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  ["project_title"]=>
  string(53) "Network analysis of mixed states in bipolar disorders"
  ["project_narrative_summary"]=>
  string(1655) "Bipolar disorder is a complex mental health condition characterized by episodes of mania, depression, or a mix of both, known as mixed episodes. Understanding the dynamics of symptoms during these episodes is crucial for effective management and treatment. Network analysis offers a novel approach to explore the interconnectedness of symptoms within these episodes.



This study proposes to define the symptomatic network of mixed episodes and compare it with the network of manic episodes in bipolar disorder. We aim to elucidate the unique symptom patterns present in mixed episodes and how are symptomatically developed differently than manic episodes even though they share common symptoms.



Participants will be recruited from open-access clinical trial database diagnosed both mixed and manic episodes in bipolar disorder.



Using network analysis techniques, we will construct symptom networks for both mixed and manic episodes separately. The primary outcomes will include network metrics such as centrality and connectivity to identify key symptoms and their relationships within each episode type. Additionally, we will compare the overall network structure and symptom associations between mixed and manic episodes.



The relevance of this research lies in its potential to enhance our understanding of the distinct symptom dynamics in mixed episodes compared to manic episodes in bipolar disorder. By identifying unique patterns of symptom interaction, we can inform more targeted interventions and personalized treatment approaches for individuals experiencing these challenging episodes."
  ["project_learn_source"]=>
  string(9) "colleague"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(5) "Sergi"
    ["last_name"]=>
    string(8) "Salmeron"
    ["degree"]=>
    string(4) "M.D."
    ["primary_affiliation"]=>
    string(105) "Department of Psychiatry and Psychology, Institute of Neurosciences, Hospital Clínic de Barcelona, Spain"
    ["email"]=>
    string(20) "ssalmeron@clinic.cat"
    ["state_or_province"]=>
    string(9) "Barcelona"
    ["country"]=>
    string(7) "España"
  }
  ["project_key_personnel"]=>
  array(1) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(5) "Sergi"
      ["p_pers_l_name"]=>
      string(8) "Salmeron"
      ["p_pers_degree"]=>
      string(4) "M.D."
      ["p_pers_pr_affil"]=>
      string(105) "Department of Psychiatry and Psychology, Institute of Neurosciences, Hospital Clínic de Barcelona, Spain"
      ["p_pers_scop_id"]=>
      string(38) " https://orcid.org/0000-0002-0212-4491"
      ["requires_data_access"]=>
      string(3) "yes"
    }
  }
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(2) "no"
    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1967) "Background

Bipolar disorder is a complex mental health condition characterized by episodes of mania, depression, or a mix of both, known as mixed episodes. Understanding the dynamics of symptoms during these episodes is crucial for effective management and treatment. Network analysis offers a novel approach to explore the interconnectedness of symptoms within these episodes.



Objective

This study proposes to define the symptomatic network of mixed episodes and compare it with the network of manic episodes in bipolar disorder. We aim to elucidate the unique symptom patterns present in mixed episodes and how are symptomatically developed differently than manic episodes even though they share common symptoms.



Study design

Participants diagnosed with bipolar I or II disorder experiencing acute mixed or manic episodes will be recruited from open-access clinical trial databases. Network analysis techniques will be employed to construct symptom networks for mixed and manic episodes separately.



Participants

Participants will be recruited from open-access clinical trial database diagnosed both mixed and manic episodes in bipolar disorder.



Primary and Secondary Outcome Measure(s)

The primary outcome measure involves characterizing the network structure of symptoms during mixed episodes compared to manic episodes. Key network metrics, including centrality and connectivity, will be calculated. Secondary outcome measures include symptom severity assessed using validated rating scales at baseline during mixed and manic episodes.



Statistical Analysis

Statistical analyses will involve assessing trends in symptom endorsement, estimating network parameters using the Ising model, and comparing network structures between diagnostic groups using the Network Comparison Test. Network visualization will be performed using the R-package qgraph."
  ["project_brief_bg"]=>
  string(1949) "Bipolar disorder is a debilitating mental health condition characterized by episodes of mania, depression, or mixed states. While much research has focused on understanding manic and depressive episodes separately, mixed episodes present a unique challenge due to their combination of symptoms from both poles of the disorder. Traditional diagnostic criteria often struggle to capture the complexity of mixed episodes, leading to misdiagnosis and inadequate treatment. Network analysis, a novel approach in psychiatric research, offers a promising avenue to explore the dynamic interactions among symptoms within these episodes.

