This cohort study uses the 33-item Mood and Feelings Questionnaire to examine whether symptom network density is associated with treatment response in adolescent depression

Fried, Eiko

Goodyer, Ian M

Schweren, Lizanne

van Borkulo, Claudia D

English

One in 4 adolescents with depression does not respond favorably to treatment.1 Prognostic markers to identify this nonresponder group are lacking and urgently needed.2 It has been suggested that the network structure of depressive symptoms (ie, group-level covariance or connectivity between symptoms) may be informative in this regard.3 Intuitively, one may expect that more densely connected networks would be more inclined to result in negative spirals (eg, sleeplessness causes an individual to be too tired to go out, which leads to a lack of friends, resulting in sadness) and therefore more liable to nonresponse. An influential naturalistic study by van Borkulo et al published in JAMA Psychiatry3 reported that adult patients with depression who continue to experience problems in subsequent years have more densely connected networks at baseline than patients who later recover. Here, we performed a conceptual replication of that study in adolescents with depression who participated in a psychological treatment trial. We tested whether network characteristics at baseline were prognostic for long-term outcomes.

Schweren, L.

van Borkulo, C.D.

Fried, E.

Goodyer, I.M.

International Migration, Integration and Social Cohesion online publications

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Assessment of Symptom Network Density as a Prognostic Marker of TreatmentResponse in Adolescent DepressionSchweren, L.; van Borkulo, C.D.; Fried, E.; Goodyer, I.M.DOI10.1001/jamapsychiatry.2017.3561Publication date2018Document VersionFinal published versionPublished inJAMA PsychiatryLicenseArticle 25fa Dutch Copyright ActLink to publicationCitation for published version (APA):Schweren, L., van Borkulo, C. D., Fried, E., & Goodyer, I. M. (2018). Assessment of SymptomNetwork Density as a Prognostic Marker of Treatment Response in Adolescent Depression.JAMA Psychiatry, 75(1), 98-100. https://doi.org/10.1001/jamapsychiatry.2017.3561General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:26 Jul 2022Assessment of Symptom Network Densityas a Prognostic Marker of Treatment Responsein Adolescent DepressionOne in 4 adolescents with depression does not respond favor-ably to treatment.1 Prognostic markers to identify this nonre-sponder group are lacking and urgently needed.2 It has been suggested that the network structure of depressive symp-toms (ie, group-level covariance or connectivity between symp-toms) may be informative in this regard.3 Intuitively, one may expect that more densely connected networks would be more inclined to result in negative spirals (eg, sleeplessness causes an individual to be too tired to go out, which leads to a lack of friends, resulting in sadness) and therefore more liable to non-response. An influential naturalistic study by van Borkulo et al published in JAMA Psychiatry3 reported that adult patients with depression who continue to experience problems in sub-Letters98 jamapsychiatry.com© 2017 American Medical Association. All rights reserved.Downloaded From:  by a University of Amsterdam User  on 04/23/2018sequent years have more densely connected networks at base-line than patients who later recover. Here, we performed a con-ceptual replication of that study in adolescents with depressionwho participated in a psychological treatment trial. We testedwhether network characteristics at baseline were prognosticfor long-term outcomes.1Methods | Patients with depression completed the 33-itemMood and Feelings Questionnaire (MFQ) prior to treatment,with regular additional assessments up to 12 months afterthe end of treatment (ie, mean [SD], 22 [4.7] months afterbaseline assessment). The MFQ assesses recent self-reporteddepressive symptoms on a 4-point scale (never, sometimes,often, and always), with total scores ranging from 0 to 66(higher scores indicate more severe depressive symptoms).4In accordance with van Borkulo et al,3 11 items optimallyrepresenting DSM-5 symptoms of depression were selectedfor use in the present study conducted from June 29, 2010,to January 17, 2013, with data analyses performed from Feb-ruary 1 to June 25, 2017. The study was approved by theCambridgeshire Research Ethics Committee and localNational Health Service provider trusts. All patients and par-ents gave written informed consent.To derive 2 equally sized groups, relatively good and poorresponders were differentiated by the median percentagechange between baseline and final follow-up of the MFQ sum-mary score (median [interquartile range], −66% [41.2%]). Base-line regularized partial correlation matrices were estimatedbased on Spearman correlation coefficients.5 From those, theweighted sum of all absolute connections in the network (net-work density) as well as in each node (node strength) was de-rived. Parameters were compared using permutation testing(global α = .05; adjusted α per node per item, .05/11 = .005;1-sided).6 All analyses were performed using R, version 3.2.4(The R Foundation), with the qgraph package, version 1.3.5.Results | The cohort consisted of 465 adolescents withdepression (349 [75.1%] were girls; aged 11-17 years). Goodresponders had higher mean (SD) MFQ summary scores atbaseline (47.5 [9.2] vs 44.3 [11.6]; P = .006) and higher mean(SD) levels of suicidality (1.25 [0.9] vs 1.00 [0.9]; P < .001)compared with poor responders. Although global networkstrength was higher in poor responders, the difference wasnot significant (good responders, 3.6; poor, 4.3; P = .15;Figure 1). There were no differences in local node strengthexcept for that of “concentration problems,” which at theuncorrected α level was more connected to the other nodesin the poor responders than in the good responders (goodresponders, 1.1; poor responders, 2.0; P = .02; Figure 2). Sen-sitivity analyses indicated similar findings when treatmentresponse was defined as below clinical threshold (ie, MFQscore <27) at the final follow-up.4Discussion | Applying the same statistical methods as thoseused in the study by van Borkulo et al,3 which had a simi-larly sized sample of adults with depression followed upnaturalistically, we found no significant association betweenhigher network strength and poorer outcomes. That thedirection of the association in our study was consistent withthe results of