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  string(684) "Intranasal esketamine (ESK) plus an oral antidepressant (OA) significantly reduces depression severity relative to placebo plus OA. However, it is unclear whether adding ESK to OA treatment differentially affects individual depressive symptoms. This study aims to investigate the relative efficacy of ESK plus OA versus placebo plus OA on individual depressive symptoms and to differentiate between direct and indirect symptom-specific effects using a network intervention analysis. The results might help clinicians to identify which individuals would potentially benefit from the complementary ESK treatment and lead to a better understanding of the antidepressant mechanism of ESK."
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    string(23) "University of Groningen"
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    string(26) "j.lima.constantino@umcg.nl"
    ["state_or_province"]=>
    string(9) "Groningen"
    ["country"]=>
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  string(136) "Health Holland, Top Sector Life Sciences & Health (LSH), Top Consortia for Knowledge and Innovations (TKI) (Grant number: LSHM22041)"
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  string(1627) "Background: Intranasal esketamine (ESK) has rapid and sustained antidepressant efficacy in treatment-resistant depression (TRD) individuals. However, the influence on individual depressive symptoms remains unclear.

Objective: To determine the relative efficacy of ESK plus oral antidepressants (OA) versus placebo plus OA on individual depressive symptoms and to differentiate between direct and indirect symptom-specific effects.

Study Design: Secondary analysis of data from double-blind, randomized controlled trials which allocated participants to receive ESK or placebo, in conjunction with a newly initiated OA.

Participants: Adults with a primary diagnosis of TRD (non-response to ≥ 1 but ≤ 5 oral antidepressant treatments in the current episode).

Primary and Secondary Outcome Measure(s): The primary outcome is the relative influence of ESK plus OA versus placebo plus OA on the change in individual depressive symptoms (Montgomery Åsberg Depression Rating Scale, MADRS). The secondary outcomes are the direct and indirect symptom-specific effects of the included treatment strategies.

Statistical Analysis: The relative influence of ESK relative to placebo on specific depressive symptoms will be assessed by including individual item scores as effect parameters and direct and indirect effects will be differentiated by accounting for symptom interrelatedness in a network model. Symptom-specific effects of the treatment strategies will be investigated using independent sample t-tests. Symptom interrelatedness will be accounted for by utilizing the mixed graphical models."
  ["project_brief_bg"]=>
  string(3174) "Significant evidence demonstrates that intranasal esketamine (ESK) has significant, sustained, and rapid antidepressant efficacy in individuals with major depressive disorder (MDD) who do not respond to initial antidepressant treatment classified as treatment-resistant depression (TRD) (1-3). Recent randomized control trials (RCTs) showed that adding ESK to oral antidepressant (OA) treatment leads to larger reductions in overall depression severity and earlier clinically meaningful improvement compared to placebo plus OA (2,3). It is unknown whether adding ESK to OA treatment has a differential effect on individual symptoms when compared to placebo with OA. Research comparing the effect of ESK plus OA versus placebo plus OA on sleep disturbance showed that patients in the ESK group had significantly greater improvement in sleep than the placebo group (4). However, no previous study has investigated the broad range of individual depression symptoms as clinical outcomes for ESK relative to placebo treatment.

The conceptualization of major depressive disorder (MDD) as a system of associated symptoms highlights the importance of accounting for treatment effects in individual symptoms when investigating antidepressant efficacy (5). In this context, studies investigating changes in individual depressive symptoms must consider the potential interrelatedness of such symptoms (4). The network intervention analysis (NIA) represents a novel approach to account for interrelatedness between symptoms, to map relationships between individual symptoms and to identify treatment targets (5-8). Moreover, it allows to differentiate direct symptom-specific effects, that is, effects that occur independently of changes in other symptoms, and indirect symptom-specific effects, effects that are mediated by changes in other symptoms (9-11).