Understanding the distinct symptom patterns and interactions during mixed episodes, and its relation to manic episodes is crucial for advancing our understanding of bipolar disorder and improving clinical management. By elucidating the network structure of symptoms during mixed episodes, we can identify key symptoms and their relationships, potentially leading to more accurate diagnosis and tailored treatment strategies. Additionally, this research may shed light on the underlying mechanisms driving mixed episodes, informing the development of targeted interventions to alleviate symptom burden and improve long-term outcomes for individuals with bipolar disorder.



The insights gained from this research will contribute to generalizable scientific and medical knowledge by providing a deeper understanding of the symptom dynamics specific to mixed episodes in bipolar disorder. This knowledge can inform future research endeavors aimed at refining diagnostic criteria, developing novel therapeutic approaches, and enhancing clinical practice guidelines. Ultimately, the application of network analysis in studying mixed episodes has the potential to transform how we conceptualize and manage bipolar disorder, leading to improved outcomes and quality of life for affected individuals.

"
  ["project_specific_aims"]=>
  string(1107) "1. To characterize the network structure of symptoms during mixed episodes compared to manic episodes in individuals with bipolar disorder.

   - Utilize network analysis techniques to construct symptom networks for mixed episodes and manic episodes separately.

   - Identify key symptoms and their centrality within each episode type.

   - Compare the overall network structure and symptom associations between mixed and manic episodes.



   Hypotheses:

   - Mixed episodes will exhibit a distinct network structure characterized by increased connectivity between symptoms from both manic and depressive poles compared to manic episodes.

   - Symptoms related to irritability, agitation, and impulsivity will show higher centrality in the network during mixed episodes, reflecting their prominence in this episode type.

   - The network analysis of mixed episodes will reveal a greater number of bidirectional connections between symptoms compared to manic episodes, indicating increased interplay between manic and depressive symptom clusters.

"
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(2) "na"
    ["label"]=>
    string(2) "na"
  }
  ["project_purposes"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_software_used"]=>
  array(2) {
    ["value"]=>
    string(1) "r"
    ["label"]=>
    string(1) "R"
  }
  ["project_research_methods"]=>
  string(603) "Inclusion Criteria:

Diagnosis of bipolar I disorder or bipolar II disorder, confirmed using standardized diagnostic criteria such as the Diagnostic and Statistical Manual of Mental Disorders (DSM-5).

Age 18 years or older.

Actual acute mixed or manic episode.



Exclusion Criteria:



Presence of severe comorbid psychiatric disorders (e.g., schizophrenia, substance use disorders) that may significantly confound symptom presentation and network analysis.

History of neurological disorders or cognitive impairment.

Pregnancy or breastfeeding."
  ["project_main_outcome_measure"]=>
  string(1219) "Primary Outcome Measure:

- Network Structure of Symptoms: The primary outcome measure involves characterizing the network structure of symptoms during mixed episodes compared to manic episodes in bipolar disorder. This will be assessed using network analysis techniques, which quantify the relationships between individual symptoms based on their co-occurrence and strength of associations within each episode type. Key network metrics, including node centrality (e.g., degree centrality, betweenness centrality) and network connectivity, will be calculated to identify central symptoms and overall network topology.



Secondary Outcome Measures:

1. Symptom Severity: Symptom severity at baseling during mixed and manic episodes will be assessed using validated rating scales such as the Young Mania Rating Scale (YMRS) and the Montgomery-Åsberg Depression Rating Scale (MADRS). These scales measure the severity of manic and depressive symptoms, and will use quantitative data on each symptom intensity, with higher scores indicating greater severity of manic or depressive symptoms, respectively. These outcome measures will be considered at baseline, defining the acute symptomatic state."
  ["project_main_predictor_indep"]=>
  string(221) "Network analysis will be applied in two different groups, classified as “manic episode” or “mixed episode” according to DSM5. According to CSR most studies already classified patients according to this conditions."
  ["project_other_variables_interest"]=>
  string(277) "Sociodemographical variables will be controled to assure the studied group resembles clinical population. Relevant sociodemographical variables (p.ex. drug use, socioeconomic situation) will be considered as nodes if analysis reveals modulatory effect on symptomatic variables."
  ["project_stat_analysis_plan"]=>
  string(2638) "Assessing trends in symptom endorsement 

Differences in endorsement rates for all symptoms between diagnostic groups will be explored using χ2 tests adopting Bonferroni correction for multiple testing. Similarities in endorsement rates across mixed states and manic states will be  assessed by Spearman rank-order correlations.



Network estimation

Network parameters for the symptoms will be estimated with a method based on the Ising model via the R-package IsingFit, or similar based on the needs of the data collected. Generally, each symptom is regressed on all others applying lL-regularized logistic regressions that constrain many of the small coefficients to zero. With this method, two sets of parameters are estimated: 1) thresholds, which represent the autonomous disposition of a symptom to be ‘on’ or ‘off’.

