy and stability. "Decline in word recognition," "fatigue," and "anxiety and nervousness" were at the center of the symptom networks, which had the largest strength values and closeness. "Decline in word recognition," "headache," and "spatial discrimination and poor orientation" were the symptoms with the highest betweenness with the strongest bridging effect. The ≥1-year disease group exhibited higher centralities for "drop attack" and "anxiety and nervousness," and a lower centrality for "headache" compared with the <1-year disease group.

Conclusions

The symptom networks of MD patients with varying disease durations were revealed. Clinicians and nurses must provide precision interventions tailored to modifying symptom severity and centrality. Nursing interventions should focus on word recognition issues and associated discomfort in MD patients with multiple symptoms.

Network evolution of core symptoms after lung cancer thoracoscopic surgery:A dynamic network analysis

OBJECTIVES

To investigate relationships between various symptoms occurring 1-2 and 5-6 days following days after thoracoscopic surgery, to identify core symptoms, and to monitor changes in core symptoms over time following lung cancer thoracoscopic surgery.

METHODS

We evaluated symptoms using the Anderson Symptom Scale (Chinese version) and the Lung Cancer-Specific Symptoms Template in 214 lung cancer patients hospitalized in the Department of Thoracic Surgery of a provincial hospital in Jiangsu Province from March 2023 to September 2023. Data was collected at 1-2 days and 5-6 days postoperatively. Symptom networks were constructed for each time point, and centrality indicators were analyzed to identify core symptoms while controlling for influencing factors.

RESULTS

According to the network analysis, fatigue (rs = 26.00、rc = 0.05、rb = 1.02) had the highest strength, closeness, and betweenness in the symptom network 1-2 days after lung cancer surgery. At 5-6 days after surgery, shortness of breath (rs = 27.00) emerged as the symptom with the highest strength, fatigue (rc = 0.04) had the highest closeness, and cough (rb = 1.08) ranked highest in betweenness within the symptom network.

CONCLUSION

Fatigue stands out as the most core symptom in the network 1-2 days after lung cancer surgery. Shortness of breath, fatigue and cough are the most core symptoms in the symptom network 5-6 days after surgery. Therefore, clinical staff can improve the postoperative symptom experience of lung cancer patients by developing symptom management programmes tailored to these core symptoms.

Exploring the dynamics of perioperative symptom networks in colorectal cancer patients: a cross-lagged panel network analysis

BACKGROUND

Colorectal cancer incidence is on the rise, necessitating precise symptom management. However, causal relationships among symptoms have been challenging to establish due to reliance on cross-sectional data. Cross-lagged panel network (CLPN) analysis offers a solution, leveraging longitudinal data for insight.

OBJECTIVE

We employed CLPN analysis to construct symptom networks in colorectal cancer patients at three perioperative time points, aiming to identify predictive relationships and intervention opportunities.

METHODS

We evaluated the prevalence and severity of symptoms throughout the perioperative period, encompassing T1 the first day of admission, T2 2-3 days postoperatively, and T3 discharge, utilizing the M. D. Anderson Symptom Inventory Gastrointestinal Cancer Module (MDASI-GI). To identify crucial nodes in the network and explore predictive and interactive effects among symptoms, CLPNs were constructed from longitudinal data in R.

RESULTS

The analysis revealed a stable network, with disturbed sleep exhibiting the highest out-EI (outgoing expected influence) during T1. Distress had a sustained impact throughout the perioperative. Disturbed sleep at T1 predicted T2 bloating, fatigue, distress, and pain. T1 distress predicted T2 sadness severity. T2 distress primarily predicted T3 fatigue, disturbed sleep, changes in taste, and bloating. T2 shortness of breath predicted T3 changes in taste and loss of appetite. Furthermore, biochemical markers like RBC and ALB had notable influence on symptom clusters during T1→T2 and T2→T3, respectively.

CONCLUSION

Prioritizing disturbed sleep during T1 and addressing distress throughout the perioperative phase is recommended. Effective symptom management not only breaks the chain of symptom progression, enhancing healthcare impact, but also eases patient symptom burdens.

