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生物精神病学。作者手稿；可于 PMC 获取，2023 年 3 月 15 日。
以最终编辑形式发布为：
在线发表于 2021 年 7 月 11 日。doi: 10.1016/j.biopsych.2021.06.024
Mapping neural circuit biotypes to symptoms and behavioral dimensions of depression and anxiety
将神经电路生物类型与抑郁和焦虑的症状及行为维度进行映射
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Associated Data 相关数据
Abstract 摘要
Background: 背景：
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.
尽管在表征调节抑郁和焦虑中受损的情感和认知功能的人类神经电路方面取得了巨大的进展，但我们仍然缺乏一种基于电路的抑郁和焦虑分类法，无法捕捉跨诊断的异质性并为临床决策提供信息。
Methods: 方法：
We developed and tested a novel system for quantifying six 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.
我们开发并测试了一种新系统，能够在个体患者层面上可重复地量化六个脑回路。我们实施了相对于健康参考样本的标准化回路定义，并设计了算法以生成整体回路及其组成区域的临床评分。
Results: 结果：
In new data from primary and generalizability samples of depression and anxiety (n=250), we demonstrate 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 distinguish response to antidepressant and behavioral intervention treatments in an independent sample (n=205).
在来自抑郁和焦虑的主要和可推广样本的新数据中（n=250），我们展示了任务无关的显著性和默认模式电路中的整体断连与焦虑回避、快乐丧失、威胁失调和负面情绪偏见等症状之间的关系——这些是超越诊断的核心特征——以及较差的日常功能。任务诱发的认知控制和情感电路中的区域功能障碍可能与认知和情感一致的情绪功能症状相关。电路功能障碍评分还区分了对抗抑郁药和行为干预治疗的反应，在一个独立样本中（n=205）。
Conclusions: 结论：
Our findings articulate circuit dimensions that relate to trans-diagnostic 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.
我们的研究结果阐明了与情绪和焦虑障碍相关的跨诊断症状的电路维度。我们的新系统为在研究小组、试验和诊所中实施标准化电路评估提供了基础，以推动精神病学中更精确的分类和治疗目标。
关键词：功能性脑电路成像，生物类型，临床转化，精准心理健康，抑郁，焦虑
INTRODUCTION 引言
Advances in non-invasive 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.
非侵入性功能性脑成像的进展表明，不同类型的脑回路功能障碍可能是抑郁症和焦虑症临床表现的基础。然而，我们缺乏一种方法来以个体水平量化临床脑回路指标，以促进可行的决策。为了朝着这一目标取得进展，我们利用多种抑郁症和焦虑症样本开发并测试了一种适合临床应用的个体水平影像系统。
Our approach was informed by a prior theoretical synthesis of functional brain imaging studies that implicate dysfunction across six large-scale circuits in the clinical features of depression and anxiety and in their treatment (1, 2) (Figure 1). These prior studies have 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 hyper-connectivity of the default mode circuit implicates rumination, while hypo-connectivity may reflect different symptoms and poorer antidepressant outcomes (1, 2). Hypo-connectivity of insula and amygdala within the salience circuit is observed across mood and anxiety disorders, particularly implicating social anxiety, and anxious avoidance (1, 2). When evoked by tasks using threat stimuli, heightened amygdala activation and reduced amygdala-prefrontal connectivity has been observed across disorders, suggesting a common underlying threat-related circuit disruption (1, 2). Within the positive affective circuit, striatal hypo-activation is implicated in reward-related behaviors characteristic of anhedonia (1, 2). Frontoparietal attention circuit hypo-connectivity implicates poor attention symptoms in both depression and anxiety. Under task conditions, frontal hypo-activation within the cognitive control circuit is indicative of more task-specific cognitive symptoms (1, 2).
我们的方法受到先前功能性脑成像研究的理论综合的启发，这些研究涉及抑郁和焦虑的临床特征及其治疗中六个大规模回路的功能障碍（1, 2）（图 1）。这些先前的研究通常集中于病例对照设计，以理解群体平均功能障碍，这可能会混淆多个潜在的个体功能障碍特征。在先前的综合中，我们试图解析可能导致特定临床特征和治疗结果的回路功能障碍类型。在无任务状态下，默认模式回路的内在超连接性与反刍思维相关，而低连接性可能反映不同的症状和较差的抗抑郁药物效果（1, 2）。在显著性回路中，岛叶和杏仁核的低连接性在情绪和焦虑障碍中普遍存在，特别与社交焦虑和焦虑回避相关（1, 2）。 当使用威胁刺激的任务被引发时，观察到杏仁核的激活增强和杏仁核-前额叶连接减少，这在各种障碍中都有出现，提示存在一个共同的威胁相关电路破坏（1, 2）。在积极情感电路中，纹状体的低激活与特征性无快感的奖励相关行为有关（1, 2）。前额顶注意电路的低连接性与抑郁和焦虑中的注意力差症状相关。在任务条件下，认知控制电路中的前额低激活表明更多特定于任务的认知症状（1, 2）。
假设电路得分与通过症状和行为评估的表型之间的方向性关系。
aFor full details of Circuit Scores and Circuit Clinical Score, see Figures 2 and and3,3, Tables S4 and S5, and Methods S4; bFor full details of composite measures of symptom phenotypes, see Tables S6 and S7 and Methods S6; cFor full details of composite measures of behavior phenotypes, see Tables S8 and S9, and Methods S7. For details of daily function measures included in exploratory analyses, not shown in Figure 1, see Table S10 and Methods S8; ddACC was used for Negative Affect Conscious Threat and the sgACC was used for Negative Affect non-conscious threat.
a 有关电路评分和电路临床评分的完整细节，请参见图 2 和图 3、表 S4 和 S5，以及方法 S4；b 有关症状表型的综合测量的完整细节，请参见表 S6 和 S7 以及方法 S6；c 有关行为表型的综合测量的完整细节，请参见表 S8 和 S9，以及方法 S7。有关探索性分析中包含的日常功能测量的详细信息（未在图 1 中显示），请参见表 S10 和方法 S8；ddACC 用于负面情感意识威胁，sgACC 用于负面情感非意识威胁。
Abbreviations: FC = Functional Connectivity; RT = Reaction Time.
缩写：FC = 功能连接；RT = 反应时间。
Regional Abbreviations: ACC = Anterior Cingulate Cortex; AG = Angular Gyrus; aI = anterior Insula; aIPL = anterior Inferior Parietal Lobule; amPFC= anterior medial PreFrontal Cortex; dACC = dorsal Anterior Cingulate Cortex; DLPFC = Dorsolateral Prefrontal Cortex; L = Left; LPFC = Lateral Prefrontal Cortex; vmPFC = venromedial Prefrontal Cortex; msPFC = medial superior PreFrontal Cotex; pACC = pregenual ACC; PCC = Posterior Cingulate Cortex; PPI = PsychoPhysiological Interaction; R = Right.