Recent research utilized NIA to determine the direct and indirect effects of selective serotonin reuptake inhibitors (SSRIs) compared to placebo on depressive symptomatology (12,13). Results showed that SSRIs had strong and rapid direct effects on depressed mood and anxiety as well as substantial indirect effects on cognitive symptoms, including symptoms high relevant to clinical practice, such as suicidal thoughts and loss of interest. Notably, indirect effects were particularly stronger in patients treated with SSRIs who had larger improvement in depressed mood. These findings illustrate how NIA can improve the understanding of the potential working mechanisms of SSRIs. In relation, this approach can elucidate the mechanisms underlying the antidepressant efficacy of ESK. Differentiating direct symptom-specific effects and indirect symptom-specific effects could assist clinical decision making, as it might help clinicians to identify individuals who are most likely to potentially benefit from ESK combined with OA instead of OA alone. Thus, applying the NIA to assess symptom-specific antidepressant effects of ESK plus OA versus placebo plus OA can potentially help elucidate the underlying mechanism of ESK as a novel treatment strategies and enhance treatment selection for individual patients."
  ["project_specific_aims"]=>
  string(367) "This study aims to determine the relative influence of ESK plus OA versus placebo plus OA on individual depressive symptoms. It further aims to differentiate between direct symptom-specific effects and indirect symptom-specific effects. Given the exploratory character of this study, it is unclear which individual depressive symptoms might differ between the groups."
  ["project_study_design"]=>
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  ["project_purposes_exp"]=>
  string(125) "Network analysis to determine the relative influence of ESK plus OA versus placebo plus OA on individual depressive symptoms."
  ["project_research_methods"]=>
  string(831) "The sample size, inclusion, and exclusion criteria will be the same as the original trial, namely:

Sample size

The total sample size includes 252 participants.

Inclusion Criteria

Participants meet the Diagnostic and Statistical Manual of Mental Disorders-fifth Edition (DSM-5) diagnostic criteria for recurrent major depressive disorder (MDD) or single-episode MDD, without psychotic features; and have had non-response to ≥ 1 but ≤ 5 oral antidepressant treatments in the current episode of depression.

Exclusion Criteria

Key exclusion criteria included moderate-to-severe substance or alcohol use disorder within the prior 6 months, positive test result(s) for specified drugs of abuse, and use of high-dose benzodiazepines (i.e., total daily dose equivalent > 6 mg of lorazepam)."
  ["project_main_outcome_measure"]=>
  string(1205) "Depressive symptoms will be assessed through continuous variables based on the individual item score of the Montgomery–Åsberg Depression Rating Scale (MADRS). The scale consists of 10 items scored from 0 (not present) to 6 (continuously present). The primary outcome is the relative change in individual depression symptoms severity following ESK plus OA versus placebo plus OA. The secondary outcomes are the direct and indirect symptom-specific effects of the treatment arms. Direct symptom-specific effects will be assessed as the change in individual symptom severity from baseline to day 28. Indirect symptom-specific effects will be assessed as the mediated change in symptom severity score between baseline to day 28 as influenced by alterations in other symptoms.

The analysis will evaluate the relative effects as well as the direct and indirect symptom-specific effects of ESK relative to placebo on anxiety symptoms. This inclusion will be contingent on the ability of the model to provide reliable estimations after the addition of anxiety-related nodes. Anxiety symptoms will be assessed through continuous variables based on the Generalized Anxiety Disorder 7-item (GAD-7).

"
  ["project_main_predictor_indep"]=>
  string(120) "The main independent variable utilized in this study will be treatment groups (ESK plus OA relative to placebo plus OA)."
  ["project_other_variables_interest"]=>
  string(527) "Other variables that will be used to characterize the study sample and for multivariable risk adjustment include demographic characteristics, namely, age (in years), sex (male versus female), and education (low, middle, or high); and clinical characteristics, namely depression severity (based on the Clinical Global Impression Severity scale and the 30-item Inventory of Depressive Symptoms scale) and level of treatment resistance (based on the Massachusetts General Hospital Antidepressant Treatment Response Questionnaire)."
  ["project_stat_analysis_plan"]=>
  string(1970) "First, the relative effect of ESK plus OA versus placebo plus OA on individual depressive symptoms using independent sample t-tests comparing the change in the severity (28 days post-treatment minus baseline) of individual depressive symptoms between the two groups. These analyses will consider individual depressive symptoms as the primary effect parameters to determine whether the addition of ESK to OA results in significant changes compared to placebo plus OA.

Second, the effect of ESK plus OA relative to placebo plus OA on individual depression symptom severity when accounting for the interrelatedness of individual depressive symptoms will be evaluated to differentiate between direct and indirect effects. Mixed graphical models (MGMs) will be employed to construct a network model of symptom interrelations. This approach will allow for distinguishing direct effects, which reflect the specific impact of ESK on individual symptoms independent of other symptoms, from indirect effects, which occur through the influence of one symptom on another within the network.