Understanding of Depressive Symptomatology across Major Depressive Disorder and Bipolar Disorder: A Network Analysis

Background and Objectives: Depressive symptoms are prominent in both major depressive disorder (MDD) and bipolar disorder (BD). However, comparative research on the network structure of depressive symptoms in these two diagnostic groups has been limited. This study aims to compare the network structure of depressive symptoms in MDD and BD, providing a deeper understanding of the depressive symptomatology of each disorder. Materials and Methods: The Zung Self-Rating Depressive Scale, a 20-item questionnaire, was administered to assess the depressive symptoms in individuals with MDD (n = 322) and BD (n = 516). A network analysis was conducted using exploratory graph analysis (EGA), and the network structure was analyzed using regularized partial correlation models. To validate the dimensionality of the Zung SDS, principal component analysis (PCA) was adopted. Centrality measures of the depressive symptoms within each group were assessed, followed by a network comparison test between the two groups. Results: In both diagnostic groups, the network analysis revealed four distinct categories, aligning closely with the PCA results. "Depressed affect" emerged as the most central symptom in both MDD and BD. Furthermore, non-core symptoms, "Personal devaluation" in MDD and "Confusion" in BD, displayed strong centrality. The network comparison test did not reveal significant differences in the network structure between MDD and BD. Conclusions: The absence of significant differences in the network structures between MDD and BD suggests that the underlying mechanisms of depressive symptoms may be similar across these disorders. The identified central symptoms, including "Depressed affect", in both disorders and the distinct non-core symptoms in each highlight the complexity of the depressive symptomatology. Future research should focus on validating these symptoms as therapeutic targets and incorporate various methodologies, including non-metric dimension reduction techniques or canonical analysis.

Network structure of symptomatology of adult attention-deficit hyperactivity disorder in patients with mood disorders

Patients with mood disorders commonly manifest comorbid psychiatric disorders, including attention-deficit/hyperactivity disorder (ADHD). However, few studies have evaluated ADHD symptoms in this population. The current study aimed to explore the network structure of ADHD symptomology and identify central symptoms in patients with mood disorders. The Korean version of the Adult ADHD Self-Report Scale was used to assess the overall ADHD symptoms in 1,086 individuals diagnosed with mood disorders (major depressive disorder [n = 373], bipolar I disorder [n = 314], and bipolar II disorder [n = 399]). We used exploratory graph analysis to detect the number of communities, and the network structure was analyzed using regularized partial correlation models. We identified the central ADHD symptom using centrality indices. Network comparison tests were conducted with different subgroups of patients with mood disorders, including three mood diagnosis groups, between the patients who met the diagnostic criteria for ADHD [ADHD-suspected, n = 259] in their self-report and the others [ADHD-non-suspected, n = 827], and groups with high [n = 503] versus low [n = 252] levels of depressive state. The network analysis detected four communities: disorganization, agitation/restlessness, hyperactivity/impulsivity, and inattention. The centrality indices indicated that "feeling restless" was the core ADHD symptom. The result was replicated in the subgroup analyses within our clinically diverse population of mood disorders, encompassing three presentations: Patients with suspected ADHD, patients without suspected ADHD, and patients with a high depressive state. Our findings reveal that "feeling restless" is the central ADHD symptom. The treatment intervention for "feeling restless" may thus play a pivotal role in tackling ADHD symptoms in adult patients with mood disorders.

The effectiveness of exercise on the symptoms in breast cancer patients undergoing adjuvant treatment: an umbrella review of systematic reviews and meta-analyses

Background

Exercise has the potential to reduce symptoms for breast cancer patients during adjuvant treatment, and high-quality systematic reviews are essential for guiding clinical practice. The objective of this umbrella review is to examine current research evidence concerning the effectiveness of exercise on symptom management in breast cancer patients undergoing adjuvant treatment.

Methods

An umbrella review was conducted. We searched for eligible systematic reviews through 11 databases until August 13rd, 2023. Two authors independently screened titles and abstracts, assessing the full-text studies based on inclusion criteria. We used AMSTAR-2 to appraise the quality of the meta-analyses. The results would be presented with narrative summaries if the replication rate of the original study for a symptom was higher than 5% (calculated via the Corrected Covered Area, CCA). The protocol was documented in the PROSPERO registry (CRD42023403990).

Results

Of the 807 systematic reviews identified, 15 met the inclusion criteria, and 7 symptoms were the main focus. The main form of exercise mentioned was aerobic combined resistance exercise. The results of the quality assessment were mostly critically low (10/15). The repetition rate calculated by CCA showed moderate to very high repetition rates (10% to 18.6%). The findings of the included reviews indicated that the effects of exercise on relieving symptoms during breast cancer adjuvant treatment were mixed.

