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Mapping Neural Circuit Biotypes to Symptoms and Behavioral Dimensions of Depression and Anxiety

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    1 ANG-P and TMB contributed equally to this work as joint first authors.
    Andrea N. Goldstein-Piekarski
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    1 ANG-P and TMB contributed equally to this work as joint first authors.
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California

    Sierra-Pacific Mental Illness Research, Education, and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
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    1 ANG-P and TMB contributed equally to this work as joint first authors.
    Tali M. Ball
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    1 ANG-P and TMB contributed equally to this work as joint first authors.
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
    Zoe Samara
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
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    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
    Brooke R. Staveland
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    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
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    Arielle S. Keller
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California

    Department of Graduate Program in Neurosciences, Stanford University, Stanford, California
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
    Scott L. Fleming
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California

    Department of Biomedical Informatics, Stanford University, Stanford, California
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
    Katherine A. Grisanzio
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    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
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    Bailey Holt-Gosselin
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
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    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
    Patrick Stetz
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
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    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California

    Sierra-Pacific Mental Illness Research, Education, and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
    Jun Ma
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    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
    Affiliations
    Department of Medicine, University of Illinois at Chicago, Chicago, Illinois

    Institute for Health Research and Policy, University of Illinois at Chicago, Chicago, Illinois
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  • Leanne M. Williams
    Correspondence
    Address correspondence to Leanne M. Williams, Ph.D.
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California

    Sierra-Pacific Mental Illness Research, Education, and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
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  • Author Footnotes
    1 ANG-P and TMB contributed equally to this work as joint first authors.
    2 ZS, BRS, ASK, SLF, KAG, BH-G, PS, and JM contributed equally to this work.
Open AccessPublished:July 11, 2021DOI:https://doi.org/10.1016/j.biopsych.2021.06.024

      Abstract

      Background

      Despite tremendous advances in characterizing human neural circuits that govern emotional and cognitive functions impaired in depression and anxiety, we lack a circuit-based taxonomy for depression and anxiety that captures transdiagnostic heterogeneity and informs clinical decision making.

      Methods

      We developed and tested a novel system for quantifying 6 brain circuits reproducibly and at the individual patient level. We implemented standardized circuit definitions relative to a healthy reference sample and algorithms to generate circuit clinical scores for the overall circuit and its constituent regions.

      Results

      In new data from primary and generalizability samples of depression and anxiety (N = 250), we demonstrated that overall disconnections within task-free salience and default mode circuits map onto symptoms of anxious avoidance, loss of pleasure, threat dysregulation, and negative emotional biases—core characteristics that transcend diagnoses—and poorer daily function. Regional dysfunctions within task-evoked cognitive control and affective circuits may implicate symptoms of cognitive and valence-congruent emotional functions. Circuit dysfunction scores also distinguished response to antidepressant and behavioral intervention treatments in an independent sample (n = 205).

      Conclusions

      Our findings articulate circuit dimensions that relate to transdiagnostic symptoms across mood and anxiety disorders. Our novel system offers a foundation for deploying standardized circuit assessments across research groups, trials, and clinics to advance more precise classifications and treatment targets for psychiatry.

      Keywords

      Advances in noninvasive functional brain imaging suggest that distinct types of brain circuit dysfunctions may underlie the clinical expression of depression and anxiety disorders. Yet, we lack a method for quantifying clinical brain circuit metrics in a subject-level manner to facilitate actionable decisions. To make progress toward this goal, we leveraged multiple samples of depression and anxiety to develop and test a subject-level image system suitable for clinical applications.
      Our approach was informed by a prior theoretical synthesis of functional brain imaging studies that implicate dysfunction across 6 large-scale circuits in the clinical features of depression and anxiety and in their treatment (
      • Williams L.M.
      Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation.
      ,
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ) (Figure 1). These prior studies typically focused on case-control designs to understand group average dysfunctions, which, arguably, might conflate multiple underlying profiles of subject-level dysfunction. In the prior synthesis, we sought to parse types of circuit dysfunction that might contribute to specific clinical features and treatment outcomes. In the task-free state, intrinsic hyperconnectivity of the default mode circuit implicates rumination, while hypoconnectivity may reflect different symptoms and poorer antidepressant outcomes (
      • Williams L.M.
      Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation.
      ,
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ). Hypoconnectivity of insula and amygdala within the salience circuit is observed across mood and anxiety disorders, particularly implicating social anxiety and anxious avoidance (
      • Williams L.M.
      Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation.
      ,
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ). When evoked by tasks using threat stimuli, heightened amygdala activation and reduced amygdala-prefrontal connectivity have been observed across disorders, suggesting a common underlying threat-related circuit disruption (
      • Williams L.M.
      Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation.
      ,
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ). Within the positive affective circuit, striatal hypoactivation is implicated in reward-related behaviors characteristic of anhedonia (
      • Williams L.M.
      Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation.
      ,
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ). Frontoparietal attention circuit hypoconnectivity implicates poor attention symptoms in both depression and anxiety. Under task conditions, frontal hypoactivation within the cognitive control circuit is indicative of more task-specific cognitive symptoms (
      • Williams L.M.
      Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation.
      ,
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ).
      Figure thumbnail gr1
      Figure 1Hypothesized directional relationships between circuit scores and phenotypes assessed by symptoms and behavior. aFor full details of circuit scores and circuit clinical score, see and , and , and . bFor full details of composite measures of symptom phenotypes, see and and . cFor full details of composite measures of behavior phenotypes, see and and . For details of daily function measures included in exploratory analyses, not shown in , see and . dDorsal anterior cingulate cortex (dACC) was used for negative affect conscious threat and subgenual anterior cingulate cortex was used for negative affect nonconscious threat. ACC, anterior cingulate cortex; AG, angular gyrus; aI, anterior insula; aIPL, anterior inferior parietal lobule; amPFC, anterior medial PFC; dACC, dorsal ACC; DLPFC, dorsolateral PFC; FC, functional connectivity; L, left; LPFC, lateral PFC; msPFC, medial superior PFC; PCC, posterior cingulate cortex; PFC, prefrontal cortex; pgACC, pregunal ACC; PPI, psychophysiological interaction; R, right; RT, reaction time; vmPFC, ventromedial PFC.
      Informed by our theoretical synthesis (
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ), we tested the working hypotheses that specific types of circuit clinical function show a one-to-one association with specific clinical phenotypes (Figure 1). To test these hypotheses, we developed standardized definitions of activation and connectivity for 6 circuits of interest and a new method for quantifying circuit clinical scores for each circuit for each subject, expressed in standard deviation units from a healthy reference sample. We leveraged multiple samples, spanning healthy subjects, untreated clinical subjects, and subjects tested in both pharmacological and behavioral intervention trials, each assessed with common circuit and clinical data elements. These multiple samples afforded us the opportunity to address challenges inherent in developing a subject-level imaging system, including the lack of well-powered samples for which data can be pooled and used to test generalizability. Circuit clinical scores were tested for hypothesized associations with symptom and behavioral phenotypes in untreated samples. Circuit associations with daily function were also explored, relevant to the disabling effects of depression and anxiety (
      • Friedrich M.J.
      Depression is the leading cause of disability around the world.
      ). To further test the clinical relevance of our system, we evaluated whether circuit clinical scores distinguish intervention response outcomes.