the previous study, however, indicates thatfurther investigation of the validity of network strength as aprognostic marker is warranted. There were 2 importantmethodological differences between the studies. First, theprevious one was a naturalistic cohort study, whereas herewe evaluated treatment outcomes. Stronger symptom net-works may be prognostic of depression persistence in natu-ralistic settings but not when symptoms (and perhaps net-works) are actively being challenged. Second, networkdensity may not have equal prognostic value in adult andadolescent groups. For instance, denser networks may be aconsequence and a behavioral marker of longer illness dura-tion or recurrent episodes but have no such signature infirst-episode depression in adolescents. Unfortunately, thishypothesis cannot be tested within the current sample ofadolescents with depression, most of whom were experienc-ing their first episode.Figure 1. Network Structures of Depressive Symptoms in 233 Relatively Good and 232 Poor Respondersto TreatmentdepintwapinshypagireteneguiconsuidepintwapinshypagireteneguiconsuiGood respondersA Poor respondersBBlue lines represent positiveconnections; red lines, negativeconnections; and thicker lines,stronger connections. For thesymptom (node) abbreviations,agi indicates psychomotor agitation;con, concentration problems;dep, feeling sad/depressed; ene, lossof energy; gui, guilt/worthlessness;hyp, hypersomnia; ins, insomnia;int, loss of interest/pleasure;ret, psychomotor retardation;sui, suicidal ideation; and wap, weightor appetite change.Lettersjamapsychiatry.com (Reprinted) JAMA Psychiatry January 2018 Volume 75, Number 1 99© 2017 American Medical Association. All rights reserved.Downloaded From:  by a University of Amsterdam User  on 04/23/2018With network analyses taking an astonishing flight in psy-chiatry, we recommend cautious application of group-level net-work density as a prognostic marker. Crucial steps to be takenby the field include further replication studies as well as in-depth psychometric evaluation of the reliability and clinicalcorrelates of network parameters.Lizanne Schweren, PhDClaudia D. van Borkulo, MScEiko Fried, PhDIan M. Goodyer, MDAuthor Affiliations: Department of Psychiatry, University of Cambridge,Cambridge, United Kingdom (Schweren, Goodyer); Department of Psychology,University of Amsterdam, Amsterdam, the Netherlands (van Borkulo, Fried).Corresponding Author: Lizanne Schweren, PhD, Department of Psychiatry,University of Cambridge, 18b Trumpington Rd, Douglas House, Cambridge CB28AH, United Kingdom (ljs82@medschl.cam.ac.uk).Accepted for Publication: October 3, 2017.Published Online: November 29, 2017. doi:10.1001/jamapsychiatry.2017.3561Author Contributions: Drs Schweren and Fried had full access to all of the datain the study and take responsibility for the integrity of the data and the accuracyof the data analysis.Study concept and design: Schweren, van Borkulo, Goodyer.Acquisition, analysis, or interpretation of data: All authors.Drafting of the manuscript: Schweren, Goodyer.Critical revision of the manuscript for important intellectual content: All authors.Statistical analysis: All authors.Obtained funding: Goodyer.Administrative, technical, or material support: Schweren, Goodyer.Study Supervision: Fried.Conflict of Interest Disclosures: Dr Goodyer reported serving as a paidconsultant for Lundbeck and licensing the Brief Psychosocial Intervention toLundbeck for use in a clinical trial. No other disclosures were reported.Funding/Support: The Improving Mood with Psychoanalytic Psychotherapyand Cognitive Behaviour Therapy (IMPACT) trial was funded by the HealthTechnology Assessment program of the National Institute for Health Research(06/05/01). Dr Schweren is a research associate funded by the Friends ofPeterhouse. Drs Fried and van Borkulo are funded by Consolidator Grant647209 from the European Research Council.Role of the Funder/Sponsor: The funding sources had no role in the design andconduct of the study; collection, management, analysis, and interpretation ofthe data; preparation, review, or approval of the manuscript; or the decision tosubmit the manuscript for publication.Additional Contributions: We thank all members of the IMPACT consortium.1. Goodyer IM, Reynolds S, Barrett B, et al. Cognitive behavioural therapy andshort-term psychoanalytical psychotherapy versus a brief psychosocialintervention in adolescents with unipolar major depressive disorder (IMPACT):a multicentre, pragmatic, observer-blind, randomised controlled superioritytrial. Lancet Psychiatry. 2017;4(2):109-119.2. Nilsen TS, Eisemann M, Kvernmo S. Predictors and moderators of outcome inchild and adolescent anxiety and depression: a systematic review ofpsychological treatment studies. Eur Child Adolesc Psychiatry. 2013;22(2):69-87.3. van Borkulo C, Boschloo L, Borsboom D, Penninx BWJH, Waldorp LJ,Schoevers RA. Association of symptom network structure with the course of[corrected] depression. JAMA Psychiatry. 2015;72(12):1219-1226.4. Daviss WB, Birmaher B, Melhem NA, Axelson DA, Michaels SM, Brent DA.Criterion validity of the Mood and Feelings Questionnaire for depressiveepisodes in clinic and non-clinic subjects. J Child Psychol Psychiatry. 2006;47(9):927-934.5. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D.Qgraph: network visualizations of relationships in psychometric data. J Stat Soft.2012;48(4):1-18.6. van Borkulo CD, Waldorp LJ, Boschloo L, et al. Comparing networkstructures on three aspects: a permutation test. ResearchGate. Published March2017. Accessed May 4, 2017. doi:10.13140/RG.2.2.29455.38569Figure 2. Differences in Total Mood and Feelings Questionnaire (MFQ)Mean Score, SD of the Mean Score, and Node Strength by SymptomBetween Good and Poor Responders to Treatment032Mean Total MFQ Score1dep wap ins hyp agi ret ene gui con suiNodeintPoor responderGood responderMeanA01.51.0SD of Total MFQ Score0.5dep wap ins hyp agi ret ene gui con suiNodeintStandard deviationB02.52.0Strength 1.51.00.5dep wap ins hyp agi ret ene gui con suiNodeintNode strengthCRed boxes outline statistically significant differences. Symptom (node)abbreviations are defined in the caption to Figure 1.Letters100 JAMA Psychiatry January 2018 Volume 75, Number 1 (Reprinted) jamapsychiatry.com© 2017 American Medical Association. All rights reserved.Downloaded From:  by a University of Amsterdam User  on 04/23/2018

Assessment of Symptom Network Density as a Prognostic Marker of Treatment Response in Adolescent Depression