Conclusions

Research is still needed to confirm the majority of studies' recommendations for exercise during adjuvant treatment for breast cancer patients, as it is crucial for managing symptoms in the rehabilitation process. To increase the efficiency of exercise in symptom management, future studies may focus more on the application of bridge symptoms, symptom networks, and ecological instantaneous assessment.

Response trajectories during escitalopram treatment of patients with major depressive disorder

Depression is a leading global cause of disability, yet about half of patients do not respond to initial antidepressant treatment. This treatment difficulty may be in part due to the heterogeneity of depression and corresponding response to treatment. Unsupervised machine learning allows underlying patterns to be uncovered, and can be used to understand this heterogeneity by finding groups of patients with similar response trajectories. Prior studies attempting this have clustered patients using a narrow range of data primarily from depression scales. In this work, we used unsupervised machine learning to cluster patients receiving escitalopram therapy using a wide variety of subjective and objective clinical features from the first eight weeks of the Canadian Biomarker Integration Network in Depression-1 trial. We investigated how these clusters responded to treatment by comparing changes in symptoms and symptom categories, and by using Principal Component Analysis (PCA). Our algorithm found three clusters, which broadly represented non-responders, responders, and remitters. Most categories of features followed this response pattern except for objective cognitive features. Using PCA with our clusters, we found that subjective mood state/anhedonia is the core feature of response with escitalopram, but there exists other distinct patterns of response around neurovegetative symptoms, activation, and cognition.

The relationship between alexithymia, depression, anxiety, and stress in elderly with multiple chronic conditions in China: a network analysis

Objective

This study aimed to construct a network structure to investigate the connections between alexithymia, depression, anxiety, and stress in Chinese older adults with multiple chronic conditions (MCC), identifying core and bridge symptoms, and comparing the network structure across different levels of alexithymia.

Methods

This study used a cross-sectional survey design and convenience sampling to recruit participants from six cities in Jiangsu Province. The study assessed the levels of alexithymia, depression, anxiety, and stress in older adults with MCC using the Toronto Alexithymia Scale (TAS-20) and the Depression Anxiety and Stress Scale-21 (DASS-21). Network analysis was performed using R language to identify core and bridge symptoms in the network and compare the network structure across different levels of alexithymia.

Results

A total of 662 participants were included in the analysis, including 395 men and 267 women. The mean age was 70.37 ± 6.92 years. The finding revealed that the "Difficulty Identifying Feelings" (DIF) node had the highest strength centrality (strength = 2.49) and predictability (rp = 0.76) in the network. The next highest strength centrality was observed for "Meaningless" (strength = 1.50), "Agitated" (strength = 1.47), "Scared" (strength = 1.42), and "No look forward" (strength = 0.75). They were identified as core symptoms. The bridge strength analysis identified "Panic," "Scared," "No wind down," "No initiative," and "No positive" as the bridge symptoms. There were notable differences in the overall network structure and specific connections between the groups with and without alexithymia (p < 0.05).

Conclusion

"DIF" is a core node in the network of older adults with MCC, indicating its significance as a potential target for psychological interventions in clinical practice. Preventing and mitigating bridge symptoms such as "panic," "Scared," "No wind down," "No initiative," and "No positive" can effectively impede the spread of symptom activation, thereby interrupting or severing the connections among comorbidities in older adults. Additionally, compared to non-alexithymia individuals, the psychological issues of older adults with alexithymia require prioritized intervention from healthcare professionals.

Exploring bridge symptoms in HIV-positive people with comorbid depressive and anxiety disorders

BACKGROUND

The prevalence of comorbid depressive and anxiety disorders in people living with HIV (PLWH) is high. However, it is unclear which symptom is the bridge symptom between depression and anxiety in PLWH. This study aimed to develop symptom networks for depression and anxiety and explore the bridge symptoms and interconnectedness between these disorders in PLWH with comorbid depressive and anxiety disorders.

METHODS

A multisite, hospital-based cross-sectional study was conducted from March 2020 to November 2021. Depression and anxiety were measured with the Hospital Anxiety and Depression Scale. We visualized the symptom network using the qgraph package and computed the bridge expected influence of each node. The GLASSO layout was used to generate undirected association networks.