      Methods and Materials

      Samples

      The study comprised 4 samples assessed with common measures (Tables S1 and S2; Supplemental Methods S5B):
      • 1.
        Healthy reference sample of 95 adults recruited at the same two sites as clinical subjects.
      • 2.
        Primary clinical sample of 160 adults with symptoms of depression and anxiety, randomly stratified into subsamples A (70%; n = 112) and B (30%; n = 48) powered to detect circuit-phenotype associations of small-to-medium size at α = 0.05 and control for overestimated effect sizes (
        • Button K.S.
        • Ioannidis J.P.
        • Mokrysz C.
        • Nosek B.A.
        • Flint J.
        • Robinson E.S.
        • et al.
        Power failure: Why small sample size undermines the reliability of neuroscience.
        ).
      • 3.
        Generalizability sample of 90 adults with clinical characteristics similar to the primary sample, yet independently recruited.
      • 4.
        Treatment sample of 205 adults, enrolled in randomized controlled trials of antidepressant pharmacotherapy for major depressive disorder (n = 137) (
        • Grieve S.M.
        • Korgaonkar M.S.
        • Etkin A.
        • Harris A.
        • Koslow S.H.
        • Wisniewski S.
        • et al.
        Brain imaging predictors and the international study to predict optimized treatment for depression: Study protocol for a randomized controlled trial.
        ,
        • Williams L.M.
        • Rush A.J.
        • Koslow S.H.
        • Wisniewski S.R.
        • Cooper N.J.
        • Nemeroff C.B.
        • et al.
        International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol.
        ) or behavioral intervention for clinically significant depressive symptoms and obesity (n = 68) (
        • Williams L.M.
        • Pines A.
        • Goldstein-Piekarski A.N.
        • Rosas L.G.
        • Kullar M.
        • Sacchet M.D.
        • et al.
        The ENGAGE study: Integrating neuroimaging, virtual reality and smartphone sensing to understand self-regulation for managing depression and obesity in a precision medicine model.
        ), in which treatment response was defined as ≥50% reduction in symptom severity.
      Subjects provided written informed consent. Procedures were approved by the Stanford University Institutional Review Board (IRB 27937 and 41837) or Western Sydney Area Health Service Human Research Ethics Committee.

      Derivation of Circuits

      A consensus definition was generated for circuits of interest using the meta-analytic database Neurosynth.org (
      • Yarkoni T.
      • Poldrack R.
      • Nichols T.
      • Van Essen D.
      • Wager T.
      NeuroSynth: A new platform for large-scale automated synthesis of human functional neuroimaging data.
      ) with search terms “default mode,” “salience,” “attention,” “threat,” “reward,” and “cognitive control” and uniformity maps with a false discovery rate (FDR) threshold of 0.01 (Figure 2A; Supplemental Methods S3 and S4A). Resulting region pairs were quantified for intrinsic functional connectivity after regressing out task effects (
      • Korgaonkar M.S.
      • Ram K.
      • Williams L.M.
      • Gatt J.M.
      • Grieve S.M.
      Establishing the resting state default mode network derived from functional magnetic resonance imaging tasks as an endophenotype: A twins study.
      ). Task-evoked activation was quantified for regions of interest and functional connectivity using psychophysiological interactions between these regions for the contrasts of sad versus neutral and threat versus neutral faces for negative affect circuit,
      Equivalent threat versus neutral contrasts were undertaken for stimuli presented under conscious and nonconscious conditions.
      happy versus neutral faces for positive affect circuit, and NoGo versus Go trials for cognitive control circuit (Supplemental Methods S4C) (Figure 2B).
      Figure thumbnail gr2
      Figure 2Quantifying circuits of interest. First, we identified 6 target circuits of interest relevant to depression and anxiety and identified potential regions in these circuits using the meta-analytic database and search tool Neurosynth.org. From top to bottom, these circuits are default mode (blue), salience (green), attention (yellow), negative affect (orange), positive affect (purple), and cognitive control (red) (A). To identify regions of interest (B), we considered the default mode, salience, and attention circuits to be task-free and the negative affect, positive affect, and cognitive control circuits to be task-evoked (details in ). We refined our circuit features by first excluding regions based on low temporal signal-to-noise ratio and low fit to gray matter (C). We evaluated internal consistency and excluded region pairs whose connectivity showed stronger associations with out-of-circuit region pairs than within-circuit region pairs in our healthy sample (D). From the resulting set of regions (E), we identified the subset implicated in hypothesized dysfunction and derived circuit clinical scores in reference to a healthy sample (F) (details in ).
      These regional quantifications were evaluated against quality control and psychometric criteria (Figure 2C). We excluded regions with gray matter overlap of <50%, regions with temporal signal-to-noise ratios below standard deviation criteria (Supplemental Methods S4), and regions of intrinsic connectivity with inadequate internal consistency (Figure 2D; Supplemental Methods S4). The refined set of regions (Figure 2E) was assigned standard anatomical definitions (Tables S3A and S3B).

