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Frontolimbic Network Topology Associated With Risk and Presence of Depression in Adolescents: A Study Using a Composite Risk Score in Brazil

  • Leehyun Yoon
    Affiliations
    Department of Human Ecology, University of California, Davis, Davis, California
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  • Fernanda Rohrsetzer
    Affiliations
    Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
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  • Lucas Battel
    Affiliations
    Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil

    Department of Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York
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  • Mauricio Anés
    Affiliations
    Division of Medical Physics and Radioprotection, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil
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  • Pedro H. Manfro
    Affiliations
    Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
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  • Luis A. Rohde
    Affiliations
    Institute of Developmental Psychiatry for Children and Adolescents, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil

    ADHD Outpatient and Developmental Psychiatry Programs, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil
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  • Anna Viduani
    Affiliations
    Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
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  • Zuzanna Zajkowska
    Affiliations
    Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, King’s College London, London, United Kingdom
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  • Valeria Mondelli
    Affiliations
    Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, King’s College London, London, United Kingdom

    National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health Service Foundation Trust, King’s College London, London, United Kingdom
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  • Author Footnotes
    1 CK and JRS contributed equally to this work as joint senior authors.
    Christian Kieling
    Footnotes
    1 CK and JRS contributed equally to this work as joint senior authors.
    Affiliations
    Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
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  • Author Footnotes
    1 CK and JRS contributed equally to this work as joint senior authors.
    Johnna R. Swartz
    Correspondence
    Address correspondence to Johnna R. Swartz, Ph.D.
    Footnotes
    1 CK and JRS contributed equally to this work as joint senior authors.
    Affiliations
    Department of Human Ecology, University of California, Davis, Davis, California
    Search for articles by this author
  • Author Footnotes
    1 CK and JRS contributed equally to this work as joint senior authors.
Open AccessPublished:March 27, 2022DOI:https://doi.org/10.1016/j.bpsc.2022.03.008

      Abstract

      Background

      There have been significant challenges in understanding functional brain connectivity associated with adolescent depression, including the need for a more comprehensive approach to defining risk, the lack of representation of participants from low- and middle-income countries, and the need for network-based approaches to model connectivity. The current study aimed to address these challenges by examining resting-state functional connectivity of frontolimbic circuitry associated with the risk and presence of depression in adolescents in Brazil.

      Methods

      Adolescents in Brazil ages 14 to 16 years were classified into low-risk, high-risk, and depressed groups using a clinical assessment and composite risk score that integrates 11 sociodemographic risk variables. After excluding participants with excessive head movement, resting-state functional magnetic resonance imaging data of 126 adolescents were analyzed. We compared group differences in frontolimbic network connectivity using region of interest–to–region of interest, graph theory, and seed-based connectivity analyses. Associations between self-reported depressive symptoms and brain connectivity were also explored.

      Results

      Adolescents with depression showed greater dorsal anterior cingulate cortex (ACC) connectivity with the orbitofrontal cortex compared with the 2 risk groups and greater dorsal ACC global efficiency than the low-risk group. Adolescents with depression also showed reduced local efficiency and a lower clustering coefficient of the subgenual ACC compared with the 2 risk groups. The high-risk group also showed a lower subgenual ACC clustering coefficient relative to the low-risk group.

      Conclusions

      These findings highlight altered connectivity and topology of the ACC within frontolimbic circuitry as potential neural correlates and risk factors of developing depression in adolescents in Brazil. This study broadens our understanding of the neural connectivity associated with adolescent depression in a global context.