区域缩写：ACC = 前扣带皮层；AG = 角回；aI = 前岛叶；aIPL = 前下顶叶小叶；amPFC = 前内侧前额叶皮层；dACC = 背侧前扣带皮层；DLPFC = 背外侧前额叶皮层；L = 左；LPFC = 外侧前额叶皮层；vmPFC = 腹内侧前额叶皮层；msPFC = 内侧上前额叶皮层；pACC = 前缘 ACC；PCC = 后扣带皮层；PPI = 心理生理交互作用；R = 右。
Informed by our theoretical synthesis (2), 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 six 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 (3). To further test the clinical relevance of our system, we evaluated whether circuit clinical scores distinguish intervention response outcomes.
根据我们的理论综合（2），我们测试了特定类型的电路临床功能与特定临床表型之间存在一对一关联的工作假设（图 1）。为了测试这些假设，我们为六个感兴趣的电路制定了激活和连接性的标准化定义，并为每个受试者的每个电路开发了一种新的量化电路临床评分的方法，该评分以健康参考样本的标准差单位表示。我们利用了多个样本，包括健康受试者、未治疗的临床受试者以及在药物和行为干预试验中测试的受试者，每个受试者都评估了共同的电路和临床数据元素。这些多个样本使我们能够解决开发受试者级成像系统固有的挑战，包括缺乏可以汇总并用于测试普遍性的强大样本。电路临床评分在未治疗样本中被测试与症状和行为表型的假设关联。 与日常功能相关的电路关联也进行了探讨，这与抑郁和焦虑的致残效应有关（3）。为了进一步测试我们系统的临床相关性，我们评估了电路临床评分是否能够区分干预反应结果。
METHODS 方法
Samples 样本
The study comprised four samples assessed with common measures (Tables S1, S2; Methods S2):
该研究包含四个样本，使用常见测量进行评估（表 S1，S2；方法 S2）：
- Healthy reference sample of 95 adults recruited at the same two sites as clinical subjects.
健康参考样本由 95 名成年人组成，这些成年人在与临床受试者相同的两个地点招募。 - 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 alpha = 0.05, and control for over-estimated effect sizes (4).
主要临床样本为 160 名有抑郁和焦虑症状的成年人，随机分层为子样本 A（70%；n=112）和 B（30%；n=48），旨在检测小到中等规模的电路-表型关联，显著性水平为α = 0.05，并控制过高估计的效应大小（4）。 - Generalizability sample of 90 adults with clinical characteristics like the primary sample, yet independently recruited.
一般化样本由 90 名成年人组成，其临床特征与主要样本相似，但独立招募。 - Treatment sample of 205 adults, enrolled in randomized controlled trials of antidepressant pharmacotherapy for major depressive disorder (n=137) (5, 6) or behavioral intervention for clinically significant depressive symptoms and obesity (n=68) (7), in which treatment response was defined as ≥50% reduction in symptom severity.
治疗样本包括 205 名成年人，参与了针对重度抑郁症的抗抑郁药物治疗的随机对照试验（n=137）（5, 6）或针对临床显著抑郁症状和肥胖的行为干预（n=68）（7），其中治疗反应被定义为症状严重程度减少≥50%。
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.
受试者提供了书面知情同意。程序已获得斯坦福大学机构审查委员会（IRB 27937 和 41837）或西悉尼地区健康服务人类研究伦理委员会的批准。
Derivation of Circuits 电路的推导
A consensus definition was generated for circuits of interest using the meta-analytic database Neurosynth.org (8) with search terms “Default Mode, Salience, Attention, Threat, Reward, and Cognitive Control”, and uniformity maps with a false discovery rate (FDR) threshold of .01 (Figure 2A; Methods S3, S4a).
使用元分析数据库 Neurosynth.org（8）生成了感兴趣电路的共识定义，搜索词为“默认模式、显著性、注意力、威胁、奖励和认知控制”，并使用假发现率（FDR）阈值为 0.01 的均匀性图（图 2A；方法 S3，S4a）。
量化感兴趣的电路。
First, we identified six 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), 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 Table S3). We refined our circuit features by first excluding regions based on low tSNR 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 (E). From the resulting set of regions (E) we identified the subset implicated in hypothesized dysfunction and derived circuit clinical scores references to a healthy sample (F; details in Table S5).
首先，我们确定了与抑郁和焦虑相关的六个目标电路，并使用元分析数据库和搜索工具 Neurosynth.org 识别了这些电路中的潜在区域。从上到下，这些电路分别是默认模式（蓝色）、显著性（绿色）、注意力（黄色）、负性情感（橙色）、正性情感（紫色）和认知控制（红色）（A）。为了识别感兴趣的区域（B），我们认为默认模式、显著性和注意力电路是无任务的，而负性情感、正性情感和认知控制电路是任务诱发的（详细信息见表 S3）。我们通过首先排除基于低 tSNR 和低灰质拟合的区域来细化我们的电路特征（C）。我们评估了内部一致性，并排除了在我们的健康样本中，连接性与电路外区域对的关联强于电路内区域对的区域对（E）。从结果区域集（E）中，我们识别出与假设功能障碍相关的子集，并推导出与健康样本的电路临床评分参考（F；详细信息见表 S5）。
Resulting region pairs were quantified for intrinsic functional connectivity after regressing out task effects (9). 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 circuita, happy versus neutral faces for positive affect circuit, and NoGo versus Go trials for cognitive control circuit (Methods S4c) (Figure 2B).
结果区域对在回归任务效应后被量化为内在功能连接（9）。任务诱发的激活被量化为感兴趣区域的激活，并使用这些区域之间的心理生理交互来量化功能连接，针对负面情感回路中的悲伤与中性面孔、威胁与中性面孔的对比，积极情感回路中的快乐与中性面孔的对比，以及认知控制回路中的不行动与行动试验（方法 S4c）（图 2B）。
These regional quantifications were evaluated against quality control and psychometric criteria (Figure 2C). We excluded regions with gray matter overlap of <50%, temporal signal-to-noise ratios (tSNRs) below standard deviation criteria (Methods S4) and regions of intrinsic connectivity with inadequate internal consistency (Figure 2D; Methods S4). The refined set of regions (Figure 2E) were assigned standard anatomical definitions (Tables S3A, B).