RESULTS

A total of 2016 individuals were included in the analysis. In the anxiety cluster, "not feeling relaxed" had the highest bridge expected influence and strength (r_{bridge expected influence} = 0.628, r_strength = 0.903). In the depression cluster, "not feeling cheerful" was identified as having a high bridge expected influence (r_{bridge expected influence} = 0.385). "Not feeling cheerful" and "not feeling relaxed" were the strongest edges across the depression and anxiety clusters (r = 0.30 ± 0.02).

CONCLUSIONS

Healthcare professionals should take note when PLWH report severe bridge symptoms. To enhance the levels of perceived cheerfulness and relaxation, positive psychology interventions could be implemented.

The course of symptoms in the first 27 months following bereavement: A latent trajectory analysis of prolonged grief, posttraumatic stress, and depression

BACKGROUND

Much remains unknown about the course of grief in the early months following bereavement, including the prevalence and timing of a recovery trajectory, whether specific symptoms mark a failure to recover, and the co-occurrence of chronic prolonged grief (PG), posttraumatic stress (PTS) and depression symptoms.

METHODS

Two hundred fifty-nine participants completed PG, PTS and depression questionnaires up to eleven times every six weeks during the two years post-bereavement. We used Latent Class Growth Mixture Modeling (LCGMM) to identify subgroups of bereaved individuals sharing similar trajectories for each disorder. We used repeated measures ANOVA to evaluate differences in individual symptoms between trajectories. Finally, we investigated to what extent chronic trajectories of these three disorders co-occurred.

RESULTS

Three trajectories of PG symptoms emerged: resilient (66.4%), chronic (25.1%) and acute recovery (8.4%). The overall severity and symptom profile of the acute recovery group were indistinguishable from that of the chronic group through 6 months post-bereavement, followed by reduction in PG from 6 to 18 months post-bereavement. Chronic PTS in the first-year post-bereavement tended to co-occur with chronic PG and/or chronic depression.

CONCLUSIONS

Twenty five percent of those with initial elevations in grief recovered in the period of 6 to 12 months post-bereavement. These findings highlight the clinical importance of severe grief in the initial months following loss, but also suggests caution in diagnosing a grief disorder within the first-year post-bereavement.

Psychosocial well-being among veterans with posttraumatic stress disorder and substance use disorder

OBJECTIVE

Concurrent posttraumatic stress disorder and substance use disorder (PTSD/SUD) in U.S. military veterans represents an urgent public health issue associated with significant clinical challenges. Although previous research has shown that veterans with PTSD/SUD endorse more psychosocial risk factors and fewer protective factors than veterans with neither or only one of these disorders, no study has applied a comprehensive framework to characterize the vocational, financial, and social well-being of veterans with PTSD/SUD. Furthermore, it is not fully known how well-being among veterans with PTSD/SUD compares to that of veterans with posttraumatic stress disorder (PTSD) only, substance use disorder (SUD) only, or neither disorder.

METHOD

This cross-sectional observational study analyzed data from the National Post-Deployment Adjustment Survey, which recruited a random national sample of U.S. veterans who served on/after September 11, 2001. Participants (weighted N = 1,102) self-reported sociodemographic, clinical, and military background information in addition to aspects of their vocational, financial, and social well-being.

RESULTS

Veterans with PTSD/SUD were particularly likely to report lifetime experiences of homelessness, violent behavior, suicidal ideation, and suicide attempts. Veterans with PTSD/SUD reported worse social well-being than the PTSD-only, SUD-only, and neither-disorder groups. They also reported worse vocational and financial well-being than veterans with SUD only or with neither disorder but did not significantly differ from the PTSD-only group on vocational or financial well-being.

CONCLUSIONS

The findings underscore the importance of assessing multiple aspects of well-being in veterans with PTSD and/or SUD. The findings also point to promising treatment targets to improve psychosocial functioning and overall quality of life among veterans with PTSD and/or SUD. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Network Analysis of Eating Disorders Symptoms Co-occurring With Impulsive Personality Traits and Negative Mood States in Patients With Bulimia Nervosa

BACKGROUND

Bulimia nervosa (BN) is characterized by recurrent episodes of eating large amounts of food without control. Studies have found positive correlations of BN symptoms with impulsive traits and negative affect. However, the network relationship supporting BN symptoms is unclear.

METHODS

The study participants included female BN patients (146) and healthy controls (HCs, 146). The participants were matched for age. All participants completed the Eating Disorder Examination Questionnaire 6.0, Barratt Impulsiveness Scale-11, Beck Anxiety Inventory, and Beck Depression Inventory. We characterized the centrality parameters of BN, impulsiveness, and anxiety and depression symptoms of BN patients compared with HCs.