      Keywords

      Depression is the leading cause of disability among adolescents across the globe (
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      ). Characterizing neurobiological risk factors and correlates of depression in adolescents could lead to improvements in preventing and treating adolescent depression. Prior research has identified several patterns of resting-state functional connectivity (rsFC) associated with the risk or presence of depression (
      • Toenders Y.J.
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      Resting-state functional connectivity of subgenual anterior cingulate cortex in depressed adolescents.
      ,
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      • Sacchet M.D.
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      Resting-state functional connectivity of the amygdala and longitudinal changes in depression severity in adolescent depression.
      ,
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      • Jolles D.D.
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      Aberrant resting-state functional connectivity in limbic and salience networks in treatment-naive clinically depressed adolescents.
      ,
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      ,
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      Decreased anticipated pleasure correlates with increased salience network resting state functional connectivity in adolescents with depressive symptomatology.
      ,
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      • Camacho M.C.
      • Ho T.C.
      • Whitfield-Gabrieli S.
      • Gotlib I.H.
      Neural markers of resilience in adolescent females at familial risk for major depressive disorder.
      ). However, a number of barriers to a comprehensive understanding of rsFC in adolescent depression remain, which if addressed could clarify our understanding of the neural correlates of risk and presence of depression in adolescents.
      First, previous studies examining rsFC in relation to adolescent depression have used either a case-control approach (depressed vs. control) or a risk approach (high risk vs. low risk). Research is needed that combines these approaches to compare low-risk (LR) adolescents, high-risk (HR) adolescents, and adolescents with major depressive disorder (MDD) to distinguish between potential neural risk factors and neural correlates of depression. Second, studies with a risk approach have generally relied on parental history of depression as the risk factor (
      • Fischer A.S.
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      • Ho T.C.
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      Neural markers of resilience in adolescent females at familial risk for major depressive disorder.
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      ), and some of them may have a high risk for depression based on other factors (e.g., social isolation). Moreover, parental history of depression can be accurately reported only by interviewing parents. Examining risk factors that can be directly reported by the adolescent could have future novel applications for identifying and preventing depression risk in a range of settings. A more comprehensive and accessible risk assessment is needed. Third, the majority of rsFC studies on adolescent depression have used a seed-based approach, restricting our knowledge to the connectivity of particular brain regions. An alternative and more comprehensive approach is a network-based approach (
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      ) that examines strength of connectivity or organizational patterns (e.g., number of connections, efficiency of connections) across multiple regions within a network of interest. Only a few studies (
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      ) have applied network-based approaches targeting the whole brain or the reward-related network. More studies with a network-based approach targeting the frontolimbic network, a network important for adolescent depression and affective development, are needed. Finally, only 38% of rsFC studies on adolescent depression were conducted in low- and middle-income countries (LMIC), and the majority of studies in LMIC (i.e., 13 out of 15) were from China (
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      ), studies in LMIC are urgently needed to address adolescent depression research disparities across the globe.
      To overcome these barriers, the current study compared rsFC of 39 LR adolescents, 45 HR adolescents, and 42 adolescents with MDD recruited in Brazil, which is classified as a middle-income country according to the World Bank (
      WorldBank
      World bank country and lending groups.
      ). To address the limitations of prior research that used parental history of depression to determine depression risk, adolescents in this study were classified using a clinical assessment and an empirically validated multivariable prognostic model (
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      ) that integrated 11 sociodemographic variables (e.g., childhood maltreatment, social isolation), which has been shown to predict depression risk across a variety of countries, including Brazil, Nepal, the United Kingdom, and Nigeria (
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      ). Moreover, to better understand aberrant functional neural architecture in high-risk and depressed adolescents, in addition to seed-based connectivity analysis, this study employed a network-based approach targeting frontolimbic circuitry implicated in both adolescent affective brain development (
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      Beyond simple models of self-control to circuit-based accounts of adolescent behavior.
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      ).
      The connectivity within the frontolimbic circuit changes significantly during adolescence (
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      ), and changing interactions between the amygdala, striatum, and prefrontal cortex (PFC) are theorized to underlie the development of affective functions (
      • Casey B.
      Beyond simple models of self-control to circuit-based accounts of adolescent behavior.
      ,
      • Casey B.
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      Development of the emotional brain.
      ), such as emotion regulation (
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      ), and cognitive control to emotional cues (
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      Changes in cortico-subcortical and subcortico-subcortical connectivity impact cognitive control to emotional cues across development.
      ). Moreover, frontolimbic connectivity was found to be affected by adverse early experiences (
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      Early developmental emergence of human amygdala-prefrontal connectivity after maternal deprivation.
      ), institutional care (
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      ), and trauma exposure (
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      ). This suggests that abnormal frontolimbic connectivity and development may be significantly associated with risk for and development of adolescent depression (
      • Jones S.A.
      • Morales A.M.
      • Lavine J.B.
      • Nagel B.J.
      Convergent neurobiological predictors of emergent psychopathology during adolescence.
      ,
      • Hulvershorn L.A.
      • Cullen K.
      • Anand A.
      Toward dysfunctional connectivity: A review of neuroimaging findings in pediatric major depressive disorder.
      ). Indeed, the majority of adolescent depression rsFC studies have focused on the amygdala (
      • Davey C.
      • Whittle S.
      • Harrison B.
      • Simmons J.
      • Byrne M.
      • Schwartz O.
      • et al.
      Functional brain-imaging correlates of negative affectivity and the onset of first-episode depression.
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      • Tymofiyeva O.
      • et al.
      Resting-state functional connectivity of the amygdala and longitudinal changes in depression severity in adolescent depression.
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      ), anterior cingulate cortex (ACC) (
      • Connolly C.G.
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      • Ho T.C.
      • Hoeft F.
      • Wolkowitz O.
      • Eisendrath S.
      • et al.
      Resting-state functional connectivity of subgenual anterior cingulate cortex in depressed adolescents.
      ,
      • Rzepa E.
      • McCabe C.
      Decreased anticipated pleasure correlates with increased salience network resting state functional connectivity in adolescents with depressive symptomatology.
      ,
      • Cullen K.R.
      • Gee D.G.
      • Klimes-Dougan B.
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      A preliminary study of functional connectivity in comorbid adolescent depression.
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      • Lee J.
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      Resting-state functional connectivity in medication-naïve adolescents with major depressive disorder.
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      Mapping the relationship between subgenual cingulate cortex functional connectivity and depressive symptoms across adolescence.
      ), PFC (
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      • Biederman J.
      • Uchida M.
      • Doehrmann O.
      • Leonard J.A.
      • et al.
      Altered intrinsic functional brain architecture in children at familial risk of major depression.
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      Orbitofrontal connectivity is associated with depression and anxiety in marijuana-using adolescents.
      ), striatal regions (
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      Striatum-based circuitry of adolescent depression and anhedonia.
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      Resting-state functional connectivity in medication-naïve adolescents with major depressive disorder.
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      ) and found their altered connectivity with other brain regions in high-risk and depressed adolescents (e.g., decreased amygdala–medial PFC [mPFC]/ACC connectivity, increased ACC–ventromedial PFC connectivity). Interestingly, a study (
      • Jin J.
      • Van Snellenberg J.X.
      • Perlman G.
      • DeLorenzo C.
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      • Kotov R.
      • et al.
      Intrinsic neural circuitry of depression in adolescent females.
      ) using predictive modeling found that rsFC within the nodes of frontolimbic circuitry, but not whole-brain connectivity, predicted both current and future depressive symptoms measured after 18 months, highlighting its high relevance to adolescent depression.
      The current study aimed to examine the strength and topology of frontolimbic network connectivity associated with the risk and presence of depression in Brazilian adolescents. Given that the above-mentioned study (
      • Jin J.
      • Van Snellenberg J.X.
      • Perlman G.
      • DeLorenzo C.
      • Klein D.N.
      • Kotov R.
      • et al.
      Intrinsic neural circuitry of depression in adolescent females.
      ) examining rsFC of frontolimbic nodes in adolescents observed that positive dorsal ACC (dACC) connectivity with other regions exhibited the greatest contribution to predicting depressive symptoms, we hypothesized that adolescents with high risk for depression and depressed adolescents in our sample would show stronger (i.e., greater rsFC), more efficient (i.e., greater global efficiency, lower average path length), and a greater number of connections (i.e., greater degree) of the same dACC node compared with the LR adolescents. To test this hypothesis, we used region of interest (ROI)–to–ROI analysis and graph theory analysis. Owing to the nascent literature that has used network-based approaches for frontolimbic rsFC, we also explored group differences in connectivity strength and topological properties of all the regions within the frontolimbic network. We additionally conducted amygdala seed-based connectivity analysis to see whether the most well-established connectivity pattern in the depression literature [i.e., reduced amygdala–mPFC/ACC connectivity identified using a seed-based approach (
      • Connolly C.G.
      • Ho T.C.
      • Blom E.H.
      • LeWinn K.Z.
      • Sacchet M.D.
      • Tymofiyeva O.
      • et al.
      Resting-state functional connectivity of the amygdala and longitudinal changes in depression severity in adolescent depression.
      ,
      • Pannekoek J.N.
      • Van Der Werff S.
      • Meens P.H.
      • van den Bulk B.G.
      • Jolles D.D.
      • Veer I.M.
      • et al.
      Aberrant resting-state functional connectivity in limbic and salience networks in treatment-naive clinically depressed adolescents.
      ,
      • Rzepa E.
      • McCabe C.
      Decreased anticipated pleasure correlates with increased salience network resting state functional connectivity in adolescents with depressive symptomatology.
      ,
      • Fischer A.S.
      • Camacho M.C.
      • Ho T.C.
      • Whitfield-Gabrieli S.
      • Gotlib I.H.
      Neural markers of resilience in adolescent females at familial risk for major depressive disorder.
      ,
      • Burghy C.A.
      • Stodola D.E.
      • Ruttle P.L.
      • Molloy E.K.
      • Armstrong J.M.
      • Oler J.A.
      • et al.
      Developmental pathways to amygdala-prefrontal function and internalizing symptoms in adolescence.
      ,
      • Herringa R.J.
      • Birn R.M.
      • Ruttle P.L.
      • Burghy C.A.
      • Stodola D.E.
      • Davidson R.J.
      • et al.
      Childhood maltreatment is associated with altered fear circuitry and increased internalizing symptoms by late adolescence.
      ,
      • Kim S.M.
      • Park S.Y.
      • Kim Y.I.
      • Son Y.D.
      • Chung U.S.
      • Min K.J.
      • et al.
      Affective network and default mode network in depressive adolescents with disruptive behaviors.
      ,
      • Kaiser R.H.
      • Andrews-Hanna J.R.
      • Wager T.D.
      • Pizzagalli D.A.
      Large-scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity.
      ,
      • Marusak H.
      • Thomason M.
      • Peters C.
      • Zundel C.
      • Elrahal F.
      • Rabinak C.
      You say ‘prefrontal cortex’and I say ‘anterior cingulate’: Meta-analysis of spatial overlap in amygdala-to-prefrontal connectivity and internalizing symptomology.
      )] would be replicated in our sample of Brazilian adolescents. We hypothesized that the HR and MDD groups would show reduced amygdala–mPFC/ACC connectivity compared with the LR group. Besides examining the association of connectivity with risk group status and clinical depression, we also explored the association of frontolimbic connectivity with subjectively experienced depressive symptoms using a self-reported, continuous measure, which allowed us to examine associations with depressive symptoms spanning the subclinical through clinical range.