这些区域量化结果经过质量控制和心理测量标准的评估（图 2C）。我们排除了灰质重叠率低于 50%的区域、时间信噪比（tSNR）低于标准差标准的区域（方法 S4），以及内部一致性不足的内在连接区域（图 2D；方法 S4）。经过精炼的区域集（图 2E）被赋予了标准解剖定义（表 S3A，B）。
Derivation of Circuit Clinical Scores
电路临床评分的推导
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 (2) (Figure 2F; S4A). 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; Methods S5B). Global circuit clinical scores were computed for each subject by averaging component regional scores once the direction of functional connectivity component scores were oriented reflect the hypothesized direction of dysfunction (Figure 3; row 3). Components were weighted evenly given evidence for the reliability of circuit averages (10) 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).
针对满足质量和心理测量标准的区域子集，以及在我们关于抑郁和焦虑功能障碍的理论综合中涉及的区域，计算了受试者级别的电路临床评分（2）（图 2F；S4A）。在这些电路临床评分中，激活和连接性以相对于健康参考样本和参考均值为零的标准差单位表示（图 3，第 2 行；方法 S5B）。通过对功能连接性成分评分的方向进行调整，使其反映假设的功能障碍方向，计算每个受试者的全球电路临床评分，方法是对成分区域评分进行平均（图 3；第 3 行）。鉴于电路平均值的可靠性证据（10）和缺乏差异贡献的证据，成分被均匀加权。全球和区域电路临床评分的内部一致性良好（图 S5），全球评分相互独立，支持其作为典型电路构造的有效性（图 S6）。
量化全球和区域电路临床评分。
An overview of the systematic process used to derive circuit clinical scores based on standardized definitions of the six 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 constituent regions are assigned abbreviated labels (D1, D2, etc.) to facilitate subsequent computations. These regions may be visualized in to reflect the hypothesized direction of dysfunction in depression and anxiety (for example, connections between regions of the salience circuit care are illustrated by dashed lines to indicate hypothesized hypo-connectivity; 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 indicated by “A” and connectivity inputs indicated by “C”.
基于六个感兴趣电路的标准化定义和假设的抑郁和焦虑中这些电路的功能障碍，概述了用于推导电路临床评分的系统过程。这些感兴趣的电路在无任务和任务诱发条件下进行了探测，分别称为默认模式电路、显著性电路、注意力电路、负性情感电路、正性情感电路和认知控制电路。采用标准化程序识别和定义每个电路的组成区域及区域间连接（第 1 行）。在临床受试者中，量化每个组成区域的激活和连接，并以相对于健康参考样本均值的标准化单位表示，使得结果电路临床评分的大小可以相对于健康均值 0 进行解释（第 2 行）。这些区域电路临床评分被分配简 abbreviated labels（D1、D2 等），以便于后续计算。这些组成区域也被分配简 abbreviated labels（D1、D2 等），以便于后续计算。 这些区域可以被视为反映抑郁和焦虑中假设的功能障碍方向（例如，显著性回路区域之间的连接用虚线表示，以指示假设的低连接性；第 2 行）。全球回路临床评分是通过平均区域回路输入计算得出的（第 3 行）。用于生成这些全球回路临床评分的公式与区域输入标签一起显示，区域激活输入用“A”表示，连接输入用“C”表示。
Content and Construct Validation of Clinical Phenotypes
临床表型的内容和构建验证
Symptom Phenotypes
To operationalize symptom phenotypes, we followed a content validation procedure (11). Items from scales with broad symptom coverage (Methods S6A; Table S6) were assigned to clinical phenotypes implicated in our theoretical taxonomy (2) and refined by principal component analysis (PCA), yielding six phenotypes labeled ‘rumination’, ‘anxious avoidance’, ‘threat dysfunction’, ‘anhedonia’, ‘negative bias’, and ‘inattention-cognitive dyscontrol’ (Methods S6B; Table S7). Phenotypes were quantified as the average of standardized scores for each subject (Methods S6C).
症状表型 为了操作化症状表型，我们遵循了一种内容验证程序（11）。来自具有广泛症状覆盖的量表的项目（方法 S6A；表 S6）被分配到我们理论分类法中涉及的临床表型（2），并通过主成分分析（PCA）进行了细化，得出了六种表型，分别标记为“反刍”、“焦虑回避”、“威胁功能障碍”、“快感缺失”、“负面偏见”和“注意力-认知失控”（方法 S6B；表 S7）。表型的量化是通过每个受试者标准化分数的平均值（方法 S6C）。
Behavioral Phenotypes
An equivalent content validation procedure was used to operationalize behavioral phenotypes based on tests assessing general and emotional cognition (Methods S7A) (12). For general cognition, five constructs aligned with a prior PCA conducted during test development (12) - 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 (Methods S7B; Table S8). For emotional cognition, eight constructs aligned with a prior PCA (12, 13): speed for explicit identification of sad, threat, disgust, and happy expressions; and implicit priming of face recognition biased by these expressions (Methods S7B; Table S9). Phenotypes were computed as the averaged standardized test score for each subject (Methods S7C).
行为表型 使用等效内容验证程序对行为表型进行操作化，基于评估一般和情感认知的测试（方法 S7A）（12）。对于一般认知，五个构念与测试开发期间进行的先前主成分分析（PCA）一致（12）——持续注意力（N-Back 连续表现测试）、反应抑制（Go-NoGo）、信息处理速度（Stroop 和 Trails-B）、执行功能（迷宫）和工作记忆（数字跨度）——第六个构念包括在测试开发期间无法获得的干扰测量（方法 S7B；表 S8）。对于情感认知，八个构念与先前的 PCA 一致（12，13）：显性识别悲伤、威胁、厌恶和快乐表情的速度；以及这些表情所偏向的面部识别的隐性启动（方法 S7B；表 S9）。表型计算为每个受试者的标准化测试分数的平均值（方法 S7C）。
Daily Function
Daily function was assessed by the Satisfaction With Life Scale (14) and Social and Occupational Functioning Assessment Scale (15) (Methods S8, Table S10).
日常功能通过生活满意度量表（14）和社会及职业功能评估量表（15）进行评估（方法 S8，表 S10）。
Circuit Clinical Scores and Phenotypes
电路临床评分和表型
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 fMRI 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 false discovery rate (16) for each family of global and regional circuit scores (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 beta coefficient values, indicating the magnitude of change in phenotype associated with one standard deviation change in the circuit predictor. Following the principle that these effect sizes can be interpreted similarly to correlations (17), <0.2 was considered a weak effect, ≥0.2 and ≤0.5 a moderate effect, and >0.5 a strong effect.