RESULTS

Among all symptoms in the constructed BN group network, Shape dissatisfaction had the highest strength. In the BN group network, three clusters of symptoms ("ED-specific symptoms," "impulsivity," and "anxiety and depression") were linked to each other by several symptoms. Compared to the HC network, impulsiveness was strongly associated with Concerns about Others Seeing One Eat in the BN network.

CONCLUSION

This study shows that ED-specific symptoms, i.e., Shape dissatisfaction, play a key role in BN. The cognition of "shape dissatisfaction" is a basis, and impulsivity and emotional symptoms are maintaining factors that may lead to BN development.

Caught in a web of trauma: Network analysis of childhood adversity and adult mental ill-health

BACKGROUND

Current interventions for adverse childhood experiences have only limited effectiveness.

OBJECTIVE

We sought to identify optimal targets for the development of new interventions against adverse childhood experiences (ACE), that is, ACEs that a) are so central in the network of childhood adversity that curbing them is likely to impact other types of adversity, too, and b) are so central to the link of childhood adversity and adult mental ill-health that curbing them is likely to prevent this negative long-term effect from developing.

PARTICIPANTS AND SETTING

384 adult psychiatric inpatients.

METHODS

Using the R packages qgraph and IsingFit, we analyzed the ACE network and the common network of ACEs and adult mental disorders.

RESULTS

We found two clusters of ACEs: direct interactions with the child and indirect traumatization via adverse circumstances. When controlling for interrelatedness, the associations of sexual abuse with posttraumatic stress disorder and borderline personality disorder were the only direct links between ACEs and adult mental disorders.

CONCLUSIONS

As neglect and violence against the mother were the most influential ACEs, curbing them is likely to destabilize the whole network of adversity. Thus, neglect and violence against the mother lend themselves as candidate targets for the development of new interventions. As sexual abuse was the only link between childhood adversity and adult mental ill-health, interventions against it seem most likely to keep this negative long-term effect from developing. Further, ideally prospective, research is needed to corroborate these findings.

The network approach to psychopathology: a review of the literature 2008-2018 and an agenda for future research

The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.

Network Analysis of Posttraumatic Stress Disorder Symptoms in a National Sample of U.S. Adults: Implications for the Phenotype and the ICD-11 Model of PTSD

The phenotype for posttraumatic stress disorder (PTSD) in the fifth edition of the Diagnostic and Statistical Manual of Mental Diseases (DSM-5) includes 20 symptoms in four clusters. In contrast, the PTSD model in the 11th revision of the International Classification of Diseases (ICD-11) includes six symptoms in three clusters. Whether those six symptoms are, in fact, the most central symptoms of the PTSD phenotype remains an open question. In a previous network analysis of DSM-5 PTSD symptoms, Mitchell and colleagues (2017) reported limited overlap between central PTSD symptoms and those in the ICD-11 model in a national sample of U.S. veterans. The present study sought to replicate and extend upon these findings in a large national sample of U.S. adults (N = 2,953). Centrality statistics from both a replication sample (i.e., participants with DSM-5 PTSD, n = 173) and an extension sample (i.e., participants who had been exposed to potentially traumatic events, n = 2,468) were moderately strongly convergent with the findings reported by Mitchell et al., rs = .54-.73. Additionally, only three of the six most central symptoms in both the replication and extension samples overlapped with the ICD-11 model, indicating that the ICD-11 model (a) failed to include network-central symptoms of the PTSD phenotype and (b) included extra symptoms that were not network-central. Several symptoms from the DSM-5 Criterion D cluster (negative alterations in cognition and mood) that were excluded in ICD-11 were found to be among the most central PTSD symptoms.

Symptomatology following loss and trauma: Latent class and network analyses of prolonged grief disorder, posttraumatic stress disorder, and depression in a treatment-seeking trauma-exposed sample

BACKGROUND

Although bereavement is likely a common stressor among patients referred to a psychotrauma clinic, no study has yet examined the co-occurrence and relationships between symptoms of prolonged grief disorder (PGD), posttraumatic stress disorder (PTSD), and major depressive disorder symptoms in this population.

METHOD

In a sample of patients seeking treatment following psychological trauma (n = 458), we used latent class analysis to identify classes of patients sharing the same profile of PGD, PTSD, and depression symptoms. We then used network analysis to investigate the relationships among these symptoms and with loss-related variables.