      Derivation of Circuit Clinical Scores

      Subject-level circuit clinical scores were computed for the subset of regions that met quality and psychometric criteria and that are also implicated in our theoretical synthesis of dysfunctions in depression and anxiety (Figure 2F; Figure S4A) (
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ). In these circuit clinical scores, activation and connectivity were expressed in standard deviation units relative to the healthy reference sample and reference mean of zero (Figure 3, row 2; Supplemental Methods S5B). Global circuit clinical scores were computed for each subject by averaging component regional scores once the direction of each component was oriented as positive or negative to reflect the hypothesized direction of dysfunction (Figure 3; row 3). Components were weighted evenly given evidence for the reliability of circuit averages (
      • Ball T.M.
      • Goldstein-Piekarski A.N.
      • Gatt J.M.
      • Williams L.M.
      Quantifying person-level brain network functioning to facilitate clinical translation.
      ) and lack of evidence for differential contributions. Internal consistency for global and regional circuit clinical scores was adequate (Figure S5), and global scores were mutually independent, supporting their validity as canonical circuit constructs (Figure S6).
      Figure thumbnail gr3
      Figure 3Quantifying global and regional circuit clinical scores. An overview of the systematic process used to derive circuit clinical scores based on standardized definitions of the 6 circuits of interest and hypothesized dysfunction in these circuits in depression and anxiety. These circuits of interest were probed in both task-free and task-evoked conditions and were referred to as the default mode, salience, attention, negative affect, positive affect, and cognitive control circuits. A standardized procedure was used to identify and define constituent regions and region-to-region connectivity for each of these circuits (row 1). Activation and connectivity for each of these constituent regions was quantified at an individual subject level in clinical subjects and expressed in standardized units relative to a healthy reference sample mean such that the magnitude of resulting circuit clinical scores is interpretable relative to a healthy mean of 0 (row 2) These regional circuit clinical scores are assigned abbreviated labels (D1, D2, etc.) to facilitate subsequent computations. These regional scores may be expressed in a directional manner to reflect hypothesized dysfunctions in depression and anxiety (for example, salience circuit connectivity is inverted to indicate hypothesized hypoconnectivity, as illustrated by dashed lines in row 2). Global circuit clinical scores were computed by averaging regional circuit inputs (row 3). The formulas used to generate these global circuit clinical scores are shown with the regional input labels and with regional activation inputs (A) and connectivity inputs (C). ∗Includes dorsal medial PFC and vmPFC assessed using both conscious and nonconscious presentations of threat stimuli. ACC, anterior cingulate cortex; AG, angular gyrus; aI, anterior insula; aIPL, anterior inferior parietal lobule; Amy, amygdala; dACC, dorsal ACC; DLPFC, dorsolateral PFC; LPFC, lateral PFC; vmPFC, ventromedial PFC; msPFC, medial superior PFC; PCu, precuneus; PFC, prefrontal cortex; pgACC, pregenual ACC; sgACC, subgenual ACC.

      Content and Construct Validation of Clinical Phenotypes

      Symptom Phenotypes

      To operationalize symptom phenotypes, we followed a content validation procedure (
      • Boateng G.O.
      • Neilands T.B.
      • Frongillo E.A.
      • Melgar-Quinonez H.R.
      • Young S.L.
      Best practices for developing and validating scales for health, social, and behavioral research: A primer.
      ). Items from scales with broad symptom coverage (Supplemental Methods S6A; Table S6) were assigned to clinical phenotypes implicated in our theoretical taxonomy (
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ) and refined by principal component analysis, yielding 6 phenotypes labeled rumination, anxious avoidance, threat dysfunction, anhedonia, negative bias, and inattention-cognitive dyscontrol (Supplemental Methods S6B; Table S7). Phenotypes were quantified as the average of standardized scores for each subject (Supplemental Methods S6C).

      Behavioral Phenotypes

      An equivalent content validation procedure was used to operationalize behavioral phenotypes based on tests assessing general and emotional cognition (Supplemental Methods S7A) (
      • Mathersul D.
      • Palmer D.M.
      • Gur R.C.
      • Gur R.E.
      • Cooper N.
      • Gordon E.
      • et al.
      Explicit identification and implicit recognition of facial emotions: II. Core domains and relationships with general cognition.
      ). For general cognition, 5 constructs aligned with a prior principal component analysis conducted during test development (
      • Mathersul D.
      • Palmer D.M.
      • Gur R.C.
      • Gur R.E.
      • Cooper N.
      • Gordon E.
      • et al.
      Explicit identification and implicit recognition of facial emotions: II. Core domains and relationships with general cognition.
      )—sustained attention (n-back continuous performance test), response inhibition (Go/NoGo), information processing speed (Stroop and Trails B), executive function (maze), and working memory (digit span)—and a sixth included an interference measure unavailable during test development (Supplemental Methods S7B; Table S8). For emotional cognition, 8 constructs aligned with a prior principal component analysis (
      • Mathersul D.
      • Palmer D.M.
      • Gur R.C.
      • Gur R.E.
      • Cooper N.
      • Gordon E.
      • et al.
      Explicit identification and implicit recognition of facial emotions: II. Core domains and relationships with general cognition.
      ,
      • Williams L.M.
      • Mathersul D.
      • Palmer D.M.
      • Gur R.C.
      • Gur R.E.
      • Gordon E.
      Explicit identification and implicit recognition of facial emotions: I. Age effects in males and females across 10 decades.
      ): speed for explicit identification of sad, threat, disgust, and happy expressions and implicit priming of face recognition biased by these expressions (Supplemental Methods S7B; Table S9). Phenotypes were computed as the averaged standardized test score for each subject (Supplemental Methods S7C).

      Daily Function

      Daily function was assessed by the Satisfaction With Life Scale (
      • Diener E.
      • Emmons R.A.
      • Larsen R.J.
      • Griffin S.
      The Satisfaction With Life Scale.
      ) and Social and Occupational Functioning Assessment Scale (
      • Morosini P.L.
      • Magliano L.
      • Brambilla L.
      • Ugolini S.
      • Pioli R.
      Development, reliability and acceptability of a new version of the DSM-IV Social and Occupational Functioning Assessment Scale (SOFAS) to assess routine social functioning.
      ) (Supplemental Methods S8; Table S10).