假设的电路临床评分与表型之间的一对一映射（图 1）使用回归模型进行测试，模型中包括年龄、性别和被审查的 fMRI 体积数量作为协变量。根据效应大小和在置信区间内效应的普遍性，评估结果的统计显著性和临床意义。我们使用 Benjamini-Hochberg 程序控制每个全局和区域电路评分家族的假发现率（16）（结果 S1）。表 1 中每个结果的 FDR 调整 p 值和 m 值在表 S11 中呈现。效应大小以标准化的 beta 系数值表示，指示与电路预测变量的一个标准差变化相关的表型变化的大小。遵循这些效应大小可以类似于相关性的原则（17），<0.2 被视为弱效应，≥0.2 且≤0.5 为中等效应，>0.5 为强效应。
Table 1. 表 1。
Summary of Results for Relations between Circuit Score and Clinical Phenotypes
电路评分与临床表型之间关系的结果总结
| 1. Results of models testing hypothesized predictions at the Global Circuit level 1. 在全球电路层面测试假设预测的模型结果 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Primary 初级 Sample A 样本 A | Primary 初级 Sample B 样本 B | Generalizability 普遍性 Sample 样本 | ||||||||
| Global Circuit Clinical Score Predictor 全球电路临床评分预测器 | Dependent Variable 因变量 | Domain 领域 | β (ES) | 95% CI 95% 置信区间 | t | p | β (ES) | Within CI 在 CI 内 | β (ES) | Within CI |
| Salience 显著性 | Anxious Avoidance 焦虑回避 | Symptoms 症状 | −0.26 | [0.09, 0.44] | −2.98 | 0.008a | 0.15 | yes 是的 | 0.11 | yes |
| 2. Results of models testing non-hypothesized predictions at the Global Circuit level 2. 在全球电路层面测试非假设预测的模型结果 | ||||||||||
| Primary 初级 Sample A 样本 A | Primary 初级 Sample B 样本 B | Generalizability 普遍性 Sample 样本 | ||||||||
| Global Circuit Clinical Score Predictor 全球电路临床评分预测器 | Dependent Variable 因变量 | Domain 领域 | β (ES) | 95% CI 95% 置信区间 | t | p | β (ES) | Within CI 在 CI 内 | β (ES) | Within CI |
| Default Mode 默认模式 | Negative Bias 负面偏见 | Symptoms 症状 | −0.25 | [−0.40, −0.07] | −2.59 | 0.009a | 0.14 | −0.05 | ||
| Default Mode 默认模式 | Anhedonia 快感缺失症 | Symptoms 症状 | −0.24 | [−0.40, −0.06] | −2.50 | 0.010a | 0.05 | −0.09 | yes | |
| Salience 显著性 | Inattention/Cognitive Dyscontrol 注意力缺陷/认知失调 | Symptoms 症状 | −0.19 | [0.01, 0.35] | −2.03 | 0.031a | 0.15 | yes 是的 | 0.17 | yes |
| Salience 显著性 | Negative Bias 负面偏见 | Symptoms 症状 | −0.26 | [0.07, 0.45] | −2.78 | 0.008a | 0.16 | yes 是的 | 0.17 | yes |
| Salience 显著性 | Threat Dysregulation 威胁失调 | Symptoms 症状 | −0.23 | [0.06, 0.39] | −2.43 | 0.011a | 0.16 | yes 是的 | 0.05 | |
| Salience 显著性 | Anhedonia 快感缺失症 | Symptoms 症状 | −0.27 | [0.06, 0.47] | −2.87 | 0.006a | 0.10 | yes 是的 | 0.09 | yes |
| Salience 显著性 | Satisfaction with life 生活满意度 | Function 功能 | −0.24 | [−0.42, −0.06] | −2.58 | 0.009a | −0.09 | yes 是的 | −0.05 | |
| 3. Results of models testing hypothesized predictions at the Regional Circuit level 3. 区域电路层面假设预测模型测试结果 | ||||||||||
| Primary 初级 Sample A 样本 A | Primary 初级 Sample B 样本 B | Generalizability 普遍性 Sample 样本 | ||||||||
| Regional Circuit Predictor 区域电路预测器 | Dependent Variable 因变量 | Domain 领域 | β (ES) | 95% CI 95% 置信区间 | t | p | β (ES) | Within CI 在 CI 内 | β (ES) | Within CI |
| Default Mode: L AG-amPFC connectivity 默认模式：L AG-amPFC 连接性 | Rumination 反刍 | Symptoms 症状 | −0.21 | [−0.38, −0.01] | −2.39 | 0.029 | −0.07 | yes 是的 | 0.04 | |
| Salience: L AI –L Amy connectivity 显著性：左侧前扣带皮层与左侧杏仁核的连接性 | Anxious Avoidance 焦虑回避 | Symptoms 症状 | −0.26 | [−0.42, −0.11] | −2.96 | 0.006a | −0.23 | yes 是的 | −0.17 | yes |
| Negative Affect (Sad): L AI activation 负面情感（悲伤）：L AI 激活 | Negative Bias 负面偏见 | Symptoms 症状 | −0.20 | [−0.37, −0.01] | −2.15 | 0.027 | −0.17 | yes 是的 | −0.06 | yes |
| Negative Affect (Sad): R AI activation 负面情感（悲伤）：R AI 激活 | Negative Bias 负面偏见 | Symptoms 症状 | −0.21 | [−0.38, −0.01] | −2.15 | 0.029 | −0.23 | yes 是的 | −0.14 | yes |
| Negative Affect (C-Threat): R Amy activation 负面情感（C-威胁）：R Amy 激活 | Threat Speed 威胁速度 | Behavior 行为 | −0.19 | [−0.34, −0.04] | −2.15 | 0.047 | −0.18 | yes 是的 | −0.04 | |
| Positive Affect (Happy): R VS activation 积极情感（快乐）：R VS 激活 | Happy Speed 快乐速度 | Behavior 行为 | −0.20 | [−0.34, −0.06] | −2.28 | 0.045 | −0.06 | yes 是的 | −0.05 | |
| Cognitive Control: ACC activation 认知控制：前扣带皮层激活 | Inattention/Cognitive Dyscontrol 注意力缺陷/认知失调 | Symptoms 症状 | −0.26 | [−0.41, −0.06] | −2.69 | 0.013 | 0.08 | −0.16 | yes | |
| 4. Results of models testing non-hypothesized predictions at the Regional Circuit level 4. 区域电路层面模型测试非假设预测的结果 | ||||||||||
| Primary 初级 Sample A 样本 A | Primary 初级 Sample B 样本 B | Generalizability 普遍性 Sample 样本 | ||||||||
| Regional Circuit Predictor 区域电路预测器 | Dependent Variable 因变量 | Domain 领域 | β (ES) | 95% CI 95% 置信区间 | t | p | β (ES) | Within CI 在 CI 内 | β (ES) | Within CI |
| Default Mode: R AG- amPFC connectivity 默认模式：R AG-amPFC 连接性 | Negativity Bias 负面偏见 | Symptoms 症状 | −0.19 | [−0.37, −0.02] | −2.01 | 0.049 | 0.04 | 0.00 | ||
| Salience: L AI–R AI connectivity 显著性：L AI–R AI 连接性 | Negativity Bias 负面偏见 | Symptoms 症状 | −0.30 | [−0.50, −0.10] | −3.28 | 0.002a | 0.07 | −0.29 | yes | |
| Salience: L AI–R AI connectivity 显著性：L AI–R AI 连接性 | Threat Dysregulation 威胁失调 | Symptoms 症状 | −0.27 | [−0.51, −0.01] | −2.95 | 0.005a | −0.03 | yes 是的 | −0.08 | yes |
| Salience: L AI–R AI connectivity 显著性：L AI–R AI 连接性 | Anhedonia 快感缺失症 | Symptoms 症状 | −0.33 | [−0.52, −0.12] | −3.57 | 0.001a | 0.01 | −0.25 | yes | |
| Salience: L AI–L Amy connectivity 显著性：L AI–L Amy 连接性 | Satisfaction with life 生活满意度 | Function 功能 | 0.18 | [0.01, 0.37] | 1.98 | 0.049 | 0.23 | yes 是的 | −0.12 | |
| Salience: L AI–R AI connectivity 显著性：L AI–R AI 连接性 | Satisfaction with life 生活满意度 | Function 功能 | 0.24 | [0.03, 0.43] | 2.59 | 0.015a | 0.01 | 0.21 | yes | |
1. Results of models testing hypothesized associations of Global Circuit Clinical Scores as Predictors and Phenotypes as Dependent Variables.