NARCIS 

Lizanne Schweren

Claudia D. van Borkulo

Eiko Fried

Ian M. Goodyer

Crossref

JAMA Psychiatry

Apollo (Cambridge)

reported by Jansen et al4 in 3-year-old children. Research sug-gests that impaired neuromotor development precedes schizo-phrenia onset, although most children with impaired neuromo-tor functioning do not develop schizophrenia.2 In contrast,children who later met criteria for BD exhibited a higher level ofmotor performance during childhood than controls.1 Our resultshighlight that the genetic predisposition for schizophreniacovaries with motor deficits observable during infancy in acommunity-based sample. Given that the prevalence of schizo-phrenia is low, these early features represent indices of liabilityrather than precursors of the disorder.This study has certain limitations. Genetic pleiotropy orearly environmental factors could also explain the association.1Selective nonresponse to neuromotor assessment could biasthe analysis. The power of the BD GWAS might have been in-sufficient to detect associations between BD PRS and neuro-motor development. Despite limitations, this study has sev-eral strengths, including an objective and prospectivelyassessed measure of neuromotor development in a large ho-mogenous sample of infants.To our knowledge, this is the first evidence that geneticliability for schizophrenia may covary with altered neuromo-tor development in infancy. Future research will show whetherearly neuromotor development can support early screening ofsusceptible groups possibly defined by genetic risk.Fadila Serdarevic, MD, DScPhilip R. Jansen, MD, MScAkhgar Ghassabian, MD, PhDTonya White, MD, PhDVincent W. V. Jaddoe, MD, PhDDanielle Posthuma, PhDHenning Tiemeier, MD, PhDAuthor Affiliations: Department of Child and Adolescent Psychiatry, ErasmusMedical Center–Sophia Children’s Hospital, Rotterdam, the Netherlands(Serdarevic, Jansen, Ghassabian, White, Tiemeier); Department of ComplexTrait Genetics, Center for Neuroscience and Cognitive Research, AmsterdamNeuroscience, VU University, Amsterdam, the Netherlands (Jansen, Posthuma);Department of Radiology, Erasmus Medical Center Rotterdam, the Netherlands(Jansen, White); Departments of Pediatrics, Population Health, andEnvironmental Medicine, New York University School of Medicine, New York,New York (Ghassabian); Department of Epidemiology, Erasmus Medical Center,Rotterdam, the Netherlands (Jaddoe, Tiemeier); Department of Pediatrics,Erasmus Medical Center–Sophia Children’s Hospital Rotterdam, the Netherlands(Jaddoe); Department of Clinical Genetics, VU Medical Center, Amsterdam, theNetherlands (Posthuma); Department of Psychiatry, Erasmus Medical Center,Rotterdam, the Netherlands (Tiemeier).Corresponding Author: Henning Tiemeier, MD, PhD, Department of Child andAdolescent Psychiatry, Erasmus Medical Centre–Sophia Children’s Hospital,Dr Molewaterplein 60, 3015 GJ, Rotterdam, the Netherlands (h.tiemeier@erasmusmc.nl).Accepted for Publication: September 20, 2017.Published Online: November 8, 2017. doi:10.1001/jamapsychiatry.2017.3459Author Contributions: Drs Serdarevic and Tiemeier had full access to all thedata in the study and take responsibility for the integrity of the data and theaccuracy of the data analysis.Study concept and design: Serdarevic, Jansen, Jaddoe, Tiemeier.Acquisition, analysis, or interpretation of data: Serdarevic, Jansen, Ghassabian,White, Posthuma, Tiemeier.Drafting of the manuscript: Serdarevic, Jansen, Ghassabian, Tiemeier.Critical revision of the manuscript for important intellectual content: All authors.Statistical analysis: Serdarevic, Jansen.Obtained funding: White, Posthuma, Tiemeier.Administrative, technical, or material support: White.Study supervision: Ghassabian, Jaddoe, Posthuma, Tiemeier.Conflict of Interest Disclosures: None reported.Funding/Support: Dr Tiemeier was supported by NWO 024.001.003 from theDutch Ministry of Education, Culture and NWO 016.VICI.170.200 from theScience and the Netherlands Organization for Scientific Research Consortiumon Individual Development. Dr Serdarevic was supported by grant 2013-2548/001-001-EMA2 from Erasmus Mundus Western Balkans postdoctoralscholarship financed by the European Commission. Dr Jansen was supported bygrant SSWO S14-27 from the Sophia Foundation for Scientific Research. TheGeneration R Study is supported by the Erasmus Medical Center-Rotterdam,the Erasmus University Rotterdam, the Netherlands Organization for ScientificResearch, and the Ministry of Health, Welfare, and Sport.Role of the Funder/Sponsor: The funding organizations had no role in thedesign and conduct of the study; collection, management, analysis, andinterpretation of the data; preparation, review, or approval of the manuscript;and decision to submit the manuscript for publication.Additional Contributions: We gratefully acknowledge the contribution ofparticipating mothers, general practitioners, hospitals, midwives, andpharmacies in Rotterdam. Frank C. Verhulst, MD, PhD (Department of Child andAdolescent Psychiatry and/ Psychology, Erasmus Medical Center), and Tamaravan Batenburg, PhD (Department of Child and Adolescent Psychiatry andPsychology, Erasmus Medical Center), provided neuromotor data. They did notreceive compensation for their contribution to this study.Additional Information: Drs Serdarevic, Jansen, and White are members ofthe Generation R Study Group, Erasmus Medical Center Rotterdam, theNetherlands.1. Murray RM, Sham P, Van Os J, Zanelli J, Cannon M, McDonald C.A developmental model for similarities and dissimilarities betweenschizophrenia and bipolar disorder. Schizophr Res. 2004;71(2-3):405-416.2. Burton BK, Hjorthøj C, Jepsen JR, Thorup A, Nordentoft M, Plessen KJ.Research review: do motor deficits during development represent anendophenotype for schizophrenia? a meta-analysis. J Child Psychol Psychiatry.2016;57(4):446-456.3. Kooijman MN, Kruithof CJ, van Duijn CM, et al. The Generation R Study:design and cohort update 2017. Eur J Epidemiol. 