Data analysis

1.	Network estimation method

The mixed graphical models (MGMs) will be utilized as it allows for the integration of categorical and continuous variables in a single framework. MGMs are based on exponential family distributions and penalized regression techniques. It provides a unified approach to modelling mixed variable types and determining conditional independence relationships between them. The analysis will be done through the  R-package 'mgm' (14).

2.	Accuracy of the estimates in the network

To investigate the accuracy of estimated connections in the network, the R-package bootnet will be used to calculate 95% confidence intervals around connection weights. Bootstrapped confidence intervals will be calculated by drawing 10,000 bootstrap samples of the data and recalculating connection weights for each sample. 

"
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  ["project_timeline"]=>
  string(1249) "The project timeline is estimated in a total of seven months. Provided that data is available, the project start date is set on 01 January 2026. The statistical analysis will be performed in the first two months primarily by a PhD student, with the support of a professor experienced in the network analysis. The first sections of the manuscript, including introduction and methodology, will be written by the PhD student concomitantly with the data analysis. After completion of the data analysis, the remaining sections of the manuscript, namely the results and discussion, will be written by the PhD student and the full manuscript will be reviewed by experienced senior researchers in psychiatry. The full manuscript will be completed, and the results will be reported back to the YODA project by 01 July 2026. The study will then be submitted for publication in a high-quality peer-reviewed journal in the field by 01 August 2026.



Key milestone dates:



-	Anticipated project start date: 01 January 2026 

-	Analysis completion date: 01 March 2026

-	Date results reported back to the YODA Project: 01 July 2026

-	Date manuscript drafted and first submitted for publication: 01 August 2026

"
  ["project_dissemination_plan"]=>
  string(307) "The project will result in the publication of a scientific article on a high-quality peer-reviewed journal in the field of psychiatry or psychopharmacology. Potential suitable journals for submission include the Psychiatry Research, the Journal of Psychopharmacology, and the Journal of Clinical Psychiatry."
  ["project_bibliography"]=>
  string(4262) "