      Circuit Clinical Scores and Phenotypes

      Hypothesized one-to-one mapping between circuit clinical scores and phenotypes (Figure 1) was tested using regression models with age, sex, and number of censored functional magnetic resonance imaging volumes included as covariates. Results were evaluated for statistical significance and for clinical meaningfulness, according to effect size and generalizability of effects within confidence limits. We used the Benjamini-Hochberg procedure to control the FDR (
      • Benjamini Y.
      • Hochberg Y.
      Controlling the false discovery rate: A practical and powerful approach to multiple testing.
      ) for each family of global and regional circuit scores (Supplemental Results S1). FDR-adjusted p values and m values for each result in Table 1 are presented in Table S11. Effect sizes were expressed as standardized β coefficient values, indicating the magnitude of change in phenotype associated with 1 SD change in the circuit predictor. Following the principle that these effect sizes can be interpreted similarly to correlations (
      • Acock A.C.
      A Gentle Introduction to Stata.
      ), < 0.2 was considered a weak effect; ≥ 0.2 and ≤ 0.5, a moderate effect; and > 0.5, a strong effect.
      Table 1Summary of Results for Relationships Between Circuit Score and Clinical Phenotypes
      Global Circuit Clinical Score Predictor/Regional Circuit PredictorDependent VariableDomainPrimary Sample APrimary Sample BGeneralizability Sample
      β (ES)95% CItpβ (ES)Within CIβ (ES)Within CI
      1. Results of Models Testing Hypothesized Predictions at the Global Circuit Level
      Results of models testing hypothesized associations of global circuit clinical scores as predictors and phenotypes as dependent variables.
      Global Circuit Clinical Score Predictor
       SalienceAnxious avoidanceSymptoms−0.260.09, 0.44−2.98.008
      Indicates results meeting familywise FDR correction of .05 in primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples (i.e., the standardized β coefficient falls within the 95% CI of the primary sample A).
      0.15Yes0.11Yes
      2. Results of Models Testing Nonhypothesized Predictions at the Global Circuit Level
      Results of models testing nonhypothesized associations of global circuit clinical scores as predictors and phenotypes as dependent variables.
      Global Circuit Clinical Score Predictor
       Default modeNegative biasSymptoms−0.25−0.40, −0.07−2.59.009
      Indicates results meeting familywise FDR correction of .05 in primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      0.14−0.05
       Default modeAnhedoniaSymptoms−0.24−0.40, −0.06−2.50.010
      Indicates results meeting familywise FDR correction of .05 in primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      0.05−0.09Yes
       SalienceInattention-cognitive dyscontrolSymptoms−0.190.01, 0.35−2.03.031
      Indicates results meeting familywise FDR correction of .05 in primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      0.15Yes0.17Yes
       SalienceNegative biasSymptoms−0.260.07, 0.45−2.78.008
      Indicates results meeting familywise FDR correction of .05 in primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      0.16Yes0.17Yes
       SalienceThreat dysregulationSymptoms−0.230.06, 0.39−2.43.011
      Indicates results meeting familywise FDR correction of .05 in primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      0.16Yes0.05
       SalienceAnhedoniaSymptoms−0.270.06, 0.47−2.87.006
      Indicates results meeting familywise FDR correction of .05 in primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      0.10Yes0.09Yes
       SalienceSatisfaction with lifeFunction−0.24−0.42, −0.06−2.58.009
      Indicates results meeting familywise FDR correction of .05 in primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      −0.09Yes−0.05
      3. Results of Models Testing Hypothesized Predictions at the Regional Circuit Level
      Results of models testing hypothesized associations of regional circuit clinical scores as predictors and phenotypes as dependent variables.
      Regional Circuit Predictor
       Default mode: L AG–amPFC connectivityRuminationSymptoms−0.21−0.38, −0.01−2.39.029−0.07Yes0.04
       Salience: L aI–L Amy ConnectivityAnxious avoidanceSymptoms−0.26−0.42, −0.11−2.96.006
      Indicates results meeting familywise FDR correction of .10 in the primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      −0.23Yes−0.17Yes
       Negative affect (sad): L aI activationNegative biasSymptoms−0.20−0.37, −0.01−2.15.027−0.17Yes−0.06Yes
       Negative affect (sad): R aI activationNegative biasSymptoms−0.21−0.38, −0.01−2.15.029−0.23Yes−0.14Yes
       Negative affect (C-Threat): R Amy activationThreat speedBehavior−0.19−0.34, −0.04−2.15.047−0.18Yes−0.04
       Positive affect (happy): R vStriatum activationHappy speedBehavior−0.20−0.34, −0.06−2.28.045−0.06Yes−0.05
       Cognitive control: ACC activationInattention-cognitive dyscontrolSymptoms−0.26−0.41, −0.06−2.69.0130.08−0.16Yes
      4. Results of Models Testing Nonhypothesized Associations at the Regional Circuit Level
      Results of models testing nonhypothesized associations of regional circuit clinical scores as predictors and phenotypes as dependent variables.
      Regional Circuit Predictor
       Default mode: R AG–amPFC connectivityNegativity biasSymptoms−0.19−0.37, −0.02−2.01.0490.040.00
       Salience: L aI–R aI connectivityNegativity biasSymptoms−0.30−0.50, −0.10−3.28.002
      Indicates results meeting familywise FDR correction of .10 in the primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      0.07−0.29Yes
       Salience: L aI–R aI connectivityThreat dysregulationSymptoms−0.27−0.51, −0.01−2.95.005
      Indicates results meeting familywise FDR correction of .10 in the primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      −0.03Yes−0.08Yes
       Salience: L aI–R aI connectivityAnhedoniaSymptoms−0.33−0.52, −0.12−3.57.001
      Indicates results meeting familywise FDR correction of .10 in the primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      0.01−0.25Yes
       Salience: L aI–L Amy connectivitySatisfaction with lifeFunction0.180.01, 0.371.98.0490.23Yes−0.12
       Salience: L aI–R aI connectivitySatisfaction with lifeFunction0.240.03, 0.432.59.015
      Indicates results meeting familywise FDR correction of .10 in the primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      0.010.21Yes
      ACC, anterior cingulate cortex; AG, angular gyrus; aI, anterior insula; amPFC, anterior medial prefrontal cortex; Amy, amygdala; CI, confidence interval for effect size represented by β value; C-Threat, conscious threat; ES, standardized effect size represented by β value, standardized β coefficient for contribution of circuit dysfunction predictors to clinical phenotype; FDR, false discovery rate; L, left; R, right; vStriatum, ventral striatum.
      a Results of models testing hypothesized associations of global circuit clinical scores as predictors and phenotypes as dependent variables.
      b Indicates results meeting familywise FDR correction of .05 in primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples (i.e., the standardized β coefficient falls within the 95% CI of the primary sample A).
      c Results of models testing nonhypothesized associations of global circuit clinical scores as predictors and phenotypes as dependent variables.
      d Indicates results meeting familywise FDR correction of .05 in primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      e Results of models testing hypothesized associations of regional circuit clinical scores as predictors and phenotypes as dependent variables.
      f Indicates results meeting familywise FDR correction of .10 in the primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      g Results of models testing nonhypothesized associations of regional circuit clinical scores as predictors and phenotypes as dependent variables.
      h Indicates results meeting familywise FDR correction of .10 in the primary sample A, and “yes” indicates instances where the relationship generalizes to primary sample B and/or generalizability samples.
      First-order regression models, testing hypothesized global circuit–phenotype associations, were run in primary sample A. In these models, t statistics were compared against the null distribution of t scores derived by 1000 random permutations (
      • Phipson B.
      • Smyth G.K.
      Permutation P-values should never be zero: Calculating exact P-values when permutations are randomly drawn.
      ), and significant effects were defined by an FDR-corrected threshold of 0.05 (Table 1.1; Supplemental Results S1A). Second-order regression models tested hypothesized regional circuit-phenotype associations, and significant effects were defined by an FDR-corrected threshold of 0.1 (Table 1.2; Supplemental Results S1B). Relationships surviving FDR correction in primary sample A were considered to have generalized if β effect sizes of sample B and/or generalizability samples fell within the 95% bootstrapped confidence interval for sample A.