2016;31(12):1243-1264.4. Jansen PR, Polderman TJC, Bolhuis K, et al. Polygenic scores forschizophrenia and educational attainment are associated with behaviouralproblems in early childhood in the general population. [published online June19, 2017]. J Child Psychol Psychiatry. 2017. doi:10.1111/jcpp.127595. Van Batenburg-Eddes T, de Groot L, Huizink AC, et al. Maternal symptoms ofanxiety during pregnancy affect infant neuromotor development: theGeneration R Study. Dev Neuropsychol. 2009;34(4):476-493.6. Burton BK, Thorup AAE, Jepsen JR, et al. Impairments of motor functionamong children with a familial risk of schizophrenia or bipolar disorder at 7 yearsold in Denmark: an observational cohort study. Lancet Psychiatry. 2017;4(5):400-408.Assessment of Symptom Network Densityas a Prognostic Marker of Treatment Responsein Adolescent DepressionOne in 4 adolescents with depression does not respond favor-ably to treatment.1 Prognostic markers to identify this nonre-sponder group are lacking and urgently needed.2 It has beensuggested that the network structure of depressive symp-toms (ie, group-level covariance or connectivity between symp-toms) may be informative in this regard.3 Intuitively, one mayexpect that more densely connected networks would be moreinclined to result in negative spirals (eg, sleeplessness causesan individual to be too tired to go out, which leads to a lack offriends, resulting in sadness) and therefore more liable to non-response. An influential naturalistic study by van Borkulo etal published in JAMA Psychiatry3 reported that adult patientswith depression who continue to experience problems in sub-Letters98 JAMA Psychiatry January 2018 Volume 75, Number 1 (Reprinted) jamapsychiatry.com© 2017 American Medical Association. All rights reserved.Downloaded From:  by a Cambridge University Library User  on 06/05/2018sequent years have more densely connected networks at base-line than patients who later recover. Here, we performed a con-ceptual replication of that study in adolescents with depressionwho participated in a psychological treatment trial. We testedwhether network characteristics at baseline were prognosticfor long-term outcomes.1Methods | Patients with depression completed the 33-itemMood and Feelings Questionnaire (MFQ) prior to treatment,with regular additional assessments up to 12 months afterthe end of treatment (ie, mean [SD], 22 [4.7] months afterbaseline assessment). The MFQ assesses recent self-reporteddepressive symptoms on a 4-point scale (never, sometimes,often, and always), with total scores ranging from 0 to 66(higher scores indicate more severe depressive symptoms).4In accordance with van Borkulo et al,3 11 items optimallyrepresenting DSM-5 symptoms of depression were selectedfor use in the present study conducted from June 29, 2010,to January 17, 2013, with data analyses performed from Feb-ruary 1 to June 25, 2017. The study was approved by theCambridgeshire Research Ethics Committee and localNational Health Service provider trusts. All patients and par-ents gave written informed consent.To derive 2 equally sized groups, relatively good and poorresponders were differentiated by the median percentagechange between baseline and final follow-up of the MFQ sum-mary score (median [interquartile range], −66% [41.2%]). Base-line regularized partial correlation matrices were estimatedbased on Spearman correlation coefficients.5 From those, theweighted sum of all absolute connections in the network (net-work density) as well as in each node (node strength) was de-rived. Parameters were compared using permutation testing(global α = .05; adjusted α per node per item, .05/11 = .005;1-sided).6 All analyses were performed using R, version 3.2.4(The R Foundation), with the qgraph package, version 1.3.5.Results | The cohort consisted of 465 adolescents withdepression (349 [75.1%] were girls; aged 11-17 years). Goodresponders had higher mean (SD) MFQ summary scores atbaseline (47.5 [9.2] vs 44.3 [11.6]; P = .006) and higher mean(SD) levels of suicidality (1.25 [0.9] vs 1.00 [0.9]; P < .001)compared with poor responders. Although global networkstrength was higher in poor responders, the difference wasnot significant (good responders, 3.6; poor, 4.3; P = .15;Figure 1). There were no differences in local node strengthexcept for that of “concentration problems,” which at theuncorrected α level was more connected to the other nodesin the poor responders than in the good responders (goodresponders, 1.1; poor responders, 2.0; P = .02; Figure 2). Sen-sitivity analyses indicated similar findings when treatmentresponse was defined as below clinical threshold (ie, MFQscore <27) at the final follow-up.4Discussion | Applying the same statistical methods as thoseused in the study by van Borkulo et al,3 which had a simi-larly sized sample of adults with depression followed upnaturalistically, we found no significant association betweenhigher network strength and poorer outcomes. That thedirection of the association in our study was consistent withthe results of the previous study, however, indicates thatfurther investigation of the validity of network strength as aprognostic marker is warranted. There were 2 importantmethodological differences between the studies. First, theprevious one was a naturalistic cohort study, whereas herewe evaluated treatment outcomes. Stronger symptom net-works may be prognostic of depression persistence in natu-ralistic settings but not when symptoms (and perhaps net-works) are actively being challenged. Second, networkdensity may not have equal prognostic value in adult andadolescent groups. For instance, denser networks may be aconsequence and a behavioral marker of longer illness dura-tion or recurrent episodes but have no such signature infirst-episode depression in adolescents. Unfortunately, thishypothesis cannot be tested within the current sample ofadolescents with depression, most of whom were experienc-ing their first episode.Figure 1. Network Structures of Depressive Symptoms in 233 Relatively Good and 232 Poor Respondersto