A threshold of 0 corresponds to a symptom having no preference while a threshold of higher (lower) than 0 corresponds to a symptom with a preference for being ‘on’ (‘off’); 2) weights, which denote pairwise connection between two symptoms and are represented by edges’. The higher (lower) the weight of a pairwise connection becomes, the more the two symptoms prefer to be in the same (different) state (‘on’ or ‘off’). The presence of communities (or clusters) of symptoms within the network is explored using the walktrap algorithm as this algorithm performs well on psychological networks and yields stable results. The accuracy and stability of the network estimation is further examined using non-parametric bootstrap methods (R-package bootnet, 1000 bootstrapped samples).



Network comparisons

Using the recently developed Network Comparison Test (NCT), the networks pertaining to the two diagnostic groups are tested for 1) invariant network structure, 2) invariant edge strength, and 3) invariant global strength. NCT uses two-tailed permutation tests in which the original group members are repeatedly randomly reassigned to new subsamples that maintain the original sample sizes, after which their network structures were compared on the three aspects described above.

The degree of similarity among individual edge weights between the two networks are further assessed with Spearman rank-order correlation. Lastly, the average symptom tendency to be non-zero (threshold) is compared between the two networks using twotailed t-test. 

All statistical analyses are performed using R Statistical Software (Foundation for Statistical Computing, Vienna, Austria). The R-package qgraph is used to visualize networks."
  ["project_timeline"]=>
  string(298) "Project initiation, including protocol development, : July 1, 2023

Completion of data collection, network analysis, and statistical analyses: August 31, 2024

Drafting of manuscript summarizing study findings and submitting to a peer-reviewed journal: November 30, 2024



"
  ["project_dissemination_plan"]=>
  string(541) "The primary product of this research will be one or more manuscripts summarizing the study findings. These manuscripts will detail the methodology, results, and implications of the research for the scientific and clinical communities. The primary audience for the study manuscripts will include researchers, clinicians, and professionals in the fields of psychiatry, psychology, and mental health. 



Proposed journals: European Neuropsychopharmacology, Bipolar Disorders, Acta Scandinavica Psychiatrica, Frontiers in Psychiatry"
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American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.
Belvederi Murri, M. et al. (2018) ‘The symptom network structure of depressive symptoms in late-life: Results from a european population study’, Molecular Psychiatry, 25(7), pp. 1447–1456. doi:10.1038/s41380-018-0232-0.
Borsboom, D., & Cramer, A. O. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91-121.
Borsboom, D. (2017) ‘A network theory of mental disorders’, World Psychiatry, 16(1), pp. 5–13. doi:10.1002/wps.20375.
Corponi, F. et al. (2020a) ‘Symptom networks in acute depression across bipolar and Major Depressive Disorders: A network analysis on a large, international, observational study’, European Neuropsychopharmacology, 35, pp. 49–60. doi:10.1016/j.euroneuro.2020.03.017.
Cramer, A.O.J., van Borkulo, C.D., Giltay, E.J., van der Maas, H.L.J., Kendler, K.S., Scheffer, M., Borsboom, D., 2016. Major depression as a complex dynamic system. PLoS One 11, e0167490. https://doi.org/10.1371/journal.pone.0167490.
Cramer, A.O.J., Waldorp, L.J., van der Maas, H.L.J., Borsboom, D., 2010. Comorbidity: a network perspective. Behav. Brain Sci. 33, 137–150. https://doi.org/10.1017/S0140525X09991567.
Cuellar, A.K., Johnson, S.L., Winters, R., 2005. Distinctions between bipolar and unipolar depression. Clin. Psychol. Rev. 25, 307–339. https://doi.org/10.1016/j.cpr.2004.12.002.
Epskamp, S., Borsboom, D., Fried, E.I., 2018. Estimating psychological networks and their accuracy: a tutorial paper. Behav. Res. Methods 50, 195–212. https://doi.org/10.3758/s13428-017-0862-1.
Epskamp, S., Cramer, A.O.J., Waldorp, L.J., Schmittmann, V.D., Borsboom, D., 2012. Qgraph : network visualizations of relationships in psychometric data. J. Stat. Softw. 48, 1–18. https: //doi.org/10.18637/jss.v048.i04.
Van Borkulo, C., Boschloo, L., Kossakowski, J., Tio, P., 2017. Comparing network structures on three aspects: a permutation test complex dynamical systems in psychology view project factors inducing coerced treatment in psychiatry View project. https://doi.org/10.13140/RG.2.2.29455.38569
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