      Circuit Dysfunctions and Treatment Outcomes

      Using logistic regression models, we first tested whether global circuit clinical scores are general predictors of response, over and above pretreatment symptom severity. Next, we used interaction terms to evaluate global circuit clinical scores as differential predictors of response as a function of type of treatment: selective serotonin reuptake inhibitors (SSRIs), sertraline and escitalopram, or selective serotonin-norepinephrine reuptake inhibitor (SNRI), extended-release venlafaxine, for antidepressants and active behavioral intervention (I-CARE) or usual care (U-CARE) for behavioral intervention. Parallel models were undertaken in hierarchical steps, evaluated by χ2 tests for each set of global and regional circuit predictors. Significant effects were defined by an FDR-corrected threshold of 0.1, and tendencies at the uncorrected threshold of 0.05 were considered in supplemental analyses to inform future investigations. Effect sizes for regional predictors that contributed to treatment outcomes were reported.

      Results

      Circuit Clinical Scores and Phenotypes

      An overall observation was that clinical phenotypes were associated with global circuit clinical scores in task-free conditions and with regional scores under task conditions (Table 1; Figure 4).
      Figure thumbnail gr4
      Figure 4Visualization of the associations between global circuit clinical scores and phenotypes. Observed relationships between global circuit clinical scores (bottom half; below the dotted line) and theoretically motivated symptom phenotypes (top half; above the dotted line). Significant relationships in the primary sample A are illustrated by thicker, darker lines, with the color of the ribbon representing the specific circuit involved and the thickness representing the magnitude of effect size (standardized regression coefficient values) and consistency of effects across samples. The color of the outermost ring of the circle’s top half represents the corresponding hypothesized one-to-one mapping of circuit and phenotype (e.g., default mode network [blue] was hypothesized to map to the rumination phenotype [blue], salience circuit [green] was hypothesized to map to the anxious avoidance phenotype [green]). aSignificant relationships are defined as those that survive the false discovery rate threshold using the Benjamini-Hochberg correction at p = .05. C Threat, conscious threat.

      Default Mode Circuit

      Global default mode scores reflective of hyperconnectivity were not associated with rumination as operationalized by our phenotype. However, global default mode hypoconnectivity significantly predicted more severe negative bias and anhedonia at the FDR-adjusted threshold, with low-moderate effect size and consistent across the generalizability sample (Table 1.1; Figure 4).
      Lower default mode connectivity specific to the left angular gyrus and anterior medial prefrontal cortex was associated with more severe rumination (Table 1.2; Figure 5). Although this association did not meet the FDR-adjusted threshold, it replicated with low-moderate effect size across primary samples A and B (Table 1.3).
      Figure thumbnail gr5
      Figure 5Visualization of the associations between regional circuit clinical scores and phenotypes. The observed relationships between regional circuit clinical scores (bottom half of each circle; below the dotted line) and symptom and/or behavioral phenotypes (top half of each circle; above the dotted line), guided by our theoretical synthesis. (A) Default mode circuit. (B) Salience circuit. (C) Attention circuit. (D) Negative affect circuit elicited by sad. (E) Negative affect circuit elicited by threat. (F) Positive affect circuit. (G) Cognitive control circuit. Relationships in primary sample A (i.e., uncorrected p < .05) are illustrated by thicker, darker lines, with the color representing the specific circuit involved and the thickness representing the magnitude of effect size (standardized regression coefficient values) and consistency of effects across samples. aRelationships observed at an uncorrected p < .05. ACC, anterior cingulate cortex; AG, angular gyrus; aI, anterior insula; aIPL, anterior inferior parietal lobule; amPFC, anterior medial PFC; Amy, amygdala; C-Threat, conscious threat; dACC, dorsal ACC; DLPFC, dorsolateral PFC; L, left; LPFC, lateral PFC; msPFC, medial superior PFC; pgACC, pregenual ACC; PCC, posterior cingulate cortex; PCu, precuneus; PFC, prefrontal cortex; R, right; vmPFC, ventromedial PFC; vStriatum, ventral striatum.

      Salience Circuit

      Salience circuit hypoconnectivity significantly predicted more severe symptoms across phenotypes, including anxious avoidance (the hypothesized one-to-one association), negative bias, threat dysregulation, anhedonia, and inattention-cognitive dyscontrol at the FDR-adjusted threshold, consistent across samples (Table 1.1; Figure 4). The hypothesized association of salience circuit hypoconnectivity and anxious avoidance was of low-moderate effect size that was consistent across all samples (Table 1.1).
      Greater salience circuit clinical scores were also significantly associated with worse satisfaction with life at the FDR-adjusted threshold, with low-moderate effect size and replicated in the primary sample B (Table 1.3; Supplemental Results S1C).
      When considering regional connections, the association between hypoconnectivity and anxious avoidance was specific to the left anterior insula and left amygdala (Table 1.2; Figure 5). Left-right insula hypoconnectivity was associated with symptoms of negative bias, threat dysregulation, and anhedonia as well as worse satisfaction with life at the FDR-adjusted threshold (Table 1.3).