TreatmentdepintwapinshypagireteneguiconsuidepintwapinshypagireteneguiconsuiGood respondersA Poor respondersBBlue lines represent positiveconnections; red lines, negativeconnections; and thicker lines,stronger connections. For thesymptom (node) abbreviations,agi indicates psychomotor agitation;con, concentration problems;dep, feeling sad/depressed; ene, lossof energy; gui, guilt/worthlessness;hyp, hypersomnia; ins, insomnia;int, loss of interest/pleasure;ret, psychomotor retardation;sui, suicidal ideation; and wap, weightor appetite change.Lettersjamapsychiatry.com (Reprinted) JAMA Psychiatry January 2018 Volume 75, Number 1 99© 2017 American Medical Association. All rights reserved.Downloaded From:  by a Cambridge University Library User  on 06/05/2018With network analyses taking an astonishing flight in psy-chiatry, we recommend cautious application of group-level net-work density as a prognostic marker. Crucial steps to be takenby the field include further replication studies as well as in-depth psychometric evaluation of the reliability and clinicalcorrelates of network parameters.Lizanne Schweren, PhDClaudia D. van Borkulo, MScEiko Fried, PhDIan M. Goodyer, MDAuthor Affiliations: Department of Psychiatry, University of Cambridge,Cambridge, United Kingdom (Schweren, Goodyer); Department of Psychology,University of Amsterdam, Amsterdam, the Netherlands (van Borkulo, Fried).Corresponding Author: Lizanne Schweren, PhD, Department of Psychiatry,University of Cambridge, 18b Trumpington Rd, Douglas House, Cambridge CB28AH, United Kingdom (ljs82@medschl.cam.ac.uk).Accepted for Publication: October 3, 2017.Published Online: November 29, 2017. doi:10.1001/jamapsychiatry.2017.3561Author Contributions: Drs Schweren and Fried had full access to all of the datain the study and take responsibility for the integrity of the data and the accuracyof the data analysis.Study concept and design: Schweren, van Borkulo, Goodyer.Acquisition, analysis, or interpretation of data: All authors.Drafting of the manuscript: Schweren, Goodyer.Critical revision of the manuscript for important intellectual content: All authors.Statistical analysis: All authors.Obtained funding: Goodyer.Administrative, technical, or material support: Schweren, Goodyer.Study Supervision: Fried.Conflict of Interest Disclosures: Dr Goodyer reported serving as a paidconsultant for Lundbeck and licensing the Brief Psychosocial Intervention toLundbeck for use in a clinical trial. No other disclosures were reported.Funding/Support: The Improving Mood with Psychoanalytic Psychotherapyand Cognitive Behaviour Therapy (IMPACT) trial was funded by the HealthTechnology Assessment program of the National Institute for Health Research(06/05/01). Dr Schweren is a research associate funded by the Friends ofPeterhouse. Drs Fried and van Borkulo are funded by Consolidator Grant647209 from the European Research Council.Role of the Funder/Sponsor: The funding sources had no role in the design andconduct of the study; collection, management, analysis, and interpretation ofthe data; preparation, review, or approval of the manuscript; or the decision tosubmit the manuscript for publication.Additional Contributions: We thank all members of the IMPACT consortium.1. Goodyer IM, Reynolds S, Barrett B, et al. Cognitive behavioural therapy andshort-term psychoanalytical psychotherapy versus a brief psychosocialintervention in adolescents with unipolar major depressive disorder (IMPACT):a multicentre, pragmatic, observer-blind, randomised controlled superioritytrial. Lancet Psychiatry. 2017;4(2):109-119.2. Nilsen TS, Eisemann M, Kvernmo S. Predictors and moderators of outcome inchild and adolescent anxiety and depression: a systematic review ofpsychological treatment studies. Eur Child Adolesc Psychiatry. 2013;22(2):69-87.3. van Borkulo C, Boschloo L, Borsboom D, Penninx BWJH, Waldorp LJ,Schoevers RA. Association of symptom network structure with the course of[corrected] depression. JAMA Psychiatry. 2015;72(12):1219-1226.4. Daviss WB, Birmaher B, Melhem NA, Axelson DA, Michaels SM, Brent DA.Criterion validity of the Mood and Feelings Questionnaire for depressiveepisodes in clinic and non-clinic subjects. J Child Psychol Psychiatry. 2006;47(9):927-934.5. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D.Qgraph: network visualizations of relationships in psychometric data. J Stat Soft.2012;48(4):1-18.6. van Borkulo CD, Waldorp LJ, Boschloo L, et al. Comparing networkstructures on three aspects: a permutation test. ResearchGate. Published March2017. Accessed May 4, 2017. doi:10.13140/RG.2.2.29455.38569Pediatric Use of Antipsychotic MedicationsBefore and After Medicaid Peer ReviewImplementationIn response to the growing cardiometabolic safety concernsabout the use of atypical antipsychotic (AAP) medications inchildren,1,2 several state Medicaid agencies have adopted anovel, more clinically nuanced and individualized approachto reviewing the appropriateness of AAP use, namely, peer re-view prior authorization (PA) policies.3 Physicians must re-ceive preapproval through contracted clinicians (peer review-ers) to prescribe AAPs to certain-aged children. We assessedthe effect of peer review PA policies on AAP use among Med-icaid-insured youth according to age restriction criteria.Figure 2. Differences in Total Mood and Feelings Questionnaire (MFQ)Mean Score, SD of the Mean Score, and Node Strength by SymptomBetween Good and Poor Responders to Treatment032Mean Total MFQ Score1dep wap ins hyp agi ret ene gui con suiNodeintPoor responderGood responderMeanA01.51.0SD of Total MFQ Score0.5dep wap ins hyp agi ret ene gui con suiNodeintStandard deviationB02.52.0Strength 1.51.00.5dep wap ins hyp agi ret ene gui con suiNodeintNode strengthCRed boxes outline statistically significant differences. Symptom (node)abbreviations are defined in the caption to Figure 1.Letters100 JAMA Psychiatry January 2018 Volume 75, Number 1 (Reprinted) jamapsychiatry.com© 2017 American Medical Association. All rights reserved.Downloaded From:  by a Cambridge University Library User  on 06/05/2018