1. 模型测试全球电路临床评分作为预测变量与表型作为因变量的假设关联的结果。
a 表示在主要样本 A 中满足家庭-wise FDR 校正为 0.05 的结果，而“是”表示关系在主要样本 B 和/或可推广样本中具有普遍性（即标准化的贝塔系数β落在主要样本 A 的 95%区间内）。
Predictor and Dependent Variable are coded in the same color rerflectinig the reflect the presence of a hypothesized association (e.g. Salience: Circuit Clinical Score – Anxious Avoidance)
预测变量和因变量以相同颜色编码，反映假设关联的存在（例如：显著性：电路临床评分 - 焦虑回避）
2. Results of models testing non-hypothesized associations of Global Circuit Clinical Scores as Predictors and Phenotypes as Dependent Variables. aindicates results meeting family-wise FDR correction of 0.05 in Primary Sample A and ‘yes’ indicates instances where the relationship generalizes to Primary Sample B and/or Generalizability samples. Predictor and Dependent Variable are coded in different color rerflectinig a non-hypothesized association (e.g. Default Mode: Global Circuit Clinical Score – Negativity Bias).
2. 模型测试全球电路临床评分作为预测变量和表型作为因变量的非假设关联的结果。a 表示在主要样本 A 中满足家庭错误发现率（FDR）校正为 0.05 的结果，而“是”表示该关系在主要样本 B 和/或可推广样本中具有普遍性。预测变量和因变量以不同颜色编码，反映出非假设关联（例如，默认模式：全球电路临床评分 - 消极偏见）。
3. Results of models testing hypothesized associations of Regional Circuit Clinical Scores as Predictors and Phenotypes as Dependent Variables. aindicates results meeting family-wise FDR correction of 0.10 in the Primary Sample A and ‘yes’ indicates instances where the relationship generalizes to Primary Sample B and/or Generalizability samples.
3. 模型测试区域电路临床评分作为预测变量与表型作为因变量的假设关联的结果。a 表示在主要样本 A 中满足家庭错误发现率（FDR）校正为 0.10 的结果，而“是”表示关系推广到主要样本 B 和/或可推广样本的情况。
4. Results of models testing non-hypothesized associations of Regional Circuit Clinical Scores as Predictors and Phenotypes as Dependent Variables. aindicates results meeting family-wise FDR correction of 0.10 in the Primary Sample A and ‘yes’ indicates instances where the relationship generalizes to Primary Sample B and/or Generalizability samples.
4. 区域电路临床评分作为预测变量和表型作为因变量的非假设关联模型测试结果。a 表示在主要样本 A 中满足家庭错误发现率（FDR）校正为 0.10 的结果，而“是”表示该关系在主要样本 B 和/或可推广样本中具有普遍性。
Abbreviations: 缩写：
β = Standardized beta coefficients; CI = Confidence Interval for the effect size represented by the value of β; ES = standardized effect size represented by the value of β, standardized beta coefficient for contribution of circuit dysfunction predictors to clinical phenotype.
β = 标准化贝塔系数；CI = 代表β值的效应大小的置信区间；ES = 代表β值的标准化效应大小，标准化贝塔系数用于表示电路功能障碍预测因子对临床表型的贡献。
Regional Abbreviations: 地区缩写：
ACC = Anterior Cingulate Cortex; AG = Angular Gyrus; AI = Anterior Insula; amPFC = anterior medial PreFrontal Cortex; Amy = Amygdala; C = Consicous; L= Left; R= Right; VS = ventral Striatum.
ACC = 前扣带皮层; AG = 角回; AI = 前岛叶; amPFC = 前内侧前额叶皮层; Amy = 杏仁体; C = 意识; L = 左; R = 右; VS = 腹侧纹状体。
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 1,000 random permutations (18) and significant effects were i by an FDR-corrected threshold of .05 (Table 1.1; 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; Results S1B). Relationships surviving FDR correction in primary sample A were considered to have generalized if beta effect sizes of sample B and/or generalizability samples fell within the 95% bootstrapped confidence interval for sample A.
一阶回归模型在主要样本 A 中运行，以测试假设的全局电路-表型关联。在这些模型中，t 统计量与通过 1000 次随机置换得出的 t 分数的零分布进行比较（18），显著效应通过 FDR 校正阈值 0.05 进行识别（表 1.1；结果 S1A）。二阶回归模型测试假设的区域电路-表型关联，显著效应通过 FDR 校正阈值 0.1 进行定义（表 1.2；结果 S1B）。在主要样本 A 中经过 FDR 校正的关系被认为是具有普遍性的，如果样本 B 和/或普遍性样本的 beta 效应大小落在样本 A 的 95%自助法置信区间内。
Circuit Dysfunctions and Treatment Outcomes
电路功能障碍与治疗结果
Using logistic regression models, we first tested whether global circuit clinical scores are general predictors of response, over and above pre-treatment 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, 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 chi-squared 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 .05 were considered in supplemental analyses to inform future investigations. Effect sizes for regional predictors that contributed to treatment outcomes were reported.