RESULTS

Most participants (65%) were members of a class that exhibited elevated endorsement of PGD symptoms. PGD, PTSD, and depression symptoms hung together as highly overlapping but distinguishable communities of symptoms. Symptoms related to social isolation and diminished sense of self bridged these communities. Violent loss was associated with more difficulty accepting the loss. The loss of close kin was most strongly associated with difficulty moving on in life.

CONCLUSIONS

PGD symptoms are common in trauma-exposed bereaved adults and closely associated with symptoms of PTSD and depression, illustrating the importance of assessing bereavement and PGD symptoms in those seeking treatment following trauma.

Does mindfulness change the mind? A novel psychonectome perspective based on Network Analysis

If the brain is a complex network of functionally specialized areas, it might be expected that mental representations could also behave in a similar way. We propose the concept of ‘psychonectome’ to formalize the idea of psychological constructs forming a dynamic network of mutually dependent elements. As a proof-of-concept of the psychonectome, networks analysis (NA) was used to explore structural changes in the network of constructs resulting from a psychological intervention. NA was applied to explore the effects of an 8-week Mindfulness-Based Stress Reduction (MBSR) program in healthy participants (N = 182). Psychological functioning was measured by questionnaires assessing five key domains related to MBSR: mindfulness, compassion, psychological well-being, psychological distress and emotional-cognitive control. A total of 25 variables, covering the five constructs, were considered as nodes in the NA. Participants significantly improved in most of the psychological questionnaires. More interesting from a network perspective, there were also significant changes in the topological relationships among the elements. Expected influence and strength centrality indexes revealed that mindfulness and well-being measures were the most central nodes in the networks. The nodes with highest topological change after the MBSR were attentional control, compassion measures, depression and thought suppression. Also, cognitive appraisal, an adaptive emotion regulation strategy, was associated to rumination before the MBSR program but became related to mindfulness and well-being measures after the program. Community analysis revealed a strong topological association between mindfulness, compassion, and emotional regulation, which supports the key role of compassion in mindfulness training. These results highlight the importance of exploring psychological changes from a network perspective and support the conceptual advantage of considering the interconnectedness of psychological constructs in terms of a ‘psychonectome’ as it may reveal ways of functioning that cannot be analyzed through conventional analytic methods.

Development of an underivatized LC-MS/MS method for quantitation of 14 neurotransmitters in rat hippocampus, plasma and urine: Application to CUMS induced depression rats

Sensitive and comprehensive measurement of systemic metabolites of tryptophan, phenylalanine and glutamate metabolism in biological samples is effective for understanding the pathogenesis of depression and other neurological diseases. Therefore, this study developed an underivatized liquid chromatography tandem mass spectrometry (LC-MS/MS) method for simultaneous monitoring the 3 components of glutamate metabolism in rat hippocampus and 11 components of tryptophan and phenylalanine metabolism in rat hippocampus, plasma and urine, and applied it to investigate their changes in rats induced by chronic unpredictable mild stress (CUMS). The investigated analytes are as follows: tryptophan, serotonin, 5-hydroxyindoleacetic acid, kynurenine, kynurenic acid, xanthurenic acid, 3-hydroxyanthranilic acid, quinolinic acid, phenylalanine, tyrosine, tyramine, glutamate, glutamine and gamma-aminobutyric acid. The method was verified to be sensitive and effective with satisfactory linearity, accuracies in the range of 78.2%-120.4%, and precisions less than 17.8% for all identified analytes. A series of significant changes in CUMS-induced rats can be detected: tryptophan, serotonin and tyrosine levels decreased and quinolinic acid increased in both hippocampus and plasma. In addition, the kynurenine/tryptophan ratios increased in hippocampus and plasma, the kynurenic acid/quinolinic acid ratios of plasma and urine were significantly reduced. These findings demonstrated that the CUMS procedure could lead to the central and peripheral imbalances of tryptophan and phenylalanine metabolism. In conclusion, a LC-MS/MS method for simultaneous measurement of several neurotransmitters in rat hippocampus, plasma and urine was developed and successfully applied to investigation of the central and peripheral changes in CUMS-induced rats. The method would be expected to provide applicability to the study of the mechanisms of depression and other related diseases associated with these neurotransmitters.