Assessment of Symptom Network Density as a Prognostic Marker of Treatment Response in Adolescent Depression.

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Assessment of Symptom Network Density as a Prognostic Marker of TreatmentResponse in Adolescent DepressionSchweren, L.; van Borkulo, C.D.; Fried, E.; Goodyer, I.M.DOI10.1001/jamapsychiatry.2017.3561Publication date2018Document VersionFinal published versionPublished inJAMA PsychiatryLicenseArticle 25fa Dutch Copyright Act (https://www.openaccess.nl/en/in-the-netherlands/you-share-we-take-care)Link to publicationCitation for published version (APA):Schweren, L., van Borkulo, C. D., Fried, E., & Goodyer, I. M. (2018). Assessment of SymptomNetwork Density as a Prognostic Marker of Treatment Response in Adolescent Depression.JAMA Psychiatry, 75(1), 98-100. https://doi.org/10.1001/jamapsychiatry.2017.3561General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:31 Aug 2023Assessment of Symptom Network Densityas a Prognostic Marker of Treatment Responsein Adolescent DepressionOne in 4 adolescents with depression does not respond favor-ably to treatment.1 Prognostic markers to identify this nonre-sponder group are lacking and urgently needed.2 It has been suggested that the network structure of depressive symp-toms (ie, group-level covariance or connectivity between symp-toms) may be informative in this regard.3 Intuitively, one may expect that more densely connected networks would be more inclined to result in negative spirals (eg, sleeplessness causes an individual to be too tired to go out, which leads to a lack of friends, resulting in sadness) and therefore more liable to non-response. An influential naturalistic study by van Borkulo et al published in JAMA Psychiatry3 reported that adult patients with depression who continue to experience problems in sub-Letters98 jamapsychiatry.com© 2017 American Medical Association. All rights reserved.Downloaded From:  by a University of Amsterdam User  on 04/23/2018sequent years have more densely connected networks at base-line than patients who later recover. Here, we performed a con-ceptual replication of that study in adolescents with depressionwho participated in a psychological treatment trial. We testedwhether network characteristics at baseline were prognosticfor long-term outcomes.1Methods | Patients with depression completed the 33-itemMood and Feelings Questionnaire (MFQ) prior to treatment,with regular additional assessments up to 12 months afterthe end of treatment (ie, mean [SD], 22 [4.7] months afterbaseline assessment). The MFQ assesses recent self-reporteddepressive symptoms on a 4-point scale (never, sometimes,often, and always), with total scores ranging from 0 to 66(higher scores indicate more severe depressive symptoms).4In accordance with van Borkulo et al,3 11 items optimallyrepresenting DSM-5 symptoms of depression were selectedfor use in the present study conducted from June 29, 2010,to January 17, 2013, with data analyses performed from Feb-ruary 1 to June 25, 2017. The study was approved by theCambridgeshire Research Ethics Committee and localNational Health Service provider trusts. All patients and par-ents gave written informed consent.To derive 2 equally sized groups, relatively good and poorresponders were differentiated by the median percentagechange between baseline and final follow-up of the MFQ sum-mary score (median [interquartile range], −66% [41.2%]). Base-line regularized partial correlation matrices were estimatedbased on Spearman correlation coefficients.5 From those, theweighted sum of all absolute connections in the network (net-work density) as well as in each node (node strength) was de-rived. Parameters were compared using permutation testing(global α = .05; adjusted α per node per item, .05/11 = .005;1-sided).6 All analyses were performed using R, version 3.2.4(The R Foundation), with the qgraph package, version 1.3.5.Results | The cohort consisted of 465 adolescents withdepression (349 [75.1%] were girls; aged 11-17 years). Goodresponders had higher mean (SD) MFQ summary scores atbaseline (47.5 [9.2] vs 44.3 [11.6]; P = .006) and higher mean(SD) levels of suicidality (1.25 [0.9] vs 1.00 [0.9]; P < .001)compared with poor responders. Although global networkstrength was higher in poor responders, the difference wasnot significant (good responders, 3.6; poor, 4.3; P = .15;Figure 1). There were no differences in local node strengthexcept for that of “concentration problems,” which at theuncorrected α level was more connected to the other nodesin the poor responders than in the good responders (goodresponders, 1.1; poor responders, 2.0; P = .02; Figure 2). Sen-sitivity analyses indicated similar findings when treatmentresponse was defined as below clinical threshold (ie, MFQscore <27) at the final follow-up.4Discussion | Applying the same statistical methods as thoseused in the study by van Borkulo et al,3 which had a simi-larly sized sample of adults with depression followed upnaturalistically, we found no significant association betweenhigher network strength and poorer outcomes. That thedirection of the association in our study was consistent withthe results of the previous study, however, indicates thatfurther investigation of the validity of network strength as aprognostic marker is warranted. There were 2 importantmethodological differences between the studies. First, theprevious one was a naturalistic cohort study, whereas herewe evaluated treatment outcomes. Stronger symptom net-works may be prognostic of depression persistence in natu-ralistic settings but not when symptoms (and perhaps net-works) are actively being challenged. Second, networkdensity may not have equal prognostic value in adult andadolescent groups. For instance, denser networks may be aconsequence and a behavioral marker of longer illness dura-tion or recurrent episodes but have no such signature infirst-episode depression in adolescents. Unfortunately, thishypothesis cannot be tested within the current sample ofadolescents with depression, most of whom were experienc-ing their first episode.Figure 1. Network Structures of Depressive Symptoms in 233 Relatively Good and 232 Poor Respondersto TreatmentdepintwapinshypagireteneguiconsuidepintwapinshypagireteneguiconsuiGood respondersA Poor respondersBBlue lines represent positiveconnections; red lines, negativeconnections; and thicker lines,stronger connections. For thesymptom (node) abbreviations,agi indicates psychomotor agitation;con, concentration problems;dep, feeling sad/depressed; ene, lossof energy; gui, guilt/worthlessness;hyp, hypersomnia; ins, insomnia;int, loss of interest/pleasure;ret, psychomotor retardation;sui, suicidal ideation; and wap, weightor appetite change.Lettersjamapsychiatry.com (Reprinted) JAMA Psychiatry January 2018 Volume 75, Number 1 99© 2017 American Medical Association. All rights reserved.Downloaded From:  by a University of Amsterdam User  on 04/23/2018With network analyses taking an astonishing flight in psy-chiatry, we recommend cautious application of group-level net-work density as a prognostic marker. Crucial steps to be takenby the field include further replication studies as well as in-depth psychometric evaluation of the reliability and clinicalcorrelates of network parameters.Lizanne Schweren, PhDClaudia D. van Borkulo, MScEiko Fried, PhDIan M. Goodyer, MDAuthor Affiliations: Department of Psychiatry, University of Cambridge,Cambridge, United Kingdom (Schweren, Goodyer); Department of Psychology,University of Amsterdam, Amsterdam, the Netherlands (van Borkulo, Fried).Corresponding Author: Lizanne Schweren, PhD, Department of Psychiatry,University of Cambridge, 18b Trumpington Rd, Douglas House, Cambridge CB28AH, United Kingdom (ljs82@medschl.cam.ac.uk).Accepted for Publication: October 3, 2017.Published Online: November 29, 2017. doi:10.1001/jamapsychiatry.2017.3561Author Contributions: Drs Schweren and Fried had full access to all of the datain the study and take responsibility for the integrity of the data and the accuracyof the data analysis.Study concept and design: Schweren, van Borkulo, Goodyer.Acquisition, analysis, or interpretation of data: All authors.Drafting of the manuscript: Schweren, Goodyer.Critical revision of the manuscript for important intellectual content: All authors.Statistical analysis: All authors.Obtained funding: Goodyer.Administrative, technical, or material support: Schweren, Goodyer.Study Supervision: Fried.Conflict of Interest Disclosures: Dr Goodyer reported serving as a paidconsultant for Lundbeck and licensing the Brief Psychosocial Intervention toLundbeck for use in a clinical trial. No other disclosures were reported.Funding/Support: The Improving Mood with Psychoanalytic Psychotherapyand Cognitive Behaviour Therapy (IMPACT) trial was funded by the HealthTechnology Assessment program of the National Institute for Health Research(06/05/01). Dr Schweren is a research associate funded by the Friends ofPeterhouse. Drs Fried and van Borkulo are funded by Consolidator Grant647209 from the European Research Council.Role of the Funder/Sponsor: The funding sources had no role in the design andconduct of the study; collection, management, analysis, and interpretation ofthe data; preparation, review, or approval of the manuscript; or the decision tosubmit the manuscript for publication.Additional Contributions: We thank all members of the IMPACT consortium.1. Goodyer IM, Reynolds S, Barrett B, et al. Cognitive behavioural therapy andshort-term psychoanalytical psychotherapy versus a brief psychosocialintervention in adolescents with unipolar major depressive disorder (IMPACT):a multicentre, pragmatic, observer-blind, randomised controlled superioritytrial. Lancet Psychiatry. 2017;4(2):109-119.2. Nilsen TS, Eisemann M, Kvernmo S. Predictors and moderators of outcome inchild and adolescent anxiety and depression: a systematic review ofpsychological treatment studies. Eur Child Adolesc Psychiatry. 2013;22(2):69-87.3. van Borkulo C, Boschloo L, Borsboom D, Penninx BWJH, Waldorp LJ,Schoevers RA. Association of symptom network structure with the course of[corrected] depression. JAMA Psychiatry. 2015;72(12):1219-1226.4. Daviss WB, Birmaher B, Melhem NA, Axelson DA, Michaels SM, Brent DA.Criterion validity of the Mood and Feelings Questionnaire for depressiveepisodes in clinic and non-clinic subjects. J Child Psychol Psychiatry. 2006;47(9):927-934.5. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D.Qgraph: network visualizations of relationships in psychometric data. J Stat Soft.2012;48(4):1-18.6. van Borkulo CD, Waldorp LJ, Boschloo L, et al. Comparing networkstructures on three aspects: a permutation test. ResearchGate. Published March2017. Accessed May 4, 2017. doi:10.13140/RG.2.2.29455.38569Figure 2. Differences in Total Mood and Feelings Questionnaire (MFQ)Mean Score, SD of the Mean Score, and Node Strength by SymptomBetween Good and Poor Responders to Treatment032Mean Total MFQ Score1dep wap ins hyp agi ret ene gui con suiNodeintPoor responderGood responderMeanA01.51.0SD of Total MFQ Score0.5dep wap ins hyp agi ret ene gui con suiNodeintStandard deviationB02.52.0Strength 1.51.00.5dep wap ins hyp agi ret ene gui con suiNodeintNode strengthCRed boxes outline statistically significant differences. Symptom (node)abbreviations are defined in the caption to Figure 1.Letters100 JAMA Psychiatry January 2018 Volume 75, Number 1 (Reprinted) jamapsychiatry.com© 2017 American Medical Association. All rights reserved.Downloaded From:  by a University of Amsterdam User  on 04/23/2018