使用逻辑回归模型，我们首先测试了全球回路临床评分是否在治疗前症状严重程度的基础上，作为反应的普遍预测因子。接下来，我们使用交互项评估全球回路临床评分作为反应的差异预测因子，具体取决于治疗类型：选择性血清素再摄取抑制剂（SSRIs：舍曲林，艾司西酞普兰）或选择性血清素-去甲肾上腺素再摄取抑制剂（SNRI：缓释文拉法辛）用于抗抑郁药，以及积极的行为干预（I-CARE）或常规护理（U-CARE）用于行为干预。采用分层步骤进行平行模型，通过卡方检验评估每组全球和区域回路预测因子。显著效应的定义为经过 FDR 校正的阈值为 0.1，而在未校正的阈值为 0.05 时的倾向在补充分析中被考虑，以为未来的研究提供信息。报告了对治疗结果有贡献的区域预测因子的效应大小。
RESULTS 结果
Circuit Clinical Scores and Phenotypes
电路临床评分和表型
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).
总体观察结果显示，临床表型与无任务条件下的全球电路临床评分相关，以及在任务条件下与区域评分相关（表 1，图 4）。
全球电路临床评分与表型之间关联的可视化。
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] and the Salience circuit [green], to the Anxious Avoidance phenotype [green]). a Significant relationships are defined as those that survive the false discovery rate (FDR) threshold using the Benjamini-Hochberg procedure at q=0.05.
观察到全球电路临床评分（下半部分；虚线以下）与理论驱动的症状表型（上半部分；虚线以上）之间的关系。主要样本 A 中的显著关系通过较粗、较深的线条表示，丝带的颜色代表特定的电路，线条的粗细表示效应大小（标准化回归系数值）的大小和在样本之间效应的一致性。圆圈上半部分最外层环的颜色代表相应的假设一对一电路与表型的映射（例如，默认模式网络[蓝色]被假设映射到反刍表型[蓝色]，而显著性电路[绿色]则映射到焦虑回避表型[绿色]）。a 显著关系被定义为那些在假发现率（FDR）阈值下存活的关系，使用 Benjamini-Hochberg 程序，q=0.05。
Abbreviations: C Threat = Conscious Threat.
缩写：C Threat = 意识威胁。
Default Mode Circuit 默认模式电路
Global default mode scores reflective of hyper-connectivity were not associated with rumination as operationalized by our phenotype. However, global default mode hypo-connectivity 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).
全球默认模式分数反映的超连接性与我们表型所操作化的反刍思维无关。然而，全球默认模式低连接性显著预测了在 FDR 调整阈值下更严重的负偏见和快感缺失，效应大小为低到中等，并在可推广样本中保持一致（表 1.1；图 4）。
Lower default mode connectivity specific to the left angular gyrus (AG) and anterior medial Prefrontal Cortex (dmPFC) 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).
左角回（AG）和前内侧前额叶皮层（dmPFC）特定的较低默认模式连接与更严重的反刍思维相关（表 1.2；图 5）。尽管这一关联未达到 FDR 调整后的阈值，但在主要样本 A 和 B 中以低至中等的效应大小得到了重复（表 1.3）。
区域电路临床评分与表型之间关联的可视化。
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<0.05) 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.
观察到的区域电路临床评分（每个圆圈的下半部分；虚线以下）与症状和/或行为表型（每个圆圈的上半部分；虚线以上）之间的关系，受到我们的理论综合指导（A=默认模式电路，B=显著性电路，C=注意力电路，D=由悲伤引发的负面情感电路，E=由威胁引发的负面情感电路，F=正面情感电路，G=认知控制电路）。主要样本 A 中的关系（即，未校正的 p<0.05）通过较粗、较深的线条表示，颜色代表所涉及的特定电路，线条的粗细代表效应大小（标准化回归系数值）和跨样本效应的一致性。
a Relationships observed at an uncorrected p<0.05.
在未校正的 p<0.05 下观察到的关系。
Abbreviations: AG = Angular Gyrus; aI = anterior Insula; aIPL = anterior Inferior Parietal Lobule; amPFC = anterior medial Prefrontal Cortex; Amy = Amygdala; dACC = dorsal Anterior Cingulate Cortex; DLPFC = Dorsal Lateral Prefrontal cortex; L = Left; LPFC = Lateral Prefrontal Cortex; vmPFC = ventromedial Prefrontal Cortex; msPFC = medial superior Prefrontal Cortex; pgACC = pregenual Anterior Cingulate Cortex; PCC = Posterior Cingulate Cortex; PCu = Precuneus; R = Right; RT = Reaction Time; vStriatum = ventral Striatum.
缩略语：AG = 角回；aI = 前岛叶；aIPL = 前下顶叶小叶；amPFC = 前内侧前额叶皮层；Amy = 杏仁体；dACC = 背侧前扣带皮层；DLPFC = 背外侧前额叶皮层；L = 左；LPFC = 外侧前额叶皮层；vmPFC = 腹内侧前额叶皮层；msPFC = 内侧上前额叶皮层；pgACC = 前扣带皮层；PCC = 后扣带皮层；PCu = 角回；R = 右；RT = 反应时间；vStriatum = 腹侧纹状体。
Salience Circuit
Salience circuit hypo-connectivity 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 hypo-connectivity and anxious avoidance was of low-moderate effect size that was consistent across all samples (Table 1.1).
显著性回路 显著性回路的低连接性显著预测了各表型中更严重的症状，包括焦虑回避（假设的一对一关联）、负面偏见、威胁失调、快感缺失以及注意力缺陷/认知失控，在 FDR 调整阈值下，样本间一致（表 1.1；图 4）。显著性回路低连接性与焦虑回避的假设关联具有低至中等的效应大小，在所有样本中一致（表 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; Results S1c).
更显著的显著性回路临床评分在 FDR 调整阈值下与生活满意度较低显著相关，效应大小为低到中等，并在主要样本 B 中得到了重复验证（表 1.3；结果 S1c）。
When considering regional connections, the association between hypo-connectivity and anxious avoidance was specific to the left anterior insula and left amygdala (Table 1.2; Figure 5). Left-right insula hypo-connectivity 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).
在考虑区域连接时，低连接性与焦虑回避之间的关联特定于左前岛叶和左杏仁核（表 1.2；图 5）。左右岛叶的低连接性与负偏见、威胁失调和快感缺失的症状相关，以及在 FDR 调整阈值下对生活满意度的更差评估（表 1.3）。
Attention Circuit
For the attention circuit, clinical phenotypes were not associated with global circuit clinical scores or regional connectivity.
注意电路 对于注意电路，临床表型与全球电路临床评分或区域连接性没有关联。
Negative Affect Circuit
For the negative affect circuit evoked by sad stimuli, hypo-activation of the anterior insula, bilaterally, predicted more severe symptoms of negative bias (Table 1.2; Figure 5). These effects did not meet the adjusted alpha 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 hyper-activation to predict accelerated responses to identifying these stimuli at the unadjusted alpha threshold with a weak effect size, consistent across primary samples A and B (Table 1.2; Figure 5).