      Methods and Materials

      Participants

      Participants were recruited for the IDEA-RiSCo (Identifying Depression Early in Adolescence Risk Stratified Cohort). Full details regarding the procedures of recruitment, screening, exclusion, clinical assessment, and questionnaires assessing sociodemographic variables are provided in the published protocol for the study (
      • Kieling C.
      • Buchweitz C.
      • Caye A.
      • Manfro P.
      • Pereira R.
      • Viduani A.
      • et al.
      The Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo): Rationale, methods, and baseline characteristics.
      ). Results of analyses with task-based functional magnetic resonance imaging (MRI) data using this cohort have been previously reported (
      • Yoon L.
      • Rohrsetzer F.
      • Battel L.
      • Anés M.
      • Manfro P.H.
      • Rohde L.A.
      • et al.
      Reward- and threat-related neural function associated with risk and presence of depression in adolescents: A study using a composite risk score in Brazil.
      ).
      For sampling adolescents who met the criteria of LR, HR, and MDD groups, 7720 adolescents 14 to 16 years of age were screened from June 2018 to November 2019 in Porto Alegre, Brazil. To stratify risk groups, we used a multivariable prognostic model, the IDEA Risk Score (IDEA-RS) (
      • Rocha T.B.
      • Fisher H.L.
      • Caye A.
      • Anselmi L.
      • Arseneault L.
      • Barros F.C.
      • et al.
      Identifying adolescents at risk for depression: A prediction score performance in cohorts based in 3 different continents.
      ). This model was developed by our group using data from the Pelotas 1993 Cohort Study (
      • Gonçalves H.
      • Wehrmeister F.C.
      • Assunção M.C.
      • Tovo-Rodrigues L.
      • Oliveira I.O.
      • Murray J.
      • et al.
      Cohort profile update: The 1993 Pelotas (Brazil) birth cohort follow-up at 22 years.
      ) and was recently validated in multiple countries (
      • Rocha T.B.
      • Fisher H.L.
      • Caye A.
      • Anselmi L.
      • Arseneault L.
      • Barros F.C.
      • et al.
      Identifying adolescents at risk for depression: A prediction score performance in cohorts based in 3 different continents.
      ,
      • Brathwaite R.
      • Rocha T.B.
      • Kieling C.
      • Gautam K.
      • Koirala S.
      • Mondelli V.
      • et al.
      Predicting the risk of depression among adolescents in Nepal using a model developed in Brazil: The IDEA project.
      ,
      • Brathwaite R.
      • Rocha T.B.
      • Kieling C.
      • Kohrt B.A.
      • Mondelli V.
      • Adewuya A.O.
      • et al.
      Predicting the risk of future depression among school-attending adolescents in Nigeria using a model developed in Brazil.
      ). This model integrates 11 sociodemographic variables (i.e., skin color, biological sex, school failure, drug use, fight involvement, ran away from home, social isolation, childhood maltreatment, poor relationship with mother, father, and between parents) and generates the probability of presenting with a unipolar depressive episode in 3 years.
      The LR group met the criteria of having a risk score equal to or below the 20th percentile of the Pelotas 1993 cohort, and the HR and MDD groups met the criteria of having a risk score equal to or above the 90th percentile of the Pelotas 1993 cohort. We required the MDD group to have high-risk sociodemographic profiles (an IDEA-RS equal to or above the 90th percentile) to attribute any neural differences between the HR and MDD groups only to the presence of depression.
      Presence of a current MDD episode (in the MDD group) or absence of a current or past MDD episode (in the LR and HR groups) was determined with the Brazilian Portuguese version of the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version (
      • Caye A.
      • Kieling R.R.
      • Rocha T.B.
      • Graeff-Martins A.S.
      • Geyer C.
      • Krieger F.
      • et al.
      Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version (K-SADS-PL), DSM-5 update: Translation into Brazilian Portuguese.
      ), conducted by a child psychiatrist. Self-reported instruments were administered, including the Brazilian Portuguese version of the adolescent-reported Mood and Feelings Questionnaire (MFQ-C) (
      • Rosa M.
      • Metcalf E.
      • Rocha T.B.
      • Kieling C.
      Translation and cross-cultural adaptation into Brazilian Portuguese of the Mood and Feelings Questionnaire (MFQ)–Long Version.
      ), measuring depressive symptoms. IQ was assessed using the Brazilian Portuguese version of the Wechsler Abbreviated Scale of Intelligence (
      • Yates D.B.
      • Trentini C.M.
      • Tosi S.D.
      • Corrêa S.K.
      • Poggere L.C.
      • Valli F.
      Apresentação da escala de inteligência Wechsler abreviada (WASI).
      ).
      To make our sample more homogeneous, we included only participants without long-term or current use of psychotropic medications. Percentages of participants with lifetime comorbid diagnoses are reported in the Supplement.
      After screening and clinical assessment, 150 participants who met inclusion criteria and did not meet exclusion criteria [see inclusion and exclusion criteria in the published protocol (
      • Kieling C.
      • Buchweitz C.
      • Caye A.
      • Manfro P.
      • Pereira R.
      • Viduani A.
      • et al.
      The Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo): Rationale, methods, and baseline characteristics.
      )] underwent MRI scanning from August 2018 to December 2019. Written informed assent and consent were obtained from adolescents and their caregivers, respectively, after the procedures had been fully explained. After exclusion of 24 participants with excessive head movement (i.e., greater than 20% volumes were censored), the sample size was 126 (LR group: n = 39; HR group: n = 45; MDD group: n = 42). This study was approved by the Brazilian National Ethics in Research Commission.