      Attention Circuit

      For the attention circuit, clinical phenotypes were not associated with global circuit clinical scores or regional connectivity.

      Negative Affect Circuit

      For the negative affect circuit evoked by sad stimuli, hypoactivation of the anterior insula, bilaterally, predicted more severe symptoms of negative bias (Table 1.2; Figure 5). These effects did not meet the adjusted α threshold but did meet criteria for a consistent effect size of low-moderate magnitude across primary A, primary B, and generalizability samples. Conversely, there was a tendency for threat-elicited right amygdala hyperactivation to predict accelerated responses to identifying these stimuli at the unadjusted α threshold with a weak effect size, consistent across primary samples A and B (Table 1.2; Figure 5).

      Positive Affect Circuit

      The positive affect circuit probed by happy stimuli global circuit clinical scores was not associated with clinical phenotypes. Lower ventral striatal activation showed a tendency for association with slower responses to identifying happy faces at the uncorrected α threshold with low-moderate effect size, generalizable across 2 samples (Table 1.2; Figure 5).

      Cognitive Control Circuit

      Lower activation of the dorsal anterior cingulate cortex (dACC) showed a tendency toward association with more severe symptoms of inattention-cognitive dyscontrol at the unadjusted α level with low-moderate effect size consistent across primary sample A and generalizability samples (Table 1.2; Figure 5).

      Circuit Clinical Scores and Treatment Outcomes

      For pharmacotherapy, we observed regional circuit predictors that were differentially related to SSRI versus SNRI outcomes. Pretreatment default mode connectivity significantly differentiated response outcomes for SSRIs versus SNRIs (p = .002) (Table S14). SNRI nonresponders were distinguished by posterior cingulate cortex–angular gyrus hyperconnectivity, and SNRI responders were distinguished by relative hypoconnectivity of these regions, whereas there was a tendency toward an opposing profile of hypoconnectivity in SSRI nonresponders and hyperconnectivity in SSRI responders (interaction effect size reflecting the standard deviations increase in the log odds of response versus nonresponse for SSRI versus SNRI for 1 SD increase in the predictor = −2.12) (Table S17; Figure S8C).
      Pretreatment negative affect circuit scores differentiated responders to SSRIs versus SNRIs (Table S14) when elicited by both conscious and nonconscious threat. SSRI responders showed pretreatment hyperconnectivity of the left amygdala and dACC and hypoconnectivity of the right amygdala and dACC for conscious threat. SNRI responders showed hypoactivation of the right amygdala and comparative hyperconnectivity of the left amygdala and subgenual ACC for nonconscious threat (Table S17; Figure S8C).
      For the behavioral intervention, pretreatment attention regional connectivity was a differential predictor of subsequent response to I-CARE versus U-CARE (Table S16). Compared with responders in U-CARE, I-CARE responders showed hypoconnectivity between the left anterior inferior parietal lobule and left prefrontal cortex within the attention circuit (Table S17; Figure S8D).
      Affect circuit function was also a differential predictor of behavioral intervention outcomes (Table S16). I-CARE responders were distinguished by lower ventromedial prefrontal cortex activation compared with nonresponders, whereas the reverse was observed for U-CARE (Table S17; Figure S10D). Within the negative affect circuit elicited by threat, relatively lower left amygdala activity distinguished response to I-CARE but nonresponse to U-CARE (Tables S16 and S17; Figure S10D).