负面情感回路 对于由悲伤刺激引发的负面情感回路，双侧前岛叶的低活化预测了更严重的负偏见症状（表 1.2；图 5）。这些效应未达到调整后的阿尔法阈值，但在主要样本 A、主要样本 B 和可推广性样本中满足了一致的低至中等幅度效应大小标准。相反，威胁引发的右侧杏仁核高活化倾向于预测在未调整的阿尔法阈值下对识别这些刺激的加速反应，且在主要样本 A 和 B 中一致，效应大小较弱（表 1.2；图 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 alpha threshold with low-moderate effect size, generalizable across two samples (Table 1.2, Figure 5).
积极情感回路 通过快乐刺激探测的积极情感回路与临床表型无关。较低的腹侧纹状体激活显示出与识别快乐面孔的反应速度较慢之间的关联趋势，在未校正的阿尔法阈值下具有低到中等的效应大小，并且在两个样本中具有可推广性（表 1.2，图 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 alpha level with low-moderate effect size consistent across primary A and generalizability samples (Table 1.2; Figure 5).
认知控制回路 背侧前扣带皮层（dACC）的低激活显示出与注意力缺陷/认知失调更严重症状之间的关联趋势，在未调整的显著性水平下，效应大小为低至中等，在主要 A 样本和可推广性样本中一致（表 1.2；图 5）。
Circuit Clinical Scores and Treatment Outcomes
电路临床评分与治疗结果
For pharmacotherapy, we observed regional circuit predictors that were differentially related to SSRI versus SNRI outcomes. Pre-treatment default mode connectivity significantly differentiated response outcomes for SSRIs versus SNRIs (p=0.002; Table S14). SNRI non-responders were distinguished by PCC-angular gyrus hyper-connectivity and SNRI responders by relative hypo-connectivity of these regions, whereas there was a tendency toward an opposing profile of hypo-connectivity in SSRI non-responders and hyper-connectivity in SSRI responders (interaction effect size reflecting the standard deviations increase in the log odds of response versus non-response for SSRI versus SNRI for one standard deviation increase in the predictor = −2.12; Table S17; Figure S8C).
在药物治疗方面，我们观察到区域电路预测因子与 SSRI 和 SNRI 的结果存在不同的关系。预处理的默认模式连接显著区分了 SSRI 与 SNRI 的反应结果（p=0.002；表 S14）。SNRI 非应答者通过 PCC-角回的超连接性被区分，而 SNRI 应答者则表现出这些区域的相对低连接性，而 SSRI 非应答者则倾向于表现出低连接性的相反特征，SSRI 应答者则表现出超连接性（交互效应大小反映了 SSRI 与 SNRI 的反应与非反应的对数几率增加的标准差，预测因子增加一个标准差=−2.12；表 S17；图 S8C）。
Pre-treatment negative affect circuit scores differentiated responders to SSRIs versus SNRIs (Table S14) when elicited by both conscious and nonconscious threat. SSRI responders showed pre-treatment hyper-connectivity of the left amygdala and dACC, and hypo-connectivity of the right amygdala and dACC for conscious threat. SNRI responders showed hypo-activation of the right amygdala and comparative hyper-connectivity of the left amygdala and subgenual ACC for nonconscious threat (Table S17. Figure S8C).
预处理的负面情感回路评分区分了对选择性血清素再摄取抑制剂（SSRIs）和去甲肾上腺素再摄取抑制剂（SNRIs）的反应者（表 S14），无论是在有意识还是无意识的威胁下。SSRIs 反应者在有意识威胁下表现出左侧杏仁核和背外侧前额叶皮层（dACC）的超连接，而右侧杏仁核和 dACC 的连接则较低。SNRIs 反应者在无意识威胁下表现出右侧杏仁核的低激活以及左侧杏仁核和下前扣带皮层的相对超连接（表 S17，图 S8C）。
For the behavioral intervention, pre-treatment attention regional connectivity was a differential predictor of subsequent response to I-CARE versus U-CARE (Table S16). I-CARE responders showed hypo-connectivity between the left anterior inferior parietal lobule and left prefrontal cortex within the attention circuit, compared to responders in U-CARE (Table S17; Figure S8D).
对于行为干预，治疗前注意力区域的连接性是 I-CARE 与 U-CARE 后续反应的差异预测因子（表 S16）。与 U-CARE 的反应者相比，I-CARE 的反应者在注意力回路中显示出左前下顶叶与左前额叶皮层之间的低连接性（表 S17；图 S8D）。
Affect circuit function was also a differential predictor of behavioral intervention outcomes (Table S16). I-CARE responders were distinguished by lower ventromedial PFC activation compared to non-responders, 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 non-response to U-CARE (Table S16, S17; Figure S10D).
情感回路功能也是行为干预结果的差异预测因素（表 S16）。与非应答者相比，I-CARE 应答者的腹内侧前额叶激活水平较低，而 U-CARE 则观察到相反的情况（表 S17；图 S10D）。在威胁引发的负面情感回路中，相对较低的左侧杏仁核活动区分了对 I-CARE 的反应，但对 U-CARE 的非反应（表 S16，S17；图 S10D）。
DISCUSSION 讨论
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.
我们开发了一个可重复的图像处理系统，用于量化个体神经电路指标，并测试了这些指标在显示与临床症状、行为和社会职业功能以及治疗反应之间关系的临床实用性。我们的方法为在心理健康领域，特别是对抑郁症和焦虑症的精准进展迈出了重要一步，这些疾病对疾病负担和自杀的贡献不成比例。
Our image processing system integrates four 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 three samples of adults with a broad range of depression and anxiety symptoms, and systematically examined brain circuit-phenotype relations informed by our theoretical framework (2). 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.
我们的图像处理系统集成了四个关键特征：标准化、经过质量控制的功能脑回路的神经解剖学定义，涵盖无任务和任务诱发的情境；可重复的程序，用于量化每个回路内区域的激活和连接性，并显示出一致性；以及计算指标的算法，这些指标量化个体受试者相对于健康参考样本的全球和区域回路临床评分。我们在三组具有广泛抑郁和焦虑症状的成年人中测试了该系统，并系统地检查了基于我们的理论框架（2）的脑回路-表型关系。我们发现，关于回路临床评分与反映该领域对神经-表型关系的共同假设的特定表型之间的假设一对一映射的证据有限。 然而，我们确实识别出了一些关联，表明特定的连接特征——特别是在显著性和默认模式电路内——可能导致多种表型表现，并且额外的电路激活和连接特征与治疗反应有关。
Within the task-free circuits, salience circuit clinical scores, especially hypo-connectivity between the anterior insula and the amygdala, was significantly predictive of anxious avoidance symptoms at the adjusted alpha level, and generalized across samples, consistent with hypotheses (2). Salience circuit hypo-connectivity 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 (19). Global salience hypo-connectivity 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 (20) that may fluctuate with interoceptive and external events, future investigations that expand our current within-circuit focus to examine between-circuit connectivity are warranted.