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Borkulo, C.D. van

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    https://openaccess.leidenuniv.nl  License: Article 25fa pilot End User Agreement This publication is distributed under the terms of Article 25fa of the Dutch Copyright Act (Auteurswet) with explicit consent by the author. Dutch law entitles the maker of a short scientific work funded either wholly or partially by Dutch public funds to make that work publicly available for no consideration following a reasonable period of time after the work was first published, provided that clear reference is made to the source of the first publication of the work.  This publication is distributed under The Association of Universities in the Netherlands (VSNU) ‘Article 25fa implementation’ pilot project. In this pilot research outputs of researchers employed by Dutch Universities that comply with the legal requirements of Article 25fa of the Dutch Copyright Act are distributed online and free of cost or other barriers in institutional repositories. Research outputs are distributed six months after their first online publication in the original published version and with proper attribution to the source of the original publication.  You are permitted to download and use the publication for personal purposes. All rights remain with the author(s) and/or copyrights owner(s) of this work. Any use of the publication other than authorised under this licence or copyright law is prohibited. If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please contact the Library through email: OpenAccess@library.leidenuniv.nl    Article details Schweren L., Borkulo C.D. van, Fried E.I. & Goodyer I.M. (2018), Assessment of Symptom Network Density as a Prognostic Marker of Treatment Response in Adolescent Depression,  75(1): 98-100. Doi: 10.1001/jamapsychiatry.2017.3561 Assessment of Symptom Network Densityas a Prognostic Marker of Treatment Responsein Adolescent DepressionOne in 4 adolescents with depression does not respond favor-ably to treatment.1 Prognostic markers to identify this nonre-sponder group are lacking and urgently needed.2 It has been suggested that the network structure of depressive symp-toms (ie, group-level covariance or connectivity between symp-toms) may be informative in this regard.3 Intuitively, one may expect that more densely connected networks would be more inclined to result in negative spirals (eg, sleeplessness causes an individual to be too tired to go out, which leads to a lack of friends, resulting in sadness) and therefore more liable to non-response. An influential naturalistic study by van Borkulo et al published in JAMA Psychiatry3 reported that adult patients with depression who continue to experience problems in sub-Letters98 jamapsychiatry.com© 2017 American Medical Association. All rights reserved.sequent years have more densely connected networks at base-line than patients who later recover. Here, we performed a con-ceptual replication of that study in adolescents with depressionwho participated in a psychological treatment trial. We testedwhether network characteristics at baseline were prognosticfor long-term outcomes.1Methods | Patients with depression completed the 33-itemMood and Feelings Questionnaire (MFQ) prior to treatment,with regular additional assessments up to 12 months afterthe end of treatment (ie, mean [SD], 22 [4.7] months afterbaseline assessment). The MFQ assesses recent self-reporteddepressive symptoms on a 4-point scale (never, sometimes,often, and always), with total scores ranging from 0 to 66(higher scores indicate more severe depressive symptoms).4In accordance with van Borkulo et al,3 11 items optimallyrepresenting DSM-5 symptoms of depression were selectedfor use in the present study conducted from June 29, 2010,to January 17, 2013, with data analyses performed from Feb-ruary 1 to June 25, 2017. The study was approved by theCambridgeshire Research Ethics Committee and localNational Health Service provider trusts. All patients and par-ents gave written informed consent.To derive 2 equally sized groups, relatively good and poorresponders were differentiated by the median percentagechange between baseline and final follow-up of the MFQ sum-mary score (median [interquartile range], −66% [41.2%]). Base-line regularized partial correlation matrices were estimatedbased on Spearman correlation coefficients.5 From those, theweighted sum of all absolute connections in the network (net-work density) as well as in each node (node strength) was de-rived. Parameters were compared using permutation testing(global α = .05; adjusted α per node per item, .05/11 = .005;1-sided).6 All analyses were performed using R, version 3.2.4(The R Foundation), with the qgraph package, version 1.3.5.Results | The cohort consisted of 465 adolescents withdepression (349 [75.1%] were girls; aged 11-17 years). Goodresponders had higher mean (SD) MFQ summary scores atbaseline (47.5 [9.2] vs 44.3 [11.6]; P = .006) and higher mean(SD) levels of suicidality (1.25 [0.9] vs 1.00 [0.9]; P < .001)compared with poor responders. Although global networkstrength was higher in poor responders, the difference wasnot significant (good responders, 3.6; poor, 4.3; P = .15;Figure 1). There were no differences in local node strengthexcept for that of “concentration problems,” which at theuncorrected α level was more connected to the other nodesin the poor responders than in the good responders (goodresponders, 1.1; poor responders, 2.0; P = .02; Figure 2). Sen-sitivity analyses indicated similar findings when treatmentresponse was defined as below clinical threshold (ie, MFQscore <27) at the final follow-up.4Discussion | Applying the same statistical methods as thoseused in the study by van Borkulo et al,3 which had a simi-larly sized sample of adults with depression followed upnaturalistically, we found no significant association betweenhigher network strength and poorer outcomes. That thedirection of the association in our study was consistent withthe results of the previous study, however, indicates thatfurther investigation of the validity of network strength as aprognostic marker is warranted. There were 2 importantmethodological differences