Testing Cold and Hot Cognitive Control as Moderators of a Network of Comorbid Psychopathology Symptoms in Adolescence

Comorbidity is pervasive across psychopathological symptoms, diagnoses, and domains. Network analysis is a method for investigating symptom-level associations that underlie comorbidity, particularly through bridge symptoms connecting diagnostic syndromes. We applied network analyses of comorbidity to data from a population-based sample of adolescents (n = 849). We implemented a method for assessing nonparametric moderation of psychopathology networks to evaluate differences in network structure across levels of intelligence and emotional control. Symptoms generally clustered by clinical diagnoses, but specific between-cluster bridge connections emerged. Internalizing symptoms demonstrated unique connections with aggression symptoms of interpersonal irritability, whereas externalizing symptoms showed more diffuse interconnections. Aggression symptoms identified as bridge nodes in the cross-sectional network were enriched for longitudinal associations with internalizing symptoms. Cross-domain connections did not significantly vary across intelligence but were weaker at lower emotional control. Our findings highlight transdiagnostic symptom relationships that may underlie co-occurrence of clinical diagnoses or higher-order factors of psychopathology.

The symptom-specific efficacy of antidepressant medication vs. cognitive behavioral therapy in the treatment of depression: results from an individual patient data meta-analysis

A recent individual patient data meta-analysis showed that antidepressant medication is slightly more efficacious than cognitive behavioral therapy (CBT) in reducing overall depression severity in patients with a DSM-defined depressive disorder. We used an update of that dataset, based on seventeen randomized clinical trials, to examine the comparative efficacy of antidepressant medication vs. CBT in more detail by focusing on individual depressive symptoms as assessed with the 17-item Hamilton Rating Scale for Depression. Five symptoms (i.e., "depressed mood" , "feelings of guilt" , "suicidal thoughts" , "psychic anxiety" and "general somatic symptoms") showed larger improvements in the medication compared to the CBT condition (effect sizes ranging from .13 to .16), whereas no differences were found for the twelve other symptoms. In addition, network estimation techniques revealed that all effects, except that on "depressed mood" , were direct and could not be explained by any of the other direct or indirect treatment effects. Exploratory analyses showed that information about the symptom-specific efficacy could help in identifying those patients who, based on their pre-treatment symptomatology, are likely to benefit more from antidepressant medication than from CBT (effect size of .30) versus those for whom both treatments are likely to be equally efficacious. Overall, our symptom-oriented approach results in a more thorough evaluation of the efficacy of antidepressant medication over CBT and shows potential in "precision psychiatry" .

When All Else Fails, Listen to the Patient: A Viewpoint on the Use of Ecological Momentary Assessment in Clinical Trials

A major problem in mental health clinical trials, such as depression, is low assay sensitivity in primary outcome measures. This has contributed to clinical trial failures, resulting in the exodus of the pharmaceutical industry from the Central Nervous System space. This reduced assay sensitivity in psychiatry outcome measures stems from inappropriately broad measures, recall bias, and poor interrater reliability. Limitations in the ability of traditional measures to differentiate between the trait versus state-like nature of individual depressive symptoms also contributes to measurement error in clinical trials. In this viewpoint, we argue that ecological momentary assessment (EMA)-frequent, real time, in-the-moment assessments of outcomes, delivered via smartphone-can both overcome these psychometric challenges and reduce clinical trial failures by increasing assay sensitivity and minimizing recall and rater bias. Used in this manner, EMA has the potential to further our understanding of treatment response by allowing for the assessment of dynamic interactions between treatment and distinct symptom response.

The centrality of affective instability and identity in Borderline Personality Disorder: Evidence from network analysis

We argue that the series of traits characterizing Borderline Personality Disorder samples do not weigh equally. In this regard, we believe that network approaches employed recently in Personality and Psychopathology research to provide information about the differential relationships among symptoms would be useful to test our claim. To our knowledge, this approach has never been applied to personality disorders. We applied network analysis to the nine Borderline Personality Disorder traits to explore their relationships in two samples drawn from university students and clinical populations (N = 1317 and N = 96, respectively). We used the Fused Graphical Lasso, a technique that allows estimating networks from different populations separately while considering their similarities and differences. Moreover, we examined centrality indices to determine the relative importance of each symptom in each network. The general structure of the two networks was very similar in the two samples, although some differences were detected. Results indicate the centrality of mainly affective instability, identity, and effort to avoid abandonment aspects in Borderline Personality Disorder. Results are consistent with the new DSM Alternative Model for Personality Disorders. We discuss them in terms of implications for therapy.