      Discussion

      We developed a reproducible image processing system for quantifying subject-level neural circuit metrics and tested these metrics for their clinical utility in showing relationships with clinical symptoms, behavior and social-occupational function, and treatment response. Our approach offers one step toward making precision advances in the mental health field, specifically for depressive and anxiety disorders that contribute disproportionately to illness burden and suicide.
      Our image processing system integrates 4 key features: standardization, quality-controlled neuroanatomical definitions of functional brain circuits spanning task-free and task-evoked contexts, reproducible procedures for quantifying the activation of and connectivity between regions within each circuit with demonstrated consistency, and algorithms for computing metrics that quantify global and regional circuit clinical scores at the individual subject level relative to a healthy reference sample. We tested this system in 3 samples of adults with a broad range of depression and anxiety symptoms and systematically examined brain circuit–phenotype relationships informed by our theoretical framework (
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ). We found limited evidence for the hypothesized one-to-one mappings between circuit clinical scores and specific phenotypes that reflect common assumptions in the field about neural-phenotype relationships. However, we did identify associations that suggest specific connectivity profiles—particularly within salience and default mode circuits—may give rise to multiple phenotype expressions and that additional circuit activation and connectivity profiles are implicated in treatment response.
      Within the task-free circuits, salience circuit clinical scores, especially hypoconnectivity between the anterior insula and the amygdala, were significantly predictive of anxious avoidance symptoms at the adjusted α level and generalized across samples, consistent with hypotheses (
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ). Salience circuit hypoconnectivity within the insula also contributed significantly to symptoms of anhedonia, negative bias, and threat dysregulation and generalized across at least one additional sample. These findings suggest a role for insula disconnection in features of negative bias and blunted positive emotion that impact daily function, consistent with findings from metabolic insula imaging (
      • Dunlop B.W.
      • Kelley M.E.
      • McGrath C.L.
      • Craighead W.E.
      • Mayberg H.S.
      Preliminary findings supporting insula metabolic activity as a predictor of outcome to psychotherapy and medication treatments for depression.
      ). Global salience hypoconnectivity showed an additional significant association with inattention-cognitive dyscontrol symptoms that generalized across samples. Given prior evidence of functional interactions between salience and attention circuits (
      • 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.
      ) that may fluctuate with interoceptive and external events, future investigations that expand our current within-circuit focus to examine between-circuit connectivity are warranted.
      Although default mode hyperconnectivity was not predictive of rumination as hypothesized, global hypoconnectivity was significantly associated with negative bias and anhedonia at the adjusted α level. Such hypoconnectivity is consistent with emerging evidence for a default mode hypoconnectivity subtype of depression (
      • Yan C.G.
      • Chen X.
      • Li L.
      • Castellanos F.X.
      • Bai T.J.
      • Bo Q.J.
      • et al.
      Reduced default mode network functional connectivity in patients with recurrent major depressive disorder.
      ,
      • Tozzi L.
      • Zhang X.
      • Chesnut M.
      • Holt-Gosselin B.
      • Ramirez C.A.
      • Williams L.M.
      Reduced functional connectivity of default mode network subsystems in depression: Meta-analytic evidence and relationship with trait rumination.
      ) and the exploratory default mode biotype proposed in our theoretical framework (
      • Williams L.M.
      Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation.
      ,
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ), informed by meta-analysis (
      • Zhu X.
      • Wang X.
      • Xiao J.
      • Liao J.
      • Zhong M.
      • Wang W.
      • et al.
      Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients.
      ). We also note that our phenotype of rumination indexed ruminative worry in particular; future investigations with broader measures of ruminative response styles are required.
      Regarding pharmacological treatment, we found that pretreatment hyperconnectivity of the posterior cingulate and angular gyrus within the default mode circuit distinguished nonresponders from responders to the SNRI in particular. This observation of hyperconnectivity accords with prior findings for duloxetine, which also inhibits both serotonin and norepinephrine uptake and has been found to regularize pretreatment default mode hyperconnectivity (
      • Posner J.
      • Hellerstein D.J.
      • Gat I.
      • Mechling A.
      • Klahr K.
      • Wang Z.
      • et al.
      Antidepressants normalize the default mode network in patients with dysthymia.
      ). It also expands on prior posterior cingulate seed-based and whole-brain connectivity analyses of this dataset that implicate relatively intact default mode connectivity as a general predictor of antidepressant remission (
      • Korgaonkar M.S.
      • Goldstein-Piekarski A.N.
      • Fornito A.
      • Williams L.M.
      Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder.
      ,
      • Goldstein-Piekarski A.N.
      • Staveland B.R.
      • Ball T.M.
      • Yesavage J.
      • Korgaonkar M.S.
      • Williams L.M.
      Intrinsic functional connectivity predicts remission on antidepressants: A randomized controlled trial to identify clinically applicable imaging biomarkers.
      ). Further, SNRI responders were characterized by pretreatment amygdala hypoactivation within the negative affect circuit, consistent with prior group-averaged findings in this dataset (
      • Williams L.M.
      • Korgaonkar M.S.
      • Song Y.C.
      • Paton R.
      • Eagles S.
      • Goldstein-Piekarski A.
      • et al.
      Amygdala reactivity to emotional faces in the prediction of general and medication-specific responses to antidepressant treatment in the randomized iSPOT-D Trial.
      ). The new finding that SNRI responders are distinguished by amygdala–subgenual ACC hypoconnectivity for nonconscious threat and SSRI responders are distinguished by an opposing profile of amygdala-dACC hyperconnectivity for conscious threat suggests that amygdala-ACC connectivity might reflect different functional states that are present before treatment and that respond to the different ways that the drug types act at the receptor level.
      For behavioral intervention, pretreatment global hypoconnectivity within the attention circuit was a significant differential predictor of response to the active I-CARE condition, consistent with independent reports that such hypoconnectivity could inform selection for cognitive behavioral therapy (
      • Yang Z.
      • Gu S.
      • Honnorat N.
      • Linn K.A.
      • Shinohara R.T.
      • Aselcioglu I.
      • et al.
      Network changes associated with transdiagnostic depressive symptom improvement following cognitive behavioral therapy in MDD and PTSD.
      ). Differential response to behavioral intervention was also distinguished by regional activation elicited by positive and negative affective stimuli. Although these treatment outcome relationships need to be confirmed in independent samples, they offer a starting point for personalized biomarker trials that require a standardized procedure for quantifying circuit dysfunction at the subject level.
      By focusing first on a discrete within-circuit, one-to-one mapping approach, our goal was to develop and evaluate a prototype for subject-level functional magnetic resonance imaging quantification suited to clinical applications. Taken together, our findings reveal minimal support for a model in which there is a discrete one-to-one mapping between the 6 circuits of interest and specific symptoms and behaviors implicated in dysfunction of these circuits, at least within the current samples and as based on our prior theoretical synthesis (
      • Williams L.M.
      Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation.
      ,
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ). Yet, the findings do demonstrate the reproducibility of the method and reveal significant and consistent effects for a specific subset of circuit-phenotype associations across samples and for circuit markers of treatment outcomes. Because our circuit clinical scores were validated in samples recruited to be representative of the community, with a range of symptom severity and comorbidities, the method arguably is applicable to the range of patients seen in the clinic (
      • Rush A.J.
      • Ibrahim H.M.
      A clinician’s perspective on biomarkers.
      ).
      Both the null findings and the nonhypothesized associations revealed by analyses prompt the consideration of limitations, potential alternative explanations, and new directions for future investigation. A crucial consideration in determining circuit-phenotype outputs is the selection of inputs and samples for analysis. Although our recruitment approach achieved representative samples, the inclusion of mildly symptomatic subjects could have limited the opportunity to pinpoint circuit dysfunctions that manifest primarily in severely symptomatic phenotypes that are the focus of case-control designs. Future investigations, currently underway, will focus on a strategy of enriching samples based on clinically relevant standard deviation thresholds for both circuit and clinical measures. Relatedly, although our samples spanned multiple diagnostic comorbidities, the most common diagnosis was generalized anxiety disorder, and major depressive disorder was 3 times more prevalent in the generalizability than in the primary sample. The preponderance of anxiety disorders in our sample may have contributed to the robust results for insula connectivity, in concert with the amygdala. This speculation accords with evidence that the insula, and the salience network it defines, serves a domain-general function that when disrupted can produce the diverse visceral, affective, and cognitive features of anxiety (
      • Paulus M.P.
      • Stein M.B.
      An insular view of anxiety.
      ). Future investigations might determine if these connections are disrupted during tasks that engage threat and other aspects of affective reactivity.
      Our clinical inputs were items from well-established symptom scales for which the focus is usually on total scores. Thus, one research product developed from this study is the classification of individual items, across these scales, according to clinical phenotypes suggested by our theoretical circuit taxonomy (
      • Williams L.M.
      Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation.
      ,
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ). This classification was validated in the current sample, but we do acknowledge that limited item coverage for some phenotypes may have limited the capacity to identify robust associations with all circuits of interest. For example, the established scales we used lack coverage of ruminative response styles, threat dysregulation, inattention, and cognitive impairments, implicated by respective dysfunctions in the default mode, negative affect, attention, and cognitive control circuits. In ongoing analyses, we are pursuing symptom-specific scales to further understand how symptom profiles are identified in the brain.
      At the circuit level, it would likewise be important to expand our use of established tasks to include tasks designed to probe more specific circuit constructs, such as functional magnetic resonance imaging reward tasks. Future investigations are also warranted to expand our initial focus on a specific set of regions informed by prior knowledge (
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ) to additional regions informed by ongoing evidence. As regional inputs are added, the weighting of these inputs to the computation of global circuit clinical scores may also need refinement, and we designed our circuit system to be flexible with the expectation of such refinement. To explore circuit-phenotype associations more fully, it will be essential to extend our within-circuit approach to the testing of putative biotypes that include subnodes, between-circuit effects, and interactions within and between circuits (
      • Williams L.M.
      Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: A theoretical review of the evidence and future directions for clinical translation.
      ,
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ). For example, parsing of subnodes of the default mode circuit and their connectivity with negative affect circuits may allow for a better understanding of associations with ruminations, self-reflection, and negative attributional biases (
      • Williams L.M.
      Precision psychiatry: A neural circuit taxonomy for depression and anxiety.
      ,
      • Zhou H.X.
      • Chen X.
      • Shen Y.Q.
      • Li L.
      • Chen N.X.
      • Zhu Z.C.
      • et al.
      Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression.
      ), and accounting for interactions between default mode, attention, and cognitive control circuits may provide a more complete characterization of a cognitive dyscontrol biotype (
      • Williams L.M.
      • Coman J.T.
      • Stetz P.C.
      • Walker N.C.
      • Kozel F.A.
      • George M.S.
      • et al.
      Identifying response and predictive biomarkers for transcranial magnetic stimulation outcomes: Protocol and rationale for a mechanistic study of functional neuroimaging and behavioral biomarkers in veterans with pharmacoresistant depression.
      ). Methodologically, it would be valuable to pursue direct tests of the impact of scanner, site, and functional localizers for more precise subject-level quantification (
      • Salehi M.
      • Greene A.S.
      • Karbasi A.
      • Shen X.
      • Scheinost D.
      • Constable R.T.
      There is no single functional atlas even for a single individual: Functional parcel definitions change with task.
      ) and to incorporate finer-grained age norms for more precise interpretation.
      Our findings for treatment accord with the view that mechanistic circuit markers for clinical phenotypes may not be the same as the circuit markers that predict treatment outcomes, help select among multiple treatment options, and/or change with treatment (
      • Rush A.J.
      • Ibrahim H.M.
      A clinician’s perspective on biomarkers.
      ). Precision medicine and prospective and repeat testing designs are needed to systematically help sort circuit dysfunctions according to these different clinical functions. Such designs will also allow for more precise characterization of which aspects of circuit dysfunction are more traitlike versus statelike and thus which are more amenable to change with treatment.