between the studies. First, theprevious one was a naturalistic cohort study, whereas herewe evaluated treatment outcomes. Stronger symptom net-works may be prognostic of depression persistence in natu-ralistic settings but not when symptoms (and perhaps net-works) are actively being challenged. Second, networkdensity may not have equal prognostic value in adult andadolescent groups. For instance, denser networks may be aconsequence and a behavioral marker of longer illness dura-tion or recurrent episodes but have no such signature infirst-episode depression in adolescents. Unfortunately, thishypothesis cannot be tested within the current sample ofadolescents with depression, most of whom were experienc-ing their first episode.Figure 1. Network Structures of Depressive Symptoms in 233 Relatively Good and 232 Poor Respondersto TreatmentdepintwapinshypagireteneguiconsuidepintwapinshypagireteneguiconsuiGood respondersA Poor respondersBBlue lines represent positiveconnections; red lines, negativeconnections; and thicker lines,stronger connections. For thesymptom (node) abbreviations,agi indicates psychomotor agitation;con, concentration problems;dep, feeling sad/depressed; ene, lossof energy; gui, guilt/worthlessness;hyp, hypersomnia; ins, insomnia;int, loss of interest/pleasure;ret, psychomotor retardation;sui, suicidal ideation; and wap, weightor appetite change.Lettersjamapsychiatry.com (Reprinted) JAMA Psychiatry January 2018 Volume 75, Number 1 99© 2017 American Medical Association. All rights reserved.With network analyses taking an astonishing flight in psy-chiatry, we recommend cautious application of group-level net-work density as a prognostic marker. Crucial steps to be takenby the field include further replication studies as well as in-depth psychometric evaluation of the reliability and clinicalcorrelates of network parameters.Lizanne Schweren, PhDClaudia D. van Borkulo, MScEiko Fried, PhDIan M. Goodyer, MDAuthor Affiliations: Department of Psychiatry, University of Cambridge,Cambridge, United Kingdom (Schweren, Goodyer); Department of Psychology,University of Amsterdam, Amsterdam, the Netherlands (van Borkulo, Fried).Corresponding Author: Lizanne Schweren, PhD, Department of Psychiatry,University of Cambridge, 18b Trumpington Rd, Douglas House, Cambridge CB28AH, United Kingdom (ljs82@medschl.cam.ac.uk).Accepted for Publication: October 3, 2017.Published Online: November 29, 2017. doi:10.1001/jamapsychiatry.2017.3561Author Contributions: Drs Schweren and Fried had full access to all of the datain the study and take responsibility for the integrity of the data and the accuracyof the data analysis.Study concept and design: Schweren, van Borkulo, Goodyer.Acquisition, analysis, or interpretation of data: All authors.Drafting of the manuscript: Schweren, Goodyer.Critical revision of the manuscript for important intellectual content: All authors.Statistical analysis: All authors.Obtained funding: Goodyer.Administrative, technical, or material support: Schweren, Goodyer.Study Supervision: Fried.Conflict of Interest Disclosures: Dr Goodyer reported serving as a paidconsultant for Lundbeck and licensing the Brief Psychosocial Intervention toLundbeck for use in a clinical trial. No other disclosures were reported.Funding/Support: The Improving Mood with Psychoanalytic Psychotherapyand Cognitive Behaviour Therapy (IMPACT) trial was funded by the HealthTechnology Assessment program of the National Institute for Health Research(06/05/01). Dr Schweren is a research associate funded by the Friends ofPeterhouse. Drs Fried and van Borkulo are funded by Consolidator Grant647209 from the European Research Council.Role of the Funder/Sponsor: The funding sources had no role in the design andconduct of the study; collection, management, analysis, and interpretation ofthe data; preparation, review, or approval of the manuscript; or the decision tosubmit the manuscript for publication.Additional Contributions: We thank all members of the IMPACT consortium.1. Goodyer IM, Reynolds S, Barrett B, et al. Cognitive behavioural therapy andshort-term psychoanalytical psychotherapy versus a brief psychosocialintervention in adolescents with unipolar major depressive disorder (IMPACT):a multicentre, pragmatic, observer-blind, randomised controlled superioritytrial. Lancet Psychiatry. 2017;4(2):109-119.2. Nilsen TS, Eisemann M, Kvernmo S. Predictors and moderators of outcome inchild and adolescent anxiety and depression: a systematic review ofpsychological treatment studies. Eur Child Adolesc Psychiatry. 2013;22(2):69-87.3. van Borkulo C, Boschloo L, Borsboom D, Penninx BWJH, Waldorp LJ,Schoevers RA. Association of symptom network structure with the course of[corrected] depression. JAMA Psychiatry. 2015;72(12):1219-1226.4. Daviss WB, Birmaher B, Melhem NA, Axelson DA, Michaels SM, Brent DA.Criterion validity of the Mood and Feelings Questionnaire for depressiveepisodes in clinic and non-clinic subjects. J Child Psychol Psychiatry. 2006;47(9):927-934.5. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D.Qgraph: network visualizations of relationships in psychometric data. J Stat Soft.2012;48(4):1-18.6. van Borkulo CD, Waldorp LJ, Boschloo L, et al. Comparing networkstructures on three aspects: a permutation test. ResearchGate. Published March2017. Accessed May 4, 2017. doi:10.13140/RG.2.2.29455.38569Figure 2. Differences in Total Mood and Feelings Questionnaire (MFQ)Mean Score, SD of the Mean Score, and Node Strength by SymptomBetween Good and Poor Responders to Treatment032Mean Total MFQ Score1dep wap ins hyp agi ret ene gui con suiNodeintPoor responderGood responderMeanA01.51.0SD of Total MFQ Score0.5dep wap ins hyp agi ret ene gui con suiNodeintStandard deviationB02.52.0Strength 1.51.00.5dep wap ins hyp agi ret ene gui con suiNodeintNode strengthCRed boxes outline statistically significant differences. Symptom (node)abbreviations are defined in the caption to Figure 1.Letters100 JAMA Psychiatry January 2018 Volume 75, Number 1 (Reprinted) jamapsychiatry.com© 2017 American Medical Association. All rights reserved.

Assessment of Symptom Network Density as a Prognostic Marker of Treatment Response in Adolescent Depression.

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