Kern Sliwa, J., Naranjo, R. R., Jr, Turkoz, I., Petrillo, M. P., Cabrera, P., & Trivedi, M. (2024). Effects of esketamine nasal spray on depressive symptom severity in adults with treatment-resistant depression and associations between the Montgomery-Åsberg Depression Rating Scale and the 9-item Patient Health Questionnaire. CNS spectrums, 29(3), 176–186. https://doi.org/10.1017/S1092852924000105
Popova, V., et al. (2019). “Efficacy and Safety of Flexibly Dosed Esketamine Nasal Spray Combined With a Newly Initiated Oral Antidepressant in Treatment-Resistant Depression: A Randomized Double-Blind Active-Controlled Study.” Am J Psychiatry 176(6): 428-438.
Fedgchin, M., et al. (2019). “Efficacy and Safety of Fixed-Dose Esketamine Nasal Spray Combined With a New Oral Antidepressant in Treatment-Resistant Depression: Results of a Randomized, Double-Blind, Active-Controlled Study (TRANSFORM-1).” Int J Neuropsychopharmacol 22(10): 616-630.
Borentain S, Williamson D, Turkoz I, Popova V, McCall WV, Mathews M, Wiegand F. Effect of Sleep Disturbance on Efficacy of Esketamine in Treatment-Resistant Depression: Findings from Randomized Controlled Trials. Neuropsychiatr Dis Treat. 2021 Nov 30;17:3459-3470.
Bekhuis, E., et al. (2018). “Symptom-Specific Effects of Psychotherapy versus Combined Therapy in the Treatment of Mild to Moderate Depression: A Network Approach.” Psychother Psychosom 87(2): 121-123.
Boschloo, L., Bekhuis, E., Weitz, E. S., Reijnders, M., DeRubeis, R. J., Dimidjian, S., Dunner, D. L., Dunlop, B. W., Hegerl, U., Hollon, S. D., Jarrett, R. B., Kennedy, S. H., Miranda, J., Mohr, D. C., Simons, A. D., Parker, G., Petrak, F., Herpertz, S., Quilty, L. C., John Rush, A., Cuijpers, P. (2019). The symptom-specific efficacy of antidepressant medication vs. cognitive behavioral therapy in the treatment of depression: results from an individual patient data meta-analysis. World psychiatry : official journal of the World Psychiatric Association (WPA), 18(2), 183–191. https://doi.org/10.1002/wps.20630
Boschloo L. (2018). Zooming In and Zooming Out: A Network Perspective on the Comorbidity of Depression and Anxiety. Journal of the American Academy of Child and Adolescent Psychiatry, 57(12), 914–915. https://doi.org/10.1016/j.jaac.2018.07.880
Boschloo, L., Cuijpers, P., Karyotaki, E., Berger, T., Moritz, S., Meyer, B., & Klein, J. P. (2019). Symptom-specific effectiveness of an internet-based intervention in the treatment of mild to moderate depressive symptomatology: The potential of network estimation techniques. Behaviour research and therapy, 122, 103440. https://doi.org/10.1016/j.brat.2019.103440
Blanken, T. F., et al. (2019). “Introducing Network Intervention Analysis to Investigate Sequential, Symptom-Specific Treatment Effects: A Demonstration in Co-Occurring Insomnia and Depression.” Psychother Psychosom 88(1): 52-54.
Lancee, J., et al. (2022). “Network Intervention Analyses of cognitive therapy and behavior therapy for insomnia: Symptom specific effects and process measures.” Behav Res Ther 153: 104100.
Fried EI, Nesse RM, Zivin K, Guille C, Sen S. Depression is more than the sum score of its parts: individual DSM symptoms have different risk factors. Psychol Med. 2014;44:2067–76.
Boschloo, L., Hieronymus, F., Cuijpers, P., & ICECA Work Group (2022). Clinical response to SSRIs relative to cognitive behavioral therapy in depression: a symptom-specific approach. World psychiatry : official journal of the World Psychiatric Association (WPA), 21(1), 152–153. https://doi.org/10.1002/wps.20944
Boschloo, L., Hieronymus, F., Lisinski, A., Cuijpers, P., & Eriksson, E. (2023). The complex clinical response to selective serotonin reuptake inhibitors in depression: a network perspective. Translational psychiatry, 13(1), 19. https://doi.org/10.1038/s41398-022-02285-2
Haslbeck, J. M. B., & Waldorp, L. J. (2020). mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data. Journal of Statistical Software, 93(8), 1–46. https://doi.org/10.18637/jss.v093.i08

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    string(100) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Due-Diligence-Assessment-2025-0696.pdf"
    ["link"]=>
    string(93) "https://yoda.yale.edu/data-request/2025-0696/yoda-project-due-diligence-assessment-2025-0696/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(47) "yoda-project-due-diligence-assessment-2025-0696"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18001)
    ["date"]=>
    string(19) "2026-01-08 16:17:24"
    ["modified"]=>
    string(19) "2026-01-08 16:17:24"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(18539)
    ["id"]=>
    int(18539)
    ["title"]=>
    string(46) "YODA Project Protocol - 2025-0696 - 2025-10-02"
    ["filename"]=>
    string(46) "YODA-Project-Protocol-2025-0696-2025-10-02.pdf"
    ["filesize"]=>
    int(130946)
    ["url"]=>
    string(95) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Protocol-2025-0696-2025-10-02.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2025-0696/yoda-project-protocol-2025-0696-2025-10-02/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2025-0696-2025-10-02"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18001)
    ["date"]=>
    string(19) "2026-01-08 16:17:43"
    ["modified"]=>
    string(19) "2026-01-08 16:17:43"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  array(21) {
    ["ID"]=>
    int(18540)
    ["id"]=>
    int(18540)
    ["title"]=>
    string(36) "YODA Project Review - 2025-0696_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2025-0696_site.pdf"
    ["filesize"]=>
    int(1315687)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Review-2025-0696_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2025-0696/yoda-project-review-2025-0696_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2025-0696_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18001)
    ["date"]=>
    string(19) "2026-01-08 16:18:05"
    ["modified"]=>
    string(19) "2026-01-08 16:18:05"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(0) ""
}
data partner
array(1) {
  [0]=>
  string(0) ""
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(0) {
}


num of trials
array(1) {
  [0]=>
  string(1) "0"
}


res
array(1) {
  [0]=>
  string(1) "3"
}