      Conclusions

      The functional image system developed and tested in this study offers one means by which our field can generate standardized subject-level imaging metrics across studies, sites, and samples. These metrics can serve as inputs into further subgroup classifications, computational models, and biomarker trials to refine our understanding of the clinical function of these metrics. Clinically, such metrics offer a step toward the use of imaging tools to aid in the personalized clinical management of mood and anxiety.

      Acknowledgments and Disclosures

      This work was supported by the National Institutes of Health (Grant No. R01MH101496 [to LMW; NCT02220309], Grant No. 5P50DA042012 [to LMW], Grant No. UH2HL132368 [to JM and LMW; NCT02246413], Grant No. F32MH108299 [to ANG-P], and Grant Nos. T32MH019938 and K23MH113708 [to TMB]). Psychopharmacology data from iSPOT-D (NCT00693849) was sponsored by Brain Resource Ltd.
      The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit the manuscript for publication.
      LMW designed the study and imaging and conceptualized the image processing system and theoretically motivated analytic approach. ANG-P, TMB, ZS, and LMW implemented the theoretically motivated analytic approach. ANG-P, BRS, and PS with LMW implemented the image processing system. KAG and LMW implemented the phenotype battery and construct analyses for the primary sample. KAG and BH-G collected data for the primary and generalizability samples. LMW designed and oversaw the antidepressant treatment study design, JM and LMW designed and oversaw the behavioral intervention treatment study design, and BRS and ASK implemented the treatment analyses and illustration. ANG-P, TMB, ZS, BRS, KAG, SLF, and LMW analyzed data. ANG-P, TMB, ZS, ASK, and LMW wrote the manuscript. KAG, SLF, BRS, BH-G, PS, and JM critically reviewed the manuscript.
      We thank Sarah Chang, B.Sc., for contributions to data acquisition and generating of sample tables and Carlos Correa, B.Comp.Sc., for contributions to software development of the image processing system. We thank Jon Kilner, M.S., M.A. (Pittsburgh, PA), for editorial support.
      The datasets for the primary sample analyzed during the current study are made available through the National Institute of Mental Health Data Archive, https://nda.nih.gov/user/dashboard/collections.html, collection number C2100. The datasets for the generalizability sample analyzed during the current study will be made available from the corresponding author on reasonable request. Patients’ whole-brain correlation matrices and our full analysis codes for the primary and generalizability samples are available from the corresponding author on reasonable request. The datasets for the treatments sample analyzed during the current study will be made available from the corresponding author on reasonable request after approval of a proposal. For the antidepressant data, reasonable requests will also require the permission of the study sponsor, Brain Resource Ltd. For the behavioral intervention data, study measures will be made available through the National Institutes of Health Science of Behavior Change repository, https://scienceofbehaviorchange.org/measures/.
      LMW declares U.S. patent applications 10/034,645 and 15/820,338: Systems and methods for detecting complex networks in MRI image data. SLF declares consulting fees received from Youper, Inc., within the last 5 years. All other authors report no biomedical financial interests or potential conflicts of interest.

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      Linked Article

      • From Magnetic Resonance Imaging to the Clinic: Using Neuroimaging to Characterize Psychiatric Phenotypes
        Biological PsychiatryVol. 91Issue 6
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          Psychiatric disorders including major depressive disorder and anxiety-related disorders are highly heterogeneous and comorbid. Frontline treatments for these disorders are often only modestly effective in ameliorating symptoms—underscoring the need to identify clinically useful biomarkers that can guide interventions. Advancements in neuroimaging over the past 2 decades have made significant strides toward identifying functional and structural brain signatures associated with psychiatric symptoms.
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