在无任务电路中，显著性电路的临床评分，特别是前岛叶与杏仁核之间的低连接性，在调整后的显著性水平下显著预测了焦虑回避症状，并在样本中普遍存在，与假设一致（2）。岛叶内的显著性电路低连接性也显著影响了无快感症状、负偏见和威胁失调，并在至少一个额外样本中普遍存在。这些发现表明，岛叶的断连在负偏见和钝化的积极情绪特征中发挥了作用，这些特征影响日常功能，与代谢岛叶成像的发现一致（19）。全球显著性低连接性与注意力/认知失控症状之间显示出额外的显著关联，并在样本中普遍存在。鉴于先前证据表明显著性和注意力电路之间的功能交互（20）可能随着内感受和外部事件的变化而波动，未来的研究应扩展我们当前的电路内关注，考察电路之间的连接性。
Although default mode hyper-connectivity was not predictive of rumination as hypothesized, global hypo-connectivity was significantly associated with negative bias and anhedonia at the adjusted alpha level. Such hypo-connectivity is consistent with emerging evidence for a default mode hypo-connectivity subtype of depression (21, 22) and the exploratory default mode biotype proposed in our theoretical framework (1, 2), informed by meta-analysis (23). 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 pre-treatment hyper-connectivity of the posterior cingulate and angular gyrus within the default mode circuit distinguished non-responders from responders to the SNRI in particular. This observation of hyper-connectivity accords with prior findings for dulexotine, which also inhibits both serotonin and norepinephrine uptake and has been found to regularize pre-treatment default mode hyper-connectivity (24). It also extends upon 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 (25, 26). Further, SNRI responders were characterized by pre-treatment amygdala hypo-activation within the negative affect circuit, consistent with prior group-averaged findings in this dataset (27). The new finding that SNRI responders are distinguished by amygdala-subgenual anterior cingulate (ACC) hypo-connectivity for nonconscious threat, and SSRI responders by an opposing profile of amygdala-dorsal ACC hyper-connectivity for conscious threat, suggests that amygdala-ACC connectivity might reflect different functional states that are present prior to treatment and that respond to the different ways that the drug types act at the receptor level.
For behavioral intervention, pre-treatment global hypo-connectivity within the attention circuit was a significant differential predictor of response to the active I-CARE condition, consistent with independent reports that such hypo-connectivity could inform selection for cognitive behavior therapy (28). 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 fMRI 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 six 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 (1, 2). 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 (29).
Both the null findings and non-hypothesized 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, 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 MDD was three 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 (30). 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 (1, 2). 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 pursue 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 fMRI reward tasks. Future investigations are also warranted to expand our initial focus on a specific set of regions informed by prior knowledge (2) 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 sub-nodes, between-circuit effects, and interactions within and between circuits (1, 2). For example, parsing of sub-nodes 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 (2, 31), and accounting for interactions between default mode, attention and cognitive control circuits may provide a more complete characterization of a cognitive dyscontrol biotype (32). Methodologically, it would be valuable to pursue direct tests of the impact of scanner, site, and functional localizers for more precise subject-level quantification (33) 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 those circuit markers that predict treatment outcomes, help select among multiple treatment options, and/or change with treatment (29). Precision medicine, 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 trait-like versus state-like and thus which are more amenable to change with treatment.
Conclusion
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 clnical management of mood and anxiety.
KEY RESOURCES TABLE
| Resource Type | Specific Reagent or Resource | Source or Reference | Identifiers | Additional Information |
|---|---|---|---|---|
| Add additional rows as needed for each resource type | Include species and sex when applicable. | Include name of manufacturer, company, repository, individual, or research lab. Include PMID or DOI for references; use “this paper” if new. | Include catalog numbers, stock numbers, database IDs or accession numbers, and/or RRIDs. RRIDs are highly encouraged; search for RRIDs at https://scicrunch.org/resources. | Include any additional information or notes if necessary. |
| Antibody | NA | |||
| Bacterial or Viral Strain | NA | |||
| Biological Sample | NA | |||
| Cell Line | NA | |||
| Chemical Compound, Drug | NA | |||
| Commercial Assay Or Kit | NA | |||
| Deposited Data; Public Database | NDA | NIMH Data Archive | https://nda.nih.gov/, Collection C2100 | |
| Organism/Strain | NA | |||
| Sequence-Based Reagent | NA | N/A | ||
| Software; Algorithm | MATLAB-2014b | MathWorks | RRID:SCR_001622 | |
| Software; Algorithm | Singularity 3.2.1 | https://github.com/sylabs/singularity/releases/tag/v3.2.1 | N/A | |
| Software; Algorithm | FSL 5.0 | Analysis Group, FMRIB, Oxford, UK | RRID:SCR_002823 | |
| Software; Algorithm | SPM 8 | Wellcome Centre for Human Neuroimaging, UCL | RRID:SCR_007037 | |
| Software; Algorithm | AFNI 19.0.07 | NIMH Scientific and Statistical Computing Core | RRID:SCR_005927 |
ACKNOWLEDGEMENTS AND DISCLOSURES
This work was supported by the National Institutes of Health [grant numbers R01MH101496 (LMW; NCT02220309), UH2HL132368 (JM, LMW; NCT02246413), F32MH108299 (ANG-P), T32MH019938 (TMB), and K23MH113708 (TMB)]. Psychopharmacology data from iSPOT-D (NCT00693849) was sponsored by Brain Resource Ltd.
We acknowledge the contributions of Sarah Chang, BSc, to data acquisition and generating of sample tables and Carlos Correa, BCompSc, to software development of the image processing system. We acknowledge the editorial support of Jon Kilner, MS, MA (Pittsburgh, PA, USA).
The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
The datasets for the primary sample analyzed during the current study are made available through the National Institutes of Health Database, NDA, 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 Behavioral Change repository, https://scienceofbehaviorchange.org/measures/.
LMW declares US Pants. App. 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 five years. All other authors report no biomedical financial interests or potential conflicts of interest.
Funding:
This work was supported by the National Institutes of Health [grant numbers R01MH101496 (LMW), UH2HL132368 (JM, LMW), F32MH108299 (ANG-P), T32MH019938 (TMB), and K23MH113708 (TMB)].
Footnotes
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aEquivalent threat vs neutral contrasts were undertaken for stimuli presented under conscious and nonconscious conditions.