Modeling Psychological Attributes in Psychology - An Epistemological Discussion: Network Analysis vs. Latent Variables

Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of latent variable models by engaging in an ontological and epistemological discussion on psychological attributes. Psychological attributes relate to the mental equilibrium of individuals embedded in their social interactions, as robust attractors within complex dynamic processes with emergent properties, distinct from physical entities located in precise areas of the brain. Latent variables thus possess legitimacy, because the emergent properties can be conceptualized and analyzed on the sole basis of their manifestations, without exploring the upstream complex system. However, in opposition with the usual Latent Variable models, this article is in favor of the integration of a dynamic system of manifestations. Latent Variables models and Network Analysis thus appear as complementary approaches. New approaches combining Latent Network Models and Network Residuals are certainly a promising new way to infer psychological attributes, placing psychological attributes in an inter-subjective dynamic approach. Pragmatism-realism appears as the epistemological framework required if we are to use latent variables as representations of psychological attributes.

The Distinction between Psychological Kinds and Natural Kinds Revisited: Can Updated Natural-Kind Theory Help Clinical Psychological Science and beyond Meet Psychology's Philosophical Challenges?

Philosophers and psychologists have long held that mind-dependent/human (or social) kinds are not natural kinds. Yet in the last three decades philosopher of science Richard Boyd has challenged this belief to widespread acclaim in the philosophy of biology, where the natural-kind status of species taxa has been debated. Boyd proposed that natural-kind status hinges not on a kind's mind independence or on demonstration of its essential properties but rather on whether it supports inductive generalization, in which case it is a “homeostatic property cluster” (HPC) kind. Boyd indicates that any human/mental kind can in principle be a natural kind, without physical reduction of its properties, as long as it constitutes an HPC kind and so can be studied by way of the causal mechanisms that, he theorizes, underlie all natural kinds. In the last decade Boyd's HPC theory of natural kinds has influenced theory of mental disorder kinds and shares commonality with Denny Borsboom's burgeoning “symptom network” approach to psychiatric diagnosis. It therefore warrants more thoroughgoing theoretical and empirical analysis. This article revisits the heterogeneity that inheres in DSM categories and motivated alternative approaches, such as the Research Domain Criteria (RDoC) of the NIMH. Also assessed are two worries about the future of “HPC kinds” of mental disorder kinds: (a) ontological relativism and reification, and (b) epistemic perspectivalism and relativistic knowledge. Though focused on clinical kinds, this analysis has implications for psychological science beyond its clinical subdiscipline.

Mental disorders as networks of problems: a review of recent insights

PURPOSE

The network perspective on psychopathology understands mental disorders as complex networks of interacting symptoms. Despite its recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in the last years.

METHODS

This paper provides a review of all empirical network studies published between 2010 and 2016 and discusses them according to three main themes: comorbidity, prediction, and clinical intervention.

RESULTS

Pertaining to comorbidity, the network approach provides a powerful new framework to explain why certain disorders may co-occur more often than others. For prediction, studies have consistently found that symptom networks of people with mental disorders show different characteristics than that of healthy individuals, and preliminary evidence suggests that networks of healthy people show early warning signals before shifting into disordered states. For intervention, centrality-a metric that measures how connected and clinically relevant a symptom is in a network-is the most commonly studied topic, and numerous studies have suggested that targeting the most central symptoms may offer novel therapeutic strategies.

CONCLUSIONS

We sketch future directions for the network approach pertaining to both clinical and methodological research, and conclude that network analysis has yielded important insights and may provide an important inroad towards personalized medicine by investigating the network structures of individual patients.

Looking back (and in)to the future: A personal reflection on 'Serotonin autoreceptor function and antidepressant drug action' (Hjorth et al., 2000)

Our article in this journal some 15 years ago focussed on the role of serotonin (5-HT) autoreceptors in the mechanism of action of antidepressant drugs. Specifically in this regard, the results were summarised of rat microdialysis studies carried out to examine: (a) the relative importance of 5-HT_1A and 5-HT_1B autoreceptors, including (b) possible regional variation, and (c) potential changes in autoreceptor responsiveness following chronic selective serotonin reuptake inhibitor administration. In the present reflection piece, I recap some of the key findings against a brief background and provide an account of their bearing within the context of subsequent endeavours in the antidepressant drug research and development field. I conclude by shortly commenting on selected topics relevant to novel, interesting advances and avenues for future research.