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Mood Variability Craving and Substance Use Disorders: From Intrinsic Brain Network Connectivity to Daily Life Experience

Open AccessPublished:November 09, 2022DOI:https://doi.org/10.1016/j.bpsc.2022.11.002

      Abstract

      Background

      Substance use disorders (SUD) are major contributors to morbidity and mortality rates worldwide, and this global burden is attributable in large part to the chronic nature of these conditions. Increased mood variability might represent a form of emotional dysregulation that may have particular significance for the risk of relapse in SUD, independently of mood severity or diagnostic status. However, the neural biomarkers that underly mood variability remain poorly understood.

      Methods

      Ecological Momentary Assessment (EMA) was used to assess mood variability, craving and substance use in real time in 54 patients treated for addiction to alcohol, cannabis, or nicotine and 30 healthy controls. Such data were jointly examined relative to spectral dynamic causal modeling (DCM) of effective brain connectivity within four networks involved in emotion generation and regulation.

      Results

      Differences in effective connectivity were related to daily life variability of emotional states experienced by persons with SUD, and mood variability was associated with craving intensity. Relative to the control participants, effective connectivity was decreased for patients in the prefrontal control networks and increased in the emotion generation networks. Findings revealed that effective connectivity within the patient group was modulated by mood variability.

      Conclusions

      The intrinsic causal dynamics in large-scale neural networks underlying emotion regulation play a predictive role in the patient's susceptibility to experiencing mood variability (and, subsequently, craving) in daily life. The findings represent an important step toward informing interventional research through biomarkers of factors that increase the risk of relapse in persons with SUD.

      Keywords

      Introduction

      Global burden of disease estimates have consistently ranked substance use disorders (SUD) as among the greatest contributors to morbidity and mortality rates worldwide (
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      ) and were not developed to target pathophysiological mechanisms associated with addiction. The lack of knowledge of the neural biomarkers underlying emotion dysregulation in persons with SUD, therefore, represents an important missing link to identifying individual vulnerabilities that may reliably predict treatment response and relapse risk (
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      Common alterations in the brain’s prefrontal-subcortical circuitries among persons with SUD can contribute to substance-specific behaviors (
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      ), and indicate differences in the recruitment of functional networks underlying emotion generation and regulation (
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      • Berboth S.
      • Eickhoff S.B.
      • Laird A.R.
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      Multiple large-scale neural networks underlying emotion regulation.
      ). Reviews and meta-analyses (
      • Wilcox C.E.
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      • Adinoff B.
      Neural Circuitry of Impaired Emotion Regulation in Substance Use Disorders.
      ,
      • Wilcox C.E.
      • Abbott C.C.
      • Calhoun V.D.
      Alterations in resting-state functional connectivity in substance use disorders and treatment implications.
      ,

      Zhang R, Volkow ND (2019, October 15): Brain default-mode network dysfunction in addiction. NeuroImage, vol. 200. Academic Press Inc., pp 313–331.

      ) investigating the intrinsic functional architecture in SUD have reported decreased resting-state functional and structural connectivity between prefrontal and subcortical regions, which might result in impaired top-down control of emotion-generating regions. In addition, decreased resting-state functional connectivity between and within prefrontal regulatory networks and disrupted default-mode network (DMN) connectivity to other large-scale networks such as the executive control (ECN) and salience networks have been observed in SUD. Studies examining large-scale networks in emotion dysregulation found decreased intrinsic connectivity with the DMN and ECN in anxiety disorders (
      • Xu J.
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      ), and major depression has been linked to reduced connectivity within the frontoparietal network and the dorsal attention network (
      • Kaiser R.H.
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      • Wager T.D.
      • Pizzagalli D.A.
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      ). Taken together, these findings suggest that decreased functional connectivity within regulatory networks on the one hand, and emotion generating/processing networks on the other, might explain poor emotion regulation in SUD. However, previous studies have essentially been limited to examining functional connectivity between different networks and its correlation with diagnostic status (
      • Wilcox C.E.
      • Abbott C.C.
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      Alterations in resting-state functional connectivity in substance use disorders and treatment implications.
      ). Very recent investigations have begun to move beyond this traditional approach in order to develop more robust models to predict emotion regulation, treatment response and substance use through advanced analytic approaches such as machine learning and cross-validation (
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      ). So far, only few studies successfully linked daily affect dynamics with brain networks (e.g., 38,40). Despite their novelty from the point of view of neuroimaging, however, these studies remain limited to assessments of symptom averages or total severity scores. The association of brain biomarkers with the core phenomena of emotion dysregulation, notably increased emotional variability (
      • Lamers F.
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      ), have yet to be examined.
      The present study investigates putative large-scale neural networks that have been shown to be reliably implicated in emotion generation and regulation (
      • Berboth S.
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      • Kohn N.
      • Morawetz C.
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      ) and examines their direct link to changes in the daily life variability of emotional states as they are experienced by persons with SUD in real time. Specifically, resting-state functional magnetic resonance imaging (rs-fMRI) is used to test effective (causal) connectivity within four predefined large-scale networks underlying emotion generation and regulation (
      • Morawetz C.
      • Riedel M.C.
      • Salo T.
      • Berboth S.
      • Eickhoff S.B.
      • Laird A.R.
      • Kohn N.
      Multiple large-scale neural networks underlying emotion regulation.
      ) by implementing spectral dynamic causal modeling (spDCM) (
      • Friston K.J.
      • Kahan J.
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      • Razi A.
      A DCM for resting state fMRI.
      ,
      • Park H.J.
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      ). The predefined networks were obtained from a recent meta-analysis of emotion regulation processes (
      • Morawetz C.
      • Riedel M.C.
      • Salo T.
      • Berboth S.
      • Eickhoff S.B.
      • Laird A.R.
      • Kohn N.
      Multiple large-scale neural networks underlying emotion regulation.
      ) and included two lateral prefrontal networks (N1 and N2) implicated in working memory, language, and attention, and two subcortical networks (N3 and N4), including the amygdala and insula involved in emotion generation/processing and interoception. By assessing effective connectivity in these networks in patients with SUD and healthy controls, we examine their link with mood variability assessed in the natural contexts of daily life by Ecological Momentary Assessment (EMA). We specifically hypothesized that: i. persons with SUD would experience greater mood variability than healthy control participants (
      • Garke M.
      • Isacsson N.H.
      • Sörman K.
      • Bjureberg J.
      • Hellner C.
      • Gratz K.L.
      • et al.
      Emotion dysregulation across levels of substance use.
      ,
      • Bradizza C.M.
      • Brown W.C.
      • Ruszczyk M.U.
      • Dermen K.H.
      • Lucke J.F.
      • Stasiewicz P.R.
      Difficulties in emotion regulation in treatment-seeking alcoholics with and without co-occurring mood and anxiety disorders.
      ,
      • Baker T.B.
      • Piper M.E.
      • McCarthy D.E.
      • Majeskie M.R.
      • Fiore M.C.
      Addiction Motivation Reformulated: An Affective Processing Model of Negative Reinforcement.
      ), and that greater mood variability would in turn, be associated with greater craving intensity (
      • Khosravani V.
      • Sharifi Bastan F.
      • Ghorbani F.
      • Kamali Z.
      Difficulties in emotion regulation mediate negative and positive affects and craving in alcoholic patients.
      ); ii. persons with SUD would demonstrate decreased connectivity in the prefrontal control (N1 and N2) and emotion generation networks (N3 and N4) compared with healthy controls (
      • Wilcox C.E.
      • Pommy J.M.
      • Adinoff B.
      Neural Circuitry of Impaired Emotion Regulation in Substance Use Disorders.
      ,
      • Wilcox C.E.
      • Abbott C.C.
      • Calhoun V.D.
      Alterations in resting-state functional connectivity in substance use disorders and treatment implications.
      ,