      Data Acquisition, Preprocessing, and Denoising

      All images were acquired on a 3T Ingenia (Philips Healthcare) MRI scanner. Structural MRI images were acquired before acquiring blood oxygen level–dependent functional MRI images for resting-state connectivity. Full details regarding the data acquisition parameters, preprocessing, and denoising are reported in the Supplement. The data were analyzed using CONN toolbox 18b. We confirmed that data from all participants had good signal coverage (i.e., signal coverage over 98% of voxels within each of our ROIs) (see Supplement for a specific method to inspect signal loss).

      ROI-to-ROI Analysis

      We estimated rsFC between all pairs of 47 ROIs (Figure 1; see Table S1 for the coordinates of the ROIs), which consisted of the frontolimbic circuitry or adolescent-depression network, as defined in a previous study (
      • Jin J.
      • Van Snellenberg J.X.
      • Perlman G.
      • DeLorenzo C.
      • Klein D.N.
      • Kotov R.
      • et al.
      Intrinsic neural circuitry of depression in adolescent females.
      ), which selected the bilateral amygdala, subregions of the striatum, the ACC, and the PFC from a functional brain atlas with 268 nodes (
      • Shen X.
      • Tokoglu F.
      • Papademetris X.
      • Constable R.T.
      Groupwise whole-brain parcellation from resting-state fMRI data for network node identification.
      ). We added 7 ROIs corresponding to the hippocampus from the same functional brain atlas given that the hippocampus is also a key frontolimbic region associated with early life stress (
      • Cohodes E.M.
      • Kitt E.R.
      • Baskin-Sommers A.
      • Gee D.G.
      Influences of early-life stress on frontolimbic circuitry: Harnessing a dimensional approach to elucidate the effects of heterogeneity in stress exposure.
      ) and depression in adolescents (
      • Lee J.
      • Pavuluri M.N.
      • Kim J.H.
      • Suh S.
      • Kim I.
      • Lee M.S.
      Resting-state functional connectivity in medication-naïve adolescents with major depressive disorder.
      ,
      • Geng H.
      • Wu F.
      • Kong L.
      • Tang Y.
      • Zhou Q.
      • Chang M.
      • et al.
      Disrupted structural and functional connectivity in prefrontal-hippocampus circuitry in first-episode medication-naive adolescent depression.
      ,
      • Herringa R.J.
      • Birn R.M.
      • Ruttle P.L.
      • Burghy C.A.
      • Stodola D.E.
      • Davidson R.J.
      • et al.
      Childhood maltreatment is associated with altered fear circuitry and increased internalizing symptoms by late adolescence.
      ). Pearson’s correlation coefficients were normalized through Fisher’s z-transformation.
      Figure thumbnail gr1
      Figure 1The 47 nodes of frontolimbic circuitry used for region of interest–to–region of interest and graph theory analysis. We adopted 40 nodes from a previous study (
      • Van Duijvenvoorde A.C.
      • Westhoff B.
      • de Vos F.
      • Wierenga L.M.
      • Crone E.A.
      A three-wave longitudinal study of subcortical-cortical resting-state connectivity in adolescence: Testing age- and puberty-related changes.
      ) that defined these nodes as an adolescent depression circuit. We added 7 nodes corresponding to hippocampus. ACC, anterior cingulate cortex; dPFC, dorsal prefrontal cortex; vPFC, ventral PFC.
      An analysis of covariance (ANCOVA) was conducted to examine group differences (LR vs. HR vs. MDD) in dACC (center of mass [mm]: 7, 21, 32) connectivity with all other nodes, controlling for age, sex, and head movement (i.e., mean framewise displacement). False discovery rate (FDR) correction was applied to correct for the number of target nodes (i.e., 46). Next, we explored group differences in connectivity in any pair of 47 frontolimbic nodes using ANCOVA. We combined the connection-level threshold (uncorrected p < .001) and network-based statistics FDR-corrected p (pFDR) (by intensity) < .05 (two-sided). Any pattern of connectivity that showed a significant group difference was submitted to a pairwise comparison t test (Tukey corrected) to specify the pattern of group difference. With the same statistical threshold, we conducted multiple regression analyses with the independent variable of log-transformed MFQ-C (self-reported depressive symptoms) and covariates of age, sex, and head movement.

      Graph Theory Analysis

      To identify group differences in the topological properties of the frontolimbic circuit, which consisted of 47 nodes (Figure 1), we used CONN’s automated protocol to construct individuals’ graph theory measures. This protocol thresholded each participant’s 47 × 47 correlation matrix to generate an adjacency matrix. We adopted cost thresholding for constructing the adjacency matrix. To illustrate, if the cost value (i.e., K) is 0.15, only the pairs with the highest 15% of the correlation coefficient values have a value of 1, and all other pairs have a value of 0. Based on 100 simulations that generated 4 unique optimal cost values (i.e., 0.1198, 0.12997, 0.14015, 0.14986) (see Figure S1 for details), we defined our graph theory measures by averaging graph theory measures obtained using each of the 4 optimal values. We report results from both one-sided cost thresholding that considers only positive correlations when defining the highest K% connections and two-sided cost thresholding that considers both positive and negative correlations when defining the highest K% connections.
      We first conducted an ANCOVA that examines the group difference of 6 graph theoretical measures of the dACC node: global efficiency, local efficiency, clustering coefficient, betweenness centrality, average path length, and degree (see Supplement for definitions). Age, sex, and head movement were entered as covariates. Next, we explored the group difference of graph theory measures of all other frontolimbic nodes. To correct for the number of nodes, we adopted a statistical threshold of two-sided pFDR < .05. To compare group differences in the integrated and segregated nature of the frontolimbic network as a whole, we conducted ANCOVA on the network-level global efficiency, local efficiency, and clustering coefficient (see Supplement for the calculation and choice of measures). Any measures that showed a significant group difference were submitted to a pairwise comparison t test (Tukey corrected) to specify the pattern of group difference. We conducted multiple regression analyses with the independent variable of log-transformed MFQ-C and covariates of age, sex, and head movement with the same statistical threshold to examine associations with continuous depression symptoms. To facilitate interpretation of the results, we identified the anatomical label of the nodes with significant results based on the Automated Anatomical Labeling atlas 3 (
      • Rolls E.T.
      • Huang C.C.
      • Lin C.P.
      • Feng J.
      • Joliot M.
      Automated anatomical labelling atlas 3.
      ).

      Seed-Based Connectivity Analysis

      Individuals’ rsFC maps with the seed regions of the left and right amygdala from the Automated Anatomical Labeling atlas 3 were estimated. We first conducted an ANCOVA with the search volume of an mPFC/ACC mask. The mPFC/ACC mask was created by combining medial orbitofrontal regions, rectus, superior medial prefrontal regions, and ACC of the Automated Anatomical Labeling atlas 3. We conducted a small-volume correction for the search region with familywise error rate correction provided in SPM12. The α value was divided by 2 to correct for 2 tests for left and right amygdala seeds. Then, whole-brain results were examined with a whole-brain pFDR < .025 threshold (two-sided). To explore the association between continuous self-reported depressive symptoms and amygdala connectivity with mPFC/ACC and whole brain, we ran multiple regression analyses with the independent variable of log-transformed MFQ-C and covariates of age, sex, and head movement. The same statistical threshold was used as ANCOVA.

      Outlier Treatment

      After identifying any significant result, we identified outliers, defined as 3 standard deviations from the mean of the risk group that each participant belonged to (for ANCOVA) or the mean of all participants (for the regression analysis). We planned to report only the results that remained significant after excluding the outliers (see Table S2 for the number of excluded participants).

      Results

      Demographic and Clinical Data

      Demographic and clinical data are presented in Table 1.
      Table 1Demographic, Clinical, and Head Motion Data
      LR Group (n = 39)HR Group (n = 45)MDD Group (n = 42)Analysis
      Categorical Variablesn (%)n (%)n (%)χ2p
      Sexχ22 = 0.11.95
       Females19 (48.72%)23 (51.11%)22 (52.38%)
       Males20 (51.28%)22 (48.89%)20 (47.62%)
      Skin Color/Race
      Categories were based on the Brazilian national census classification of race.
      χ28 = 5.41.71
       Black7 (17.95%)10 (22.22%)8 (19.05%)
       Brown7 (17.95%)12 (26.67%)7 (16.67%)
       Native Brazilian1 (2.56%)2 (4.44%)3 (7.14%)
       White24 (61.54%)21 (46.67%)23 (54.76%)
       Yellow0 (0%)0 (0%)1 (2.38%)
      Continuous VariablesMean (SD)Mean (SD)Mean (SD)F2,123p
      Age15.44 (0.74)15.82 (0.82)15.81 (0.79)3.03.052
      WASI IQ90.64 (10.64)88.09 (9.07)88.81 (10.14)0.72.49
      IDEA-RS
      IDEA-RS was developed with data from the Pelotas 1993 Cohort Study. Using 11 sociodemographic variables measured at age 15, the model predicted risk of a current unipolar depressive episode at age 18 years [see (18,19) for more details].
      , %
      1.30 (0.33)8.28 (4.54)9.59 (5.81)42.62< .001
      The effect was driven by the difference in LR < HR (t123 = 7.37, p < .001) and LR < MDD (t123 = 8.61, p < .001).
      MFQ-C
      Brazilian Portuguese version of the adolescent-reported MFQ-C.
      6.44 (4.71)12.73 (8.07)41.90 (11.17)206.75< .001
      The effect was driven by the difference in LR < HR (t123 = 3.40, p = .003), LR < MDD (t123 = 18.83, p < .001), and HR < MDD (t123 = 16.05, p < .001).
      Censored Number of Volumes12.44 (13.01)13.93 (10.23)14.31 (10.41)0.31.73
      Mean Framewise Displacement0.19 (0.05)0.20 (0.07)0.19 (0.05)0.32.73
      HR, high-risk; IDEA-RS, Identifying Depression Early in Adolescence Risk Score; LR, low-risk; MDD, major depressive disorder; MFQ-C, Mood and Feelings Questionnaire-Child; WASI, Wechsler Abbreviated Scale of Intelligence.
      a Categories were based on the Brazilian national census classification of race.
      b IDEA-RS was developed with data from the Pelotas 1993 Cohort Study. Using 11 sociodemographic variables measured at age 15, the model predicted risk of a current unipolar depressive episode at age 18 years [see (
      • Rocha T.B.
      • Fisher H.L.
      • Caye A.
      • Anselmi L.
      • Arseneault L.
      • Barros F.C.
      • et al.
      Identifying adolescents at risk for depression: A prediction score performance in cohorts based in 3 different continents.
      ,
      • Brathwaite R.
      • Rocha T.B.
      • Kieling C.
      • Gautam K.
      • Koirala S.
      • Mondelli V.
      • et al.
      Predicting the risk of depression among adolescents in Nepal using a model developed in Brazil: The IDEA project.
      ) for more details].
      c The effect was driven by the difference in LR < HR (t123 = 7.37, p < .001) and LR < MDD (t123 = 8.61, p < .001).
      d Brazilian Portuguese version of the adolescent-reported MFQ-C.
      e The effect was driven by the difference in LR < HR (t123 = 3.40, p = .003), LR < MDD (t123 = 18.83, p < .001), and HR < MDD (t123 = 16.05, p < .001).

      ROI-to-ROI Connectivity Within Frontolimbic Circuitry

      We found that dACC connectivity with the posterior orbitofrontal cortex (OFC) (center of mass: 27, 20, −21) showed a significant group difference (F2,119 = 8.98, pFDR = .01), with the effect driven by greater connectivity of the MDD group compared with the LR group (t119 = 3.86, p < .001) and HR group (t119 = 3.39, p = .003) (Figure 2). Effects of the exploratory analysis with other nodes were not significant.
      Figure thumbnail gr2
      Figure 2The significant group difference in connectivity between dorsal anterior cingulate cortex (dACC) and posterior orbitofrontal cortex (OFC). The left panel describes the location of dACC (blue) and posterior OFC (green), of which connectivity strength showed a significant group difference. The right panel describes the specific pattern of group differences in dACC-OFC connectivity. Error bar indicates standard error. ∗∗p ≤ .01; ∗∗∗p ≤ .001. HR, high-risk; LR, low-risk; MDD, major depressive disorder.

      Graph Theory Measures Within Frontolimbic Circuitry

      In the analysis with one-sided cost thresholding, the ANCOVA targeting dACC revealed a significant group difference in global efficiency driven by greater global efficiency of the MDD group compared with the LR group (Figure 3). In the analysis with two-sided cost thresholding, the analysis exploring all other frontolimbic nodes found significant group differences in local efficiency and clustering coefficient of left subgenual ACC (sgACC). For sgACC local efficiency, the group difference was driven by reduced local efficiency in the MDD group compared with the 2 risk groups. For the sgACC clustering coefficient, the group difference was driven by a decreasing pattern in the order of LR, HR, and MDD groups (Figure 4). The statistical values and center of mass of each node are presented in Table 2. Note that the results of the local efficiency and clustering coefficient of the sgACC could not include 18 participants because they did not have a neighboring subgraph, and 3 participants were excluded as outliers from the LE analysis (Table S2). There was no difference between included and excluded participants in clinical status and risk score (Tables S3 and S4).
      Figure thumbnail gr3
      Figure 3The significant group difference in dorsal anterior cingulate cortex (dACC) global efficiency. The left top panel describes the location of dACC (blue), of which global efficiency showed a significant group difference, and other nodes (gray) in the frontolimbic circuit. The left bottom panel describes the specific pattern of group differences in dACC global efficiency. Error bar indicates standard error. The right panel illustrates the high and low global efficiency of dACC. The data of participants with the highest and lowest dACC global efficiency were used. The graphs were generated using one of the optimal cost values (i.e., 0.14986). Circles and lines indicate nodes and edges, respectively. The circle with an asterisk indicates the dACC node. The brightness of nodes represents the shortest path length from dACC (a darker circle means shorter path length, and a lighter circle means longer path length). A participant with high dACC global efficiency has a greater number of dark circles, while a participant with low dACC global efficiency has a greater number of light circles. ∗p ≤ .05. dPFC, dorsal prefrontal cortex; HR, high-risk; LR, low-risk; MDD, major depressive disorder; vPFC, ventral PFC.
      Figure thumbnail gr4
      Figure 4The significant group difference in subgenual anterior cingulate cortex (sgACC) local efficiency and clustering coefficient. The top panel describes the location of sgACC (blue), of which local efficiency and clustering coefficient showed significant group differences, and other nodes (gray) in the frontolimbic circuitry. The middle panel illustrates the high and low local efficiency and clustering coefficient of sgACC. We used data of participants with the highest and lowest local efficiency and clustering coefficient among the participants who had 4 direct connections originating from sgACC. The graphs were generated using one of the optimal cost values (i.e., 0.14986). Circles and lines indicate nodes and edges, respectively. The circle with an asterisk indicates the sgACC node. The subgraph of a participant with the highest local efficiency and clustering coefficient shows that all the nodes in the subgraph are connected to each other, while the subgraph of a participant with the lowest local efficiency and clustering coefficient shows that only 2 connections are present between the nodes other than sgACC. The left bottom panel describes the specific pattern of group differences in sgACC local efficiency and clustering coefficient. The black dot indicates individuals’ data, and the error bar indicates standard error. ∗p ≤ .05; ∗∗p ≤ .01; ∗∗∗p ≤ .001. HR, high-risk; LR, low-risk; MDD, major depressive disorder.
      Table 2Statistical Values for Analysis of Covariance of Graph Theory Measures
      Graph Theory MeasuresNode (CoM)F StatisticsPairwise Comparison Statistics
      Global EfficiencyRight dACC (7, 21, 32)F2,118 = 3.34, p = .039LR < MDD: t118 = 2.419, p = .045
      HR < MDD: t118 = 1.951, p = .129
      LR < HR: t118 = 0.575, p = .834
      Local EfficiencyLeft sgACC (−5, 29, −10)F2,99 = 11.6, pFDR = .001LR > MDD: t99 = 4.71, p < .001
      HR > MDD: t99 = 3.07, p = .008
      LR > HR: t99 = 1.93, p = .135
      Clustering CoefficientLeft sgACC (−5, 29, −10)F2,102 = 11.47, pFDR = .002LR > MDD: t102 = 4.77, p < .001
      HR > MDD: t102 = 2.61, p = .028
      LR > HR: t102 = 2.41, p = .047
      CoM, center of mass; dACC, dorsal anterior cingulate cortex; FDR, false discovery rate; HR, high-risk; LR, low-risk; MDD, major depressive disorder; sgACC, subgenual ACC.
      Multiple regression analysis with MFQ-C showed that greater self-reported depressive symptoms are associated with greater dACC global efficiency (t120 = 2.23, p = .03) and degree (t121 = 2.24, p = .03) when using one-sided cost thresholding and reduced sgACC local efficiency (t100 = −4.01, pFDR = .005) and clustering coefficient (t103 = −3.89, pFDR = .008) when using two-sided cost thresholding (Figure 5). Network-level global efficiency, local efficiency, and clustering coefficient did not show significant group differences or associations with self-reported depressive symptoms.
      Figure thumbnail gr5
      Figure 5The association between self-reported depressive symptoms and dorsal anterior cingulate cortex (dACC) and subgenual ACC (sgACC) graph theory measures. The top panels are the partial regression plots that describe the positive association between self-reported depressive symptoms and dACC global efficiency and degree, controlling for age, sex, and head movement (i.e., mean framewise displacement). The bottom panels are the partial regression plots that describe the negative association between self-reported depressive symptoms and sgACC local efficiency and clustering coefficient, controlling for age, sex, and head movement (i.e., mean framewise displacement). The shaded area represents the 95% confidence interval.

      Amygdala Seed-Based Connectivity

      Amygdala connectivity with the regions within mPFC/ACC or the whole brain did not show group differences or associations with MFQ-C.

      Discussion

      The current study investigated how frontolimbic network connectivity is associated with the risk and presence of depression in Brazilian adolescents stratified using 11 sociodemographic variables and clinical assessment. We found that the MDD group showed greater dACC-OFC connectivity compared with the 2 risk groups and greater dACC global efficiency compared with the LR group. The MDD group showed reduced sgACC local efficiency and a lower clustering coefficient than the 2 risk groups, and the HR group showed a lower sgACC clustering coefficient than the LR group. Adolescents with greater self-reported depressive symptoms showed greater dACC global efficiency, greater dACC degree, reduced sgACC local efficiency, and a lower sgACC clustering coefficient. This study indicates that the risk and presence of adolescent depression in Brazil is associated with altered ACC connectivity patterns within frontolimbic circuitry.
      We found several connectivity patterns of frontolimbic circuitry associated with the presence of depression, which supports the theory suggesting the critical role of interactions of the regions within the frontolimbic circuit in adolescent affective development (
      • Casey B.
      Beyond simple models of self-control to circuit-based accounts of adolescent behavior.
      ,
      • Casey B.
      • Heller A.S.
      • Gee D.G.
      • Cohen A.O.
      Development of the emotional brain.
      ) and depression (
      • Jones S.A.
      • Morales A.M.
      • Lavine J.B.
      • Nagel B.J.
      Convergent neurobiological predictors of emergent psychopathology during adolescence.
      ,
      • Jin J.
      • Van Snellenberg J.X.
      • Perlman G.
      • DeLorenzo C.
      • Klein D.N.
      • Kotov R.
      • et al.
      Intrinsic neural circuitry of depression in adolescent females.
      ,
      • Hulvershorn L.A.
      • Cullen K.
      • Anand A.
      Toward dysfunctional connectivity: A review of neuroimaging findings in pediatric major depressive disorder.
      ). Interestingly, consistent with our hypothesis based on the recent discovery showing that dACC connectivity to other frontolimbic regions has a critical role in predicting depressive symptoms (
      • Jin J.
      • Van Snellenberg J.X.
      • Perlman G.
      • DeLorenzo C.
      • Klein D.N.
      • Kotov R.
      • et al.
      Intrinsic neural circuitry of depression in adolescent females.
      ), we found that adolescents with MDD showed greater dACC-OFC connectivity compared with adolescents in the other 2 groups and that they showed greater dACC global efficiency compared with adolescents in the LR group. This result indicates that greater and more efficient dACC connectivity may be a neural correlate or outcome of the development of depression in adolescents.
      In addition to the recent study by Jin et al. (
      • Jin J.
      • Van Snellenberg J.X.
      • Perlman G.
      • DeLorenzo C.
      • Klein D.N.
      • Kotov R.
      • et al.
      Intrinsic neural circuitry of depression in adolescent females.
      ), multiple studies have reported abnormal dACC connectivity in depressed adolescents. Two studies (
      • Wu B.
      • Li X.
      • Zhou J.
      • Zhang M.
      • Long Q.
      Altered whole-brain functional networks in drug-naïve, first-episode adolescents with major depression disorder.
      ,
      • Jin C.
      • Gao C.
      • Chen C.
      • Ma S.
      • Netra R.
      • Wang Y.
      • et al.
      A preliminary study of the dysregulation of the resting networks in first-episode medication-naive adolescent depression.
      ) showed heightened degree and efficiency of ACC in depressed adolescents in terms of connectivity with whole-brain regions, although the ACC was not divided into its subregions in these studies. Studies with a seed-based approach have also demonstrated that depressed adolescents showed heightened dACC rsFC with frontal (
      • Rzepa E.
      • McCabe C.
      Anhedonia and depression severity dissociated by dmPFC resting-state functional connectivity in adolescents.
      ) and striatal (
      • Gabbay V.
      • Ely B.A.
      • Li Q.
      • Bangaru S.D.
      • Panzer A.M.
      • Alonso C.M.
      • et al.
      Striatum-based circuitry of adolescent depression and anhedonia.
      ) regions and that depressive symptoms were associated with greater dACC rsFC with perigenual ACC (
      • Rzepa E.
      • McCabe C.
      Decreased anticipated pleasure correlates with increased salience network resting state functional connectivity in adolescents with depressive symptomatology.
      ). Based on the functions of dACC in salience detection (
      • Menon V.
      • Uddin L.Q.
      Saliency, switching, attention and control: A network model of insula function.
      ) and action selection based on reward contingency (
      • Hayden B.Y.
      • Platt M.L.
      Neurons in anterior cingulate cortex multiplex information about reward and action.
      ), we speculate that heightened resting-state global efficiency of dACC in depressed adolescents may be associated with extensive transfer of salience signals detected from negative and self-relevant information or selecting avoidance behavior by weighting expected cost and deweighting expected benefit of normally rewarding events (e.g., social activities). As the prominent role of OFC is value updating (
      • Rudebeck P.H.
      • Saunders R.C.
      • Lundgren D.A.
      • Murray E.A.
      Specialized representations of value in the orbital and ventrolateral prefrontal cortex: Desirability versus availability of outcomes.
      ), high dACC-OFC connectivity of depressed adolescents could be related to increased propensity to update the value of an object or behavior when it is associated with an outcome with high saliency, such as an unexpected or threatening experience. It should be noted that, in contrast to the dACC-OFC connectivity effect, there was no difference in dACC global efficiency between high-risk and depressed adolescents, which suggests that the high dACC global efficiency could not be attributed solely to the diagnosis of depression, but rather the combination of high-risk profiles and the clinical diagnosis.
      Adolescents with MDD also showed reduced sgACC local efficiency and clustering coefficient compared with adolescents in the LR and HR groups. The sgACC has been a major target of depression treatment through deep brain stimulation (
      • Berlim M.T.
      • McGirr A.
      • Van den Eynde F.
      • Fleck M.P.
      • Giacobbe P.
      Effectiveness and acceptability of deep brain stimulation (DBS) of the subgenual cingulate cortex for treatment-resistant depression: A systematic review and exploratory meta-analysis.
      ), as it is consistently implicated in depression, potentially owing to its critical role in affective processing, such as reappraising visceral signals (
      • Dixon M.L.
      • Thiruchselvam R.
      • Todd R.
      • Christoff K.
      Emotion and the prefrontal cortex: An integrative review.
      ), sustaining physiological arousal during anticipation of reward (
      • Rudebeck P.H.
      • Putnam P.T.
      • Daniels T.E.
      • Yang T.
      • Mitz A.R.
      • Rhodes S.E.
      • et al.
      A role for primate subgenual cingulate cortex in sustaining autonomic arousal.
      ), and self-reported distress after a negative experience such as social exclusion (
      • Rotge J.Y.
      • Lemogne C.
      • Hinfray S.
      • Huguet P.
      • Grynszpan O.
      • Tartour E.
      • et al.
      A meta-analysis of the anterior cingulate contribution to social pain.
      ). Studies have documented that adolescents with MDD showed reduced sgACC functional connectivity with distributed frontal cortical regions (
      • Cullen K.R.
      • Gee D.G.
      • Klimes-Dougan B.
      • Gabbay V.
      • Hulvershorn L.
      • Mueller B.A.
      • et al.
      A preliminary study of functional connectivity in comorbid adolescent depression.
      ,
      • Ho T.C.
      • Yang G.
      • Wu J.
      • Cassey P.
      • Brown S.D.
      • Hoang N.
      • et al.
      Functional connectivity of negative emotional processing in adolescent depression.
      ), and adolescents with greater depressive symptoms and more severe anhedonia showed reduced sgACC functional connectivity with mPFC (
      • Strikwerda-Brown C.
      • Davey C.G.
      • Whittle S.
      • Allen N.B.
      • Byrne M.L.
      • Schwartz O.S.
      • et al.
      Mapping the relationship between subgenual cingulate cortex functional connectivity and depressive symptoms across adolescence.
      ) and nucleus accumbens (
      • Gabbay V.
      • Ely B.A.
      • Li Q.
      • Bangaru S.D.
      • Panzer A.M.
      • Alonso C.M.
      • et al.
      Striatum-based circuitry of adolescent depression and anhedonia.
      ), respectively. Our results extend the finding of major disruption of sgACC connectivity in adolescent depression by demonstrating the reduced interconnectedness of its neighboring regions. Importantly, we also observed a reduced sgACC clustering coefficient but not local efficiency in the HR group compared with the LR group. This result indicates that while both a reduced number of direct connections and inefficient connections among the neighboring regions of the sgACC are neural correlates or outcomes of depression, only a reduced number of direct connections serves as a potential risk factor for developing depression.
      It is important to note that the observed abnormal efficiency of dACC and sgACC in depressed adolescents supports the recent proposal (
      • Lichenstein S.D.
      • Verstynen T.
      • Forbes E.E.
      Adolescent brain development and depression: A case for the importance of connectivity of the anterior cingulate cortex.
      ,
      • Ho T.C.
      • Sacchet M.D.
      • Connolly C.G.
      • Margulies D.S.
      • Tymofiyeva O.
      • Paulus M.P.
      • et al.
      Inflexible functional connectivity of the dorsal anterior cingulate cortex in adolescent major depressive disorder.
      ) suggesting that ACC connectivity is key for healthy behavioral development in multiple domains (e.g., relationships, achievement) owing to its hublike function of integrating multimodal inputs (e.g., social, cognitive, and visceral) to guide adaptive self-regulation. Interestingly, the analysis with self-reported depressive symptoms mirrored the findings from the group analysis, with an addition of greater number of edges originating from dACC (i.e., degree), suggesting that abnormal ACC topological properties are related not only to clinical diagnosis of depression but also to subjective experience of depressive symptoms across the subclinical to clinical range.
      Contrary to our expectation, there was no group difference in amygdala–mPFC/ACC connectivity, which has consistently been implicated in depression in adolescents and adults in high-income countries (
      • Kaiser R.H.
      • Andrews-Hanna J.R.
      • Wager T.D.
      • Pizzagalli D.A.
      Large-scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity.
      ,
      • Marusak H.
      • Thomason M.
      • Peters C.
      • Zundel C.
      • Elrahal F.
      • Rabinak C.
      You say ‘prefrontal cortex’and I say ‘anterior cingulate’: Meta-analysis of spatial overlap in amygdala-to-prefrontal connectivity and internalizing symptomology.
      ). It should be noted that we did find in another article using this sample that higher depressive symptoms were associated with decreased amygdala-mPFC connectivity during a face-matching task (
      • Yoon L.
      • Rohrsetzer F.
      • Battel L.
      • Anés M.
      • Manfro P.H.
      • Rohde L.A.
      • et al.
      Reward- and threat-related neural function associated with risk and presence of depression in adolescents: A study using a composite risk score in Brazil.
      ), suggesting that this association may become more apparent when connectivity is elicited by a stimulus, such as a threatening face.
      This study has several limitations. First, this study is cross-sectional. A longitudinal study that examines the intraindividual change in frontolimbic network topology before and after developing depression is needed to understand the timing of changes in topology in relation to developing depression. Second, although the IDEA-RS provides a more comprehensive approach to measuring depression risk, it did not include parental history of depression, as this cannot be assessed accurately through adolescent self-report. A future study is needed to systematically compare the IDEA-RS and parental history of depression to determine the advantages and disadvantages of the 2 approaches for understanding neural correlates of depression risk. Note that a study testing the predictive validity of the IDEA-RS within a different Brazilian sample found that the risk score improved prediction of depression risk above and beyond family history (
      • Rocha T.B.
      • Fisher H.L.
      • Caye A.
      • Anselmi L.
      • Arseneault L.
      • Barros F.C.
      • et al.
      Identifying adolescents at risk for depression: A prediction score performance in cohorts based in 3 different continents.
      ), suggesting that it may capture risk not captured by family history. Third, we did not examine whether the three topological properties altered in adolescents with MDD were associated with different symptom dimensions (e.g., decision-making ability, rumination, anhedonia). A future study that examines specific depression symptom dimensions would have implications for personalized treatment. Fourth, we did not collect a field map and did not apply distortion correction to the images, so results and/or lack of results for regions susceptible to distortion, such as the OFC, should be interpreted with caution.
      In conclusion, with an underrepresented and extensively phenotyped Brazilian adolescent sample, we found that aberrant connectivity of the ACC in frontolimbic circuitry may be involved in risk for and the presence of depression in adolescence. The present study provides the first evidence to our knowledge that high-risk adolescents and adolescents with clinical depression show altered topology of the frontolimbic network implicated in adolescent affective brain development and depression. These results advance our knowledge on the atypical neural architecture of adolescents with depression and depression risk, specifically in Brazil, and will ultimately contribute to the prevention and treatment of adolescent depression across the globe.

      Acknowledgments and Disclosures

      We are extremely grateful to the schools and individuals who participated in this study, and to all members of the IDEA team for their dedication, hard work, and insights. The IDEA project, to which this research belongs, is supported by the MQ Brighter Futures Programme (Grant No. MQBF/1 IDEA [to VM, CK, and JRS]), Medical Research Council (Grant No. MC_PC_MR/R019460/1 [to CK and VM]), and Academy of Medical Sciences (Grant No. GCRFNG∖100281 [to CK]) under the Global Challenges Research Fund. This work was also supported by research grants from the Brazilian public funding agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (Grant Nos. 477129/2012-9 and 445828/2014-5 [to CK and LAR]), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Grant No. 62/2014 [to CK]), and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant No. 17/2551-0001009-4 [to CK]). CK is a Conselho Nacional de Desenvolvimento Científico e Tecnológico researcher and an Academy of Medical Sciences Newton Advanced Fellow. VM is supported by the Medical Research Foundation (Grant No. MRF-160-0005-ELPMONDE ) and National Institute for Health Research Mental Health Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the National Health Service, National Institute for Health Research, or Department of Health and Social Care. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
      LY had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. LY, VM, CK, and JRS were responsible for study concept and design. All authors were responsible for acquisition, analysis, and interpretation of data. LY drafted the original manuscript. All authors critically revised the manuscript for important intellectual content. LY performed statistical analysis. LAR, VM, CK, and JRS obtained funding.
      The sociodemographic and self-reported measures were used and reported in another article (
      • Kieling C.
      • Buchweitz C.
      • Caye A.
      • Manfro P.
      • Pereira R.
      • Viduani A.
      • et al.
      The Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo): Rationale, methods, and baseline characteristics.
      ) that detailed the protocol for the larger IDEA-RiSCo study. A subset of resting-state functional MRI data (i.e., 29 adolescents in the MDD group) was used and reported in another article (
      • Battel L.
      • Swartz J.
      • Anes M.
      • Manfro P.H.
      • Rohde L.A.
      • Viduani A.
      • et al.
      Neuroimaging adolescents with depression in a middle-income country: Feasibility of an fMRI protocol and preliminary results.
      ), which was a preliminary study that examined the feasibility of MRI research in adolescents in Brazil. Task-based functional MRI data and sociodemographic and self-reported measures from this cohort of participants have been reported in another article (
      • Yoon L.
      • Rohrsetzer F.
      • Battel L.
      • Anés M.
      • Manfro P.H.
      • Rohde L.A.
      • et al.
      Reward- and threat-related neural function associated with risk and presence of depression in adolescents: A study using a composite risk score in Brazil.
      ).
      VM has received research funding from Johnson & Johnson, a pharmaceutical company interested in the development of anti-inflammatory strategies for depression, but the research described in this article is unrelated to this funding. All other authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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