      Zhang R, Volkow ND (2019, October 15): Brain default-mode network dysfunction in addiction. NeuroImage, vol. 200. Academic Press Inc., pp 313–331.

      ); and iii. mood variability in SUD would be negatively associated with connectivity within the regulatory networks (N1 and N2) and positively associated with connectivity in the emotion generation/processing networks (N3 and N4).

      Methods

      Participants

      A total of 126 individuals (86 patients with SUD and 40 healthy controls) participated in the EMA study. Eighty-four individuals (54 patients and 30 healthy controls) of the full sample also completed an MRI examination. See Supplement for further description of participants and inclusion criteria.

      Procedure

      DSM-IV-TR diagnoses of substance use disorders were established based on criteria assessed by the Mini International Neuropsychiatric Interview French Version 5.0.0 (MINI(50)). After verifying eligibility, all participants completed a clinical assessment battery and were trained to operate a study-dedicated smartphone (Samsung Galaxy S with a 10.6 cm screen, 12-point font size). The surveys occurred over a 7-day period, five times per day at random intervals within 5 equal time epochs from morning to evening (approximately every 3h). Participants recruited for the primary investigation involving EMA were also allowed to participate in the ancillary MRI study. The subsample of individuals who received an MRI examination did so within 48h before completing clinical testing and EMA. Financial compensation was provided with a maximum of €100 in purchase vouchers to complete both the EMA and MRI phases of the study. The feasibility and validity of the EMA protocol used in this investigation have previously been demonstrated in patients with SUD (
      • Fatseas M.
      • Serre F.
      • Alexandre J.
      • Debrabant R.
      • Auriacombe M.
      • Swendsen J.
      Craving and substance use among patients with alcohol, tobacco, cannabis or heroin addiction: a comparison of substance- and person-specific cues.
      ,
      • Fatseas M.
      • Serre F.
      • Swendsen J.
      • Auriacombe M.
      Effects of anxiety and mood disorders on craving and substance use among patients with substance use disorder: An ecological momentary assessment study.
      ,
      • Serre F.
      • Fatseas M.
      • Debrabant R.
      • Alexandre J.M.
      • Auriacombe M.
      • Swendsen J.
      Ecological momentary assessment in alcohol, tobacco, cannabis and opiate dependence: A comparison of feasibility and validity.
      ).

      Ecological Momentary Assessment

      Participants were prompted to respond to diverse questions concerning their emotions, behaviors, experiences, and environmental contexts at each EMA survey. In particular, they were asked to describe the maximum level of craving since the previous assessment on a seven-point scale ranging from 1 (no desire to use substances) to 7 (extreme desire to use substances). They were also asked if they had used the substance that initiated their treatment since the previous EMA assessment, followed by questions about the quantity (if any) of the consumed substance and other descriptive information. Participants were asked about any other form of a psychoactive substance used during that time period that was not the target of treatment (alcohol, tobacco, cannabis, opiates, cocaine, amphetamine, and other substances). Based on the mood circumplex model (

      Larsen RJ, Diener E (1992): Promises and problems with the circumplex model of emotion. In: Clark M, editor. Emotion. pp 25–59.

      ), a 7-point scale was used at each EMA assessment to assess the continuum of mood experienced by the participant at that moment, ranging from 1 (very sad) to 7 (very happy).
      rs-fMRI data acquisition
      See Supplement for details.

      Data analysis

      EMA data analysis. Hierarchical linear and non-linear modeling (HLM version 8.0(55)) was used to account for the multilevel structure of the EMA data that involved within-person variance in mood, craving, and substance use, as well as between-person variance in clinical and sociodemographic characteristics. Mood variability was based on calculating the average standard deviation (SD) of mood scores acquired for each day of the 7-day EMA period. These within-day SD coefficients were then examined for their association with the averages of craving for that same day and with within-day sums of substance use occasions (for both the treated substance and any substance). The within-person coefficients calculated for each day of the EMA sampling period were also extracted for use in neuroimaging analyses.
      Neuroimaging analysis. The analytic pipeline is illustrated in Figure 1.
      Figure thumbnail gr1
      Figure 1Overview of key processing steps for predictive analysis of mood variability from rs-FMRI spectral DCM parameters from four predefined networks.
      Step 1: Determining GM volume differences. The anatomical scans were preprocessed with the Computational Anatomy Toolbox (CAT) implemented in SPM12 (http://www.neuro.uni-jena.de/cat/index.html#VBM). See Supplement for further description of preprocessing. We tested for group differences in GM volume of the network-specific regions by applying two-sample t-tests with group as factor and masked by each network. No differences in GM volume were determined between patients and healthy controls in any of the ROIs of the four networks.
      Step 2: Image Preprocessing. Resting-state functional MRI and T1-weighted MRI images were preprocessed using fMRIPrep 20.2.1 (
      • Esteban O.
      • Markiewicz C.J.
      • Blair R.W.
      • Moodie C.A.
      • Isik A.I.
      • Erramuzpe A.
      • et al.
      fMRIPrep: a robust preprocessing pipeline for functional MRI.
      ), which is based on Nipype 1.5.1 (
      • Gorgolewski K.
      • Burns C.D.
      • Madison C.
      • Clark D.
      • Halchenko Y.O.
      • Waskom M.L.
      • Ghosh S.S.
      Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python.
      ). See Supplement for further description of preprocessing.
      Step 3: Definition of functional brain networks and time series extraction. We defined regions-of-interest (ROIs) based on a recently published meta-analysis investigating the neural correlates of emotion regulation (
      • Morawetz C.
      • Riedel M.C.
      • Salo T.
      • Berboth S.
      • Eickhoff S.B.
      • Laird A.R.
      • Kohn N.
      Multiple large-scale neural networks underlying emotion regulation.
      ). This meta-analysis identified four large-scale neural networks using a meta-analytic hierarchical clustering approach (network 1 [N1]: 10 nodes; network 2 [N2]: 8 nodes; network 3 [N3]: 8 nodes; network 4 [N4]: 9 nodes) (illustrated in Supplemental Figure S1). The MNI coordinates of all ROIs can be found in Table 1 and are illustrated in Figure 2. N1 and N2 are mainly related to emotion regulatory processes, while N3 and N4 are involved in emotion generative and physiological processes. We extracted blood-level dependent (BOLD) time-series from each network's predefined functional brain regions for each subject. See Supplement for further description.
      Table 1MNI coordinates of ROIs identified by meta-analysis
      NetworkSideRegionBACoordinates
      xyz
      1LSuperior Frontal Gyrus (LSFG)802450
      RMiddle Frontal Gyrus (RMFG)8402442
      RInferior Parietal Lobule (RIPL)4058-5238
      LInferior Parietal Lobule (LIPL)40-58-5044
      LMiddle Frontal Gyrus (LMFG)10-3652-2
      LMiddle Frontal Gyrus (LMFG)6-421448
      RMiddle Frontal Gyrus (RMFG)114246-8
      RInsula (RINS)1336166
      RAnterior Cingulate Cortex (RACC)232-2230
      RPrecuneus (RPreCun)710-6436
      2LInferior Frontal Gyrus (LIFG)47-4624-8
      LSuperior Frontal Gyrus (LSFG)6-41062
      RInferior Frontal Gyrus (RIFG)475028-8
      LSuperior Temporal Gyrus (LSTG)39-46-5228
      LMiddle Temporal Gyrus (LMTG)*-54-34-2
      LMiddle Frontal Gyrus (LMFG)6-44650
      LSuperior Frontal Gyrus (LSFG)9-304826
      LCaudate (LCaudate)*-161012
      3LAmygdala (LAMY)*-22-4-16
      RAmygdala (RAMY)*24-4-18
      RFusiform Face Area (RFFA)3740-46-18
      RThalamus (RThal)*6-260
      LFusiform Face Area (LFFA)37-38-54-14
      LParahippocampal Gyrus (LPHG)27-22-28-4
      BMedial Frontal Gyrus (MedFG)10054-10
      LInferior Occipital Gyrus (LIOG)19-42-76-6
      4LPostcentral Gyrus (LPostCG)2-58-2232
      LInsula (LINS)13-44-410
      LSuperior Parietal Lobule (LSPL)7-28-5256
      RPostcentral Gyrus (RPostCG)262-2230
      LCuneus (LCun)18-10-7622
      LMiddle Occipital Gyrus (LMOG)19-48-742
      RThalamus (RThal)*10-26-4
      RPrecuneus (RPreCun)1928-6038
      RPosterior Cingulate (RPCC)3016-5616
      Side: L=left, R=right, B=bilateral.
      Figure thumbnail gr2
      Figure 2Regions-of-interest (ROI) definition and fully connected model. The initial model for each network assumed a fully connected intrinsic architecture within (A) network 1 (N1, 10 nodes, 100 connections), (B) network 2 (N2, 8 nodes, 64 connections), (C) network 3 (N3, 8 nodes, 64 connections), and (D) network 4 (N4, 9 nodes, 81 connections). ROIs, represented in circles, are color-coded for various brain regions (e.g., frontal, temporal, parietal, etc.).
      Step 4: Deterministic spectral DCM. Spectral dynamic causal modeling (spDCM) was performed using DCM12 as implemented in SPM12. DCM tests the directed functional interactions between brain regions. Connectivity between ROIs is measured in Hertz (Hz). Positive values indicate an excitatory connection, while negative values represent an inhibitory connection. See Supplement for further description of spDCM.
      Step 5: PEB model. On the second level, we were interested in modulating the connectivity between neural networks by mood variability. For this, we used the hierarchical Parametric Empirical (PEB) framework for DCM(58), which is reported in detail in the Supplement.
      Four PEB analyses were conducted: 1) testing the group difference in each effective connectivity between the SUD and control groups; and 2) testing the linear relationship between each effective connectivity and the mood variability score (main regressor of interest) within the SUD group; 3) testing the linear relationship between each effective connectivity and the mood variability score (main regressor of interest) within the SUD group corrected for lifetime depression (reported in the Supplement); 4) testing the linear relationship between each effective connectivity and the mood variability score (main regressor of interest) within the SUD group corrected for lifetime depression and substance type (reported in the Supplement). In all PEB models, age, gender, and the mean frame-to-frame displacement were modeled as regressors of no interest. Only effects (i.e., changes in effective connectivity) that show a posterior probability >.95 are reported.
      Step 6: Prediction - cross-validation. In a final step, leave-one-out cross-validation (LOOCV) was performed to determine the robustness of observed effect sizes. This procedure assessed the predictive validity of mood variability, i.e., whether the effect size was large enough to predict a left-out patient's mood variability score from the connectivity within the four networks within the SUD group. LOOCV was performed for each significant connection separately.

      Results

      Demographic and clinical results

      Table 2 presents the sociodemographic and clinical characteristics of the sample. Craving intensity was greater in patients than controls and for the cannabis relative to the alcohol group, and use of the substance at the origin of treatment was more frequent for the nicotine group than the other substance groups. Mood negativity and mood variability were significantly greater in patients than in the control group. Those in the alcohol group had greater negative mood than those in the nicotine group. No significant sex differences in mood variability were found between men and women in the patient (t=-0.459, p=0.647) and healthy control group (t=1.074, p=0.290). The subsample who completed the MRI examination did not differ from those who participated only in EMA for variables presented in Table 2, except those healthy controls who received an MRI were more compliant than controls who did not (96% versus 89%). This MRI group also included proportionately more men than the EMA-only group.
      Table 2Description of sociodemographic, clinical and EMA variables for the sample
      Healthy Controls (n=40)

      M SD %
      Any SUD (n=86)

      M SD %
      Alcohol (n=36)

      M SD %
      Nicotine (n=34)

      M SD %
      Cannabis (n=16)

      M SD %
      Age33.62 8.2740.10 11.65**44.12B 10.9539.34 11.8832.64 8.93
      Sex (% female)504336A6219
      Education (years)14.45 3.0013.05 2.54*13.23 2.2513.21 2.9112.25 2.32
      Lifetime comorbidity
      Mood558***615656
      Anxiety344***443563
      Psychosis323**8A3825
      Any880***758581
      EMA
      Compliance32.87 1.92 9429.87 3.64*** 8530.61 2.96 8729.88 3.22 8528.19 5.26 81
      Craving intensity1.03 0.092.76 1.18***2.46 0.94B2.73 1.223.49 1.34
      Mood severity5.24 0.694.55 0.77***4.27 0.80A4.85 0.684.58 0.68
      Mood variability0.61 0.190.71 0.26*0.76 0.230.67 0.290.68 0.27
      Substance use (Treated)- -15.83 10.2410.36 8.34A22.56 8.89C13.81 8.89
      Substance use (Any)1.90 2.3523.01 8.66***23.50 8.9823.35 8.6721.19 8.16
      ASI: Addiction Severity Index; EMA: Ecological Momentary Assessment
      *p < .05; **p < .01; ***p < .001;
      A alcohol ≠ nicotine;
      B alcohol ≠ cannabis;
      C nicotine ≠ cannabis

      Association of mood variability with craving and substance use

      Although within-day averages of mood and craving were not associated in the overall sample, γ=0.272, SE=0.180, p > .05, mood variability was significantly linked to increased craving intensity, γ=0.517, SE=0.256, p < .05. Moreover, when these analyses used the average daily severity of mood scores as covariates, the results confirmed the independent association between mood variability and craving intensity, γ=0.559, SE=0.255, p < .05. Subsequent analyses demonstrated that the association of mood variability and craving intensity was significant among patients with a substance use disorder, γ=0.763, SE=0.329, p < .05, and not among healthy controls. For this reason, the link between mood variability and effective connectivity was investigated in SUD patients only. The association of mood variability and craving intensity did not vary in magnitude across the alcohol, nicotine, or cannabis groups. Finally, the magnitude of the within-person association between mood variability and craving intensity in patients was examined as a function of comorbid diagnoses. While no effect was observed for lifetime psychotic or anxiety disorders, those with lifetime depression had a significantly stronger association between mood variability and craving intensity (Table 3). The within-person association of mood variability and craving was not associated with current comorbid diagnoses, and mood variability was not directly associated with the use of the treated substance or use of other substances. Of note, sex had an impact on the within-person association between mood variability and craving, with men showing a more pronounced association than women (coefficient = -0.593, p<0.001).
      Table 3Within-day mood variability and craving intensity as a function of lifetime depression
      VariableCoefSEdfT ratio
      Within-person association

      Mood variability/Craving
      0.6520.322822.023*
      Between-person moderators
      Age-0.0080.01082-0.821
      Sex-0.5930.18982-3.142***
      Depression0.5030.190822.649**
      *p < .05, **p < .01, ***p < .001

      Group comparisons

      The group difference (SUD minus control) in effective connectivity within each network are shown in Figure 3. Positive values (green) indicate increased connectivity for the patients compared to the control group. In contrast, negative values (red) indicate reduced connectivity for the patients compared to the control group. The direction of effective connectivity (excitatory/inhibitory) between two regions is reported in Table 4. See Supplement for further description on the group comparisons.
      Figure thumbnail gr3
      Figure 3Spectral dynamic causal modeling results of group difference (patients > controls). Effective connectivity of group difference within each network. Green/red colors indicate a positive/negative connectivity for patients compared to controls. Effect sizes that survived a 95% posterior confidence criterion or more are shown in color. The matrix can be interpreted as effective connectivity from column (source) to row (target).
      Table 4Direction of effective connectivity between two regions (group comparisons)
      Network 1ConnectivityInteraction with GroupEffect size in Hz
      SourceTarget
      InhibitionLMFG_BA6LIPL_BA40--0.08
      LMFG_BA6RIPL_BA40--0.09
      RPreCun_BA7RMFG_BA8--0.10
      RPreCun_BA7RIPL_BA40--0.10
      RMFG_BA8RPreCun_BA7--0.07
      RMFG_BA8LMFG_BA10--0.08
      RMFG_BA11LIPL_BA40--0.15
      RMFG_BA11RIPL_BA40--0.15
      RIPL_BA40RIPL_BA40--0.23
      LMFG_BA6LMFG_BA6+0.12
      RPreCun_BA7LSFG_BA8+0.11
      RINS_BA13RMFG_BA11+0.09
      RINS_BA13RINS_BA13+0.16
      RACC_BA23RACC_BA23+0.10
      LIPL_BA40RPreCun_BA7+0.10
      LIPL_BA40RMFG_BA11+0.07
      LIPL_BA40RINS_BA13+0.10
      ExcitationLMFG_BA6RPreCun_BA7--0.08
      LMFG_BA6LSFG_BA8--0.11
      LMFG_BA6RMFG_BA8--0.11
      LMFG_BA6LMFG_BA10--0.14
      LMFG_BA6RACC_BA23--0.09
      LSFG_BA8RPreCun_BA7--0.09
      LSFG_BA8RACC_BA23--0.10
      RMFG_BA8RINS_BA13--0.11
      RACC_BA23LMFG_BA10--0.08
      RIPL_BA40LSFG_BA8--0.10
      LMFG_BA10LMFG_BA6+0.12
      LMFG_BA10LIPL_BA40+0.12
      LIPL_BA40RIPL_BA40+0.09
      RIPL_BA40RPreCun_BA7+0.13
      RIPL_BA40RMFG_BA8+0.17
      Network 2
      InhibitionLSFG_BA9LMFG_BA6--0.09
      LSFG_BA9LSFG--0.15
      LSFG_BA9LIFG_BA47--0.10
      LSFG_BA9LMTG--0.09
      LSTG_BA39LSFG--0.09
      LSTG_BA39LMTG--0.11
      LMTGLMTG--0.24
      LCaudateLSFG_BA9--0.15
      LCaudateLIFG_BA47--0.12
      LSFGLSFG+0.14
      RIFG_BA47RIFG_BA47+0.11
      LMTGLMFG_BA6+0.10
      LMTGLIFG_BA47+0.15
      LMTGRIFG_BA47+0.20
      LCaudateLSTG_BA39+0.11
      ExcitationLIFG_BA47LSFG_BA9--0.11
      RIFG_BA47LIFG_BA47--0.09
      LMFG_BA6LSTG_BA39+0.11
      LSFGLSTG_BA39+0.08
      Network 3
      InhibitionMedFG_BA10MedFG_BA10--0.11
      LIOG_BA19RAMY--0.15
      RFFA_BA37LAMY+0.15
      LPHG_BA27LIOG_BA19+0.30
      LAMYLPHG_BA27+0.12
      RAMYRFFA_BA37+0.11
      LIOG_BA19RFFA_BA37+0.18
      RThalMedFG_BA10+0.10
      RThalRFFA_BA37+0.08
      ExcitationLFFA_BA37LFFA_BA37--0.16
      LFFA_BA37RFFA_BA37--0.10
      RFFA_BA37LIOG_BA19--0.16
      RFFA_BA37RThal--0.12
      LPHG_BA27RThal--0.19
      LAMYLFFA_BA37--0.08
      LAMYLAMY--0.15
      LAMYRAMY--0.13
      RAMYRAMY--0.12
      LIOG_BA19LIOG_BA19--0.17
      MedFG_BA10LIOG_BA19+0.08
      LFFA_BA37LPHG_BA27+0.19
      LFFA_BA37RThal+0.18
      LPHG_BA27LPHG_BA27+0.12
      Network 4
      InhibitionLSPL_BA7LMOG_BA19--0.13
      LINS_BA13LMOG_BA19--0.13
      LINS_BA13RPreCun_BA19--0.11
      LCun_BA18LpostCG_BA2--0.17
      LCun_BA18LSPL_BA7--0.14
      LMOG_BA19RPostCG_BA2--0.10
      RPreCun_BA19RPreCun_BA19--0.23
      RPCC_BA30RPostCG_BA2--0.09
      RThalLINS_BA13--0.12
      LpostCG_BA2LINS_BA13+0.08
      LpostCG_BA2LCun_BA18+0.10
      LpostCG_BA2LMOG_BA19+0.18
      LpostCG_BA2RPCC_BA30+0.10
      LpostCG_BA2RThal+0.08
      RPostCG_BA2LINS_BA13+0.10
      LSPL_BA7LINS_BA13+0.13
      LINS_BA13LINS_BA13+0.10
      RPreCun_BA19RPostCG_BA2+0.11
      RThalLpostCG_BA2+0.12
      RThalRPostCG_BA2+0.14
      RThalLSPL_BA7+0.09
      RThalLCun_BA18+0.24
      RThalLMOG_BA19+0.28
      ExcitationRPostCG_BA2RPostCG_BA2--0.14
      RPostCG_BA2LMOG_BA19--0.09
      LINS_BA13LpostCG_BA2--0.11
      LINS_BA13RPostCG_BA2--0.23
      LINS_BA13LCun_BA18--0.16
      LINS_BA13RPCC_BA30--0.12
      LCun_BA18RPreCun_BA19--0.08
      LMOG_BA19LpostCG_BA2--0.09
      LMOG_BA19LINS_BA13--0.26
      LMOG_BA19RThal--0.08
      LpostCG_BA2RPreCun_BA19+0.12
      RThalRPCC_BA30+0.08

      Association of effective connectivity with mood variability

      The results of the relationship between mood variability and effective connectivity within each network in the patient group are shown in Figure 4. Positive values (green) indicate a positive relationship between the effective connectivity and mood variability, while negative values (red) represent a negative association. The direction of effective connectivity (excitatory/inhibitory) between two regions is reported in Table 5.
      Figure thumbnail gr4
      Figure 4Spectral dynamic causal modeling results of the patient group. Effective connectivity of mood variability within each network. Green/red colors indicate a positive/negative relationship with mood variability. Effect sizes that survived a 95% posterior confidence criterion or more are shown in color. The connections with a group difference (patients > controls) are highlighted in dashed boxes. Number of incoming (target) and outgoing (source) connections are shown in the grey bar graphs along the sides. The matrix can be interpreted as effective connectivity from column (source) to row (target).
      Table 5Effective connectivity of emotional variability (MV)
      Network 1ConnectivityRelation with MVEffect size in Hz
      SourceTarget
      InhibitionLMFG_BA6LIPL_BA40--0.20
      RMFG_BA8LMFG_BA10--0.20
      LSFG_BA8RMFG_BA11+0.10
      LSFG_BA8LMFG_BA10+0.11
      RMFG_BA11RIPL_BA40+0.14
      LMFG_BA6LSFG_BA8+0.20
      RIPL_BA40LSFG_BA8--0.14
      RIPL_BA40RINS_BA13--0.11
      LIPL_BA40LMFG_BA6--0.11
      LIPL_BA40LMFG_BA10+0.18
      RINS_BA13LIPL_BA40--0.30
      RINS_BA13LMFG_BA6--0.12
      RINS_BA13RMFG_BA8+0.12
      RACC_BA23RIPL_BA40+0.15
      ExcitationLSFG_BA8RACC_BA23--0.16
      LMFG_BA10RMFG_BA11--0.16
      LSFG_BA8LMFG_BA6--0.14
      LMFG_BA6RMFG_BA11--0.11
      LSFG_BA8RPreCun_BA7--0.11
      RMFG_BA8RINS_BA13--0.11
      LMFG_BA10RMFG_BA8+0.09
      RMFG_BA11LMFG_BA10+0.12
      LMFG_BA10LIPL_BA40+0.13
      RMFG_BA8RIPL_BA40+0.21
      RIPL_BA40LIPL_BA40--0.16
      RPreCun_BA7RACC_BA23--0.15
      RIPL_BA40RMFG_BA11--0.10
      RACC_BA23RPreCun_BA7+0.20
      Network 2
      InhibitionLSFG_BA9LIFG_BA47--0.39
      LIFG_BA47LSFG_BA9+0.15
      LMFG_BA6LSFG+0.18
      LMFG_BA6RIFG_BA47+0.21
      LSFG_BA9LSTG_BA39+0.24
      LSFGLMTG+0.42
      LSTG_BA39LIFG_BA47--0.29
      LSTG_BA39LSFG--0.20
      LCaudateLIFG_BA47--0.21
      LCaudateLSTG_BA39+0.18
      LCaudateLMFG_BA6+0.23
      ExcitationLMFG_BA6LSTG_BA39--0.23
      LIFG_BA47RIFG_BA47--0.17
      LIFG_BA47LMTG--0.16
      LIFG_BA47LSFG--0.16
      LSFGLCaudate--0.11
      RIFG_BA47LMTG+0.12
      LSFGLSTG_BA39+0.13
      LSTG_BA39LSFG_BA9--0.25
      LMTGLMFG_BA6+0.14
      Network 3
      InhibitionMedFG_BA10RThal--0.15
      LIOG_BA19RFFA_BA37--0.16
      RThalLAMY--0.15
      RThalRFFA_BA37--0.13
      RThalLPHG_BA27--0.11
      RThalMedFG_BA10+0.27
      LAMYLPHG_BA27--0.19
      LPHG_BA27MedFG_BA10--0.15
      LPHG_BA27LAMY+0.17
      LPHG_BA27RThal+0.18
      LPHG_BA27RFFA_BA37+0.25
      RAMYLPHG_BA27+0.25
      RAMYRThal+0.27
      ExcitationMedFG_BA10RAMY--0.13
      LFFA_BA37LPHG_BA27--0.17
      LIOG_BA19LFFA_BA37--0.17
      LFFA_BA37RFFA_BA37+0.15
      RFFA_BA37RThal+0.22
      Network 4
      InhibitionLSPL_BA7RPCC_BA30--0.15
      LSPL_BA7RThal--0.13
      RPostCG_BA2LCun_BA18+0.12
      RPostCG_BA2RThal+0.17
      RPostCG_BA2LMOG_BA19+0.20
      LCun_BA18LSPL_BA7+0.20
      LCun_BA18RPreCun_BA19+0.22
      RThalLINS_BA13--0.16
      RThalRPreCun_BA19+0.11
      RThalLSPL_BA7+0.12
      RPCC_BA30RPostCG_BA2--0.20
      LINS_BA13LMOG_BA19--0.19
      LINS_BA13RPCC_BA30--0.12
      ExcitationRPreCun_BA19RThal--0.19
      RPostCG_BA2LPostCG_BA2--0.18
      RPreCun_BA19RPostCG_BA2--0.16
      LPreCun_BA19RPostCG_BA2--0.15
      LSPL_BA7RPreCun_BA19--0.13
      LPostCG_BA2LCun_BA18--0.12
      LMOG_BA19LCun_BA18--0.10
      RPostCG_BA2RPCC_BA30+0.10
      LPostCG_BA2RPostCG_BA2+0.15
      RPCC_BA30LPostCG_BA2--0.18
      RPCC_BA30RPreCun_BA19--0.16
      RPCC_BA30RThal+0.15
      Within N1, 50% inhibitory and 50% excitatory connections were positively or negatively related to mood variability. In N2, a similar pattern of results was observed with 55% inhibitory and 45% excitatory connections that were positively or negatively associated with mood variability. N3 demonstrated more inhibitory (72%) than excitatory (28%) connections to be linked to mood variability. In N4, 52% of inhibitory and 48% of excitatory connections were modulated by mood variability. Regarding the direction of the association between mood variability and effective connectivity, positive and negative relationships were more or less balanced across all connections within each network.
      In terms of interconnectivity, we found that in N1 32% of all connections showing an association with mood variability project from frontal regions to frontal regions. In N2 frontal regions mainly target frontal (30%) and temporal (30%) regions. In N3 and N4 the pattern of interconnectivity was less pronounced with 16% of the connections projecting from limbic to limbic regions and in N3 24% of the connections coupling parietal with parietal regions in N4.
      In all networks, connections were determined that showed a reliable group difference (between SUD and controls) and a reliable relationship with mood variability in the SUD group (indicated in rectangles in Figure 4). In N1 and N2, 32% and 35% of all connections, respectively, demonstrated a reliable group difference and association with mood variability. In N3 and N4, 28% and 32% of all connections, respectively, differed between groups and were related to mood variability.
      Supplementary analyses investigating mood variability – while controlling for the only significant comorbid diagnosis (lifetime depression) that demonstrated a positive relationship between mood variability and craving – revealed near-identical results (Supplemental Figure S4, Table S1 & S3). Controlling for substance type and lifetime depression did not alter the previously reported findings (Supplemental Figure S5, Table S2 & S4).

      Leave-one-out cross-validation

      Results of the LOOCV are shown in Figure 5 and reported in Table 6. LOOCV revealed that effect sizes were large enough to predict mood variability with an out-of-sample estimate for four connections in N1, seven connections in N2, four connections in N3, and seven connections in N4. This analysis indicates which specific connections within each network could significantly predict mood variability across SUD patients.
      Figure thumbnail gr5
      Figure 5Leave-one-out cross-validation results for each network (N1 to N4) in patients. Only significant effect sizes that were large enough to predict mood variability with an out-of-sample estimate are illustrated (p<0.05). Green/red arrows indicate a positive/negative relationship with mood variability in patients within each network. The thickness of arrows indicates positive/negative effective connectivity. The size of ROIs indicates the number of inputs/outputs. ROIs are color-coded for various brain regions.
      Table 6Results of Leave One Out Cross-Validation (LOOCV) analyses
      SourceTargetr-valuep-value
      N1LMFG_BA6LIPL_BA400.220.05
      RINS_BA13LIPL_BA400.38<0.001
      RACC_BA23RPreCun_BA70.280.02
      RACC_BA23RIPL_BA400.290.02
      N2LMFG_BA6LSTG_BA390.270.02
      LSFGLMTG0.36<0.001
      LSFG_BA9LIFG_BA470.330.01
      LSFG_BA9LSTG_BA390.310.01
      LIFG_BA47LSFG0.250.04
      LSTG_BA39LIFG_BA470.270.03
      N3MedFG_BA10RAMY0.330.01
      RFFA_BA37RThal0.310.01
      LPHG_BA27RFFA_BA370.260.03
      RAMYLPHG_BA270.230.05
      N4RPostCG_BA2LCun_BA180.240.04
      RPostCG_BA2LMOG_BA190.300.01
      LCun_BA18LSPL_BA70.280.02
      RPCC_BA30LpostCG_BA20.300.01
      RPCC_BA30RPostCG_BA20.35<0.001
      LCun_BA18RPreCun_BA190.230.05

      Discussion

      SUD is often experienced as a chronic, lifelong disorder due to lasting alterations in brain functioning induced by substance use as well as to deleterious effects of comorbid mood disorders (
      • Volkow N.D.
      • Michaelides M.
      • Baler R.
      The Neuroscience of Drug Reward and Addiction.
      ). One common bridge between both risk factors may be increased emotional variability, that is observed in anxiety and mood disorders (
      • Lamers F.
      • Swendsen J.
      • Cui L.
      • Husky M.
      • Johns J.
      • Zipunnikov V.
      • Merikangas K.R.
      Mood reactivity and affective dynamics in mood and anxiety disorders.
      ) and that may have transdiagnostic significance. Using ecological momentary assessment, mood variability (but not mood tonality or severity) was found to be associated with increased craving in individuals with SUD. These findings were significant regardless of comorbid diagnosis or type of SUD, but they were of greater magnitude among persons with a history of depression. Taken together, these findings provide novel insights into the mechanisms that maintain craving and that may therefore contribute to the global burden of SUD (
      • Bardach A.E.
      • Alcaraz A.O.
      • Ciapponi A.
      • Garay O.U.
      • Riviere A.P.
      • Palacios A.
      • et al.
      Alcohol consumption’s attributable disease burden and cost-effectiveness of targeted public health interventions: a systematic review of mathematical models.
      ,
      • Degenhardt L.
      • Charlson F.
      • Ferrari A.
      • Santomauro D.
      • Erskine H.
      • Mantilla-Herrara A.
      • et al.
      The global burden of disease attributable to alcohol and drug use in 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.
      ).
      To our knowledge, the present findings are also the first to demonstrate the underlying causal network dynamics associated with mood variability in the daily lives of individuals with SUD. Overall, effective connectivity between groups was decreased for patients in the prefrontal control networks and increased in the emotion generation networks compared to healthy controls. The findings further revealed that effective connectivity within the patient group was modulated by mood variability and that a subset of these connectivity parameters were significant predictors of mood variability. However, contrary to our hypothesis, no specific direction of association was observed between effective connectivity and mood variability within the networks. Our findings therefore support the notion that mood variability and craving in persons with SUD are linked to the dynamic network configurations within prefrontal control and subcortical emotion-generating networks at rest.
      Patients with SUD compared to controls showed a pattern of more reduced connectivity in the prefrontal control networks (N1 and N2). This is generally consistent with previous work (
      • Wilcox C.E.
      • Pommy J.M.
      • Adinoff B.
      Neural Circuitry of Impaired Emotion Regulation in Substance Use Disorders.
      ,
      • Wilcox C.E.
      • Abbott C.C.
      • Calhoun V.D.
      Alterations in resting-state functional connectivity in substance use disorders and treatment implications.
      ), which showed that reduced connectivity within the ECN (centered on nodes in the dorsolateral/ventrolateral prefrontal cortex and the lateral posterior parietal cortex) relates to impaired functioning in cognitive control. The cause of this decreased connectivity within regulatory networks in SUD has been proposed to be impairment in white matter integrity (
      • Harris G.J.
      • Jaffin S.K.
      • Hodge S.M.
      • Kennedy D.
      • Caviness V.S.
      • Marinkovic K.
      • et al.
      Frontal white matter and cingulum diffusion tensor imaging deficits in alcoholism.
      ). In N2, the reward-related regions such as the striatum (i.e. caudate) also demonstrated more reduced connectivity with prefrontal regions in line with previous studies (
      • Wilcox C.E.
      • Pommy J.M.
      • Adinoff B.
      Neural Circuitry of Impaired Emotion Regulation in Substance Use Disorders.
      ). In the emotion generation and interoceptive networks (N3 and N4), no clear pattern of change emerged in connectivity strength. Interestingly, the effect of group differences was more pronounced for the inhibitory coupling in N2 and N4. This finding might indicate weak communication from interoceptive regions such as the insula involved in the monitoring of internal bodily states to achieve or maintain homeostasis (
      • Craig A.D.
      Interoception : the sense of the physiological condition of the body.
      ,
      • Craig A.D.
      How do you feel? Interoception: the sense of the physiological condition of the body.
      ) with the prefrontal cortex, resulting in emotion regulation disturbances (
      • Sutherland M.T.
      • McHugh M.J.
      • Pariyadath V.
      • Stein E.A.
      Resting state functional connectivity in addiction: Lessons learned and a road ahead.
      ).
      The intrinsic causal dynamics in large-scale neural networks implicated in emotion generation and regulation play a predictive role in the patients’ susceptibility to experiencing mood variability (and subsequently, craving) in daily life. Patients with high mood variability showed a pattern of equally inhibitory and excitatory connectivity within cognitive control (N1 and N2) and interoceptive (N4) networks. The higher interconnectivity between frontal regions in the control networks linked to mood variability may represent an altered ability to down-regulate emotions adaptively, including processes such as directing attention away from the emotional stimulus, maintaining the goal of the regulation strategy, selecting goal-appropriate strategies, and reinterpreting the meaning of the emotional stimulus/situation, thereby contributing to the manifestation of symptoms of mood variability in SUD.
      Of particular interest in N2 are the effective connections of the striatum, a region which plays a key role in reward processing and addictive behavior. Bidirectional intrinsic changes of connection strength related to mood variability between the striatum and frontal/temporal regions were found. High mood variability was related to a decrease in connectivity from the frontal cortex to the striatum and an increase in connectivity from the striatum to the frontal and temporal cortex. These results expand on previous findings that linked emotion regulation, abstinence, craving and subjective withdrawal to elevated connectivity between the ECN and reward network (
      • Wilcox C.E.
      • Abbott C.C.
      • Calhoun V.D.
      Alterations in resting-state functional connectivity in substance use disorders and treatment implications.
      ).
      Within the emotion generating network (N3), mood variability was linked to greater inhibitory connectivity, mainly originating from subcortical and limbic regions. This indicates that mood variability seems to modulate bottom-up, context-dependent signals important for emotional reactivity, the generation of emotional responses and the reactivation of autobiographic memory processes. Of note, patients with high mood variability also showed reduced excitatory connectivity from regions involved in valuation (i.e. medFG) (
      • Hare T.
      • Camerer C.F.
      • Rangel A.
      Self-control in decision-making involves modulation of the vmPFC valuation system.
      ,
      • Bartra O.
      • McGuire J.T.
      • Kable J.W.
      The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value.
      ) to the amygdala, indicating impaired modulation of emotion generating/processing regions (
      • Wilcox C.E.
      • Pommy J.M.
      • Adinoff B.
      Neural Circuitry of Impaired Emotion Regulation in Substance Use Disorders.
      ).
      N4 has been involved in emotion regulation and generating/processing, thus taking on an intermediate integrative role between the other three networks (
      • Morawetz C.
      • Riedel M.C.
      • Salo T.
      • Berboth S.
      • Eickhoff S.B.
      • Laird A.R.
      • Kohn N.
      Multiple large-scale neural networks underlying emotion regulation.
      ). Among all four networks, N4 is the least clear in terms of characteristic network dynamics (i.e. patterns of directionality). This aligns well with the notion that interoception in addiction (
      • Verdejo-Garcia A.
      • Clark L.
      • Dunn B.D.
      The role of interoception in addiction: a critical review.
      ,
      • Paulus M.P.
      • Stewart J.L.
      Interoception and drug addiction.
      ) influences moment-to-moment information processing by identifying the most subjectively relevant stimuli that could arise from internal or external sources, thereby implementing a network-switching function between the DMN and ECN (

      Sutherland MT, McHugh MJ, Pariyadath V, Stein EA (2012, October 1): Resting state functional connectivity in addiction: Lessons learned and a road ahead. NeuroImage, vol. 62. NIH Public Access, pp 2281–2295.

      ).
      The LOOCV results highlight the possibility that resting-state connectivity within all four networks might be particularly useful in predicting mood variability in daily life. To further support the clinical utility and predictive value of these connectivity alterations, it was important to consider whether the effects observed here were truly predictive of mood variability (or due to comorbid diagnoses or substance type), and that they constitute generalizable markers. Indeed, our findings did not differ when including lifetime depression and substance use disorder type as control variables in our analysis, thus suggesting that differences in connectivity associated with mood variability are independent of these factors.
      Several limitations of this investigation should be considered in interpreting the observed findings. First, as this represents the first study testing causal network dynamics in SUD in relation to mood variability, we did not differentiate between the different types of substances (i.e. cannabis, tobacco or alcohol) in the overall analyses and power considerations would not permit substance-specific analyses. However, the lack of significant alterations in the findings when controlling for substance type indicates that the magnitude of such differences is unlikely to be moderate or large. Related to this issue, it has to be noted that the findings might be limited in generalizability as a specific set of drugs has been examined, disregarding other drugs such as opioids. Second, we did not control for withdrawal states or duration of abstinence which might impact changes in resting-state connectivity (
      • Wilcox C.E.
      • Abbott C.C.
      • Calhoun V.D.
      Alterations in resting-state functional connectivity in substance use disorders and treatment implications.
      ). Finally, the interpretation of the mood variability variable is limited in several aspects (
      • de Haan-Rietdijk S.
      • Kuppens P.
      • Hamaker E.L.
      What’s in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data.
      )as it is not sensitive to temporal order (
      • Larsen R.J.
      The Stability of Mood Variability: A Spectral Analytic Approach to Daily Mood Assessments.
      ), partly confounded by mean levels (
      • Mestdagh M.
      • Pe M.
      • Pestman W.
      • Verdonck S.
      • Kuppens P.
      • Tuerlinckx F.
      Sidelining the mean: The relative variability index as a generic mean-corrected variability measure for bounded variables.
      ) and could be driven by multiple underlying psychological processes (
      • Vanhasbroeck N.
      • Ariens S.
      • Tuerlinckx F.
      • Loossens T.
      Computational Models for Affect Dynamics.
      ,
      • Wang L.P.
      • Hamaker E.
      • Bergeman C.S.
      Investigating inter-individual differences in short-term intra-individual variability.
      ). Future studies are needed to implement a relative variability index to model mood variability and replicate the current findings(
      • Mestdagh M.
      • Pe M.
      • Pestman W.
      • Verdonck S.
      • Kuppens P.
      • Tuerlinckx F.
      Sidelining the mean: The relative variability index as a generic mean-corrected variability measure for bounded variables.
      ).
      Large-scale resting-state DCM revealed systematic causal network dynamics in relation to mood variability among persons with SUD. As such, our study provides novel insights into how mood variability - as it naturally occurs in daily life - is associated with (and potentially predicted by) directional connectivity of emotion generative and regulatory brain networks. Patients with SUD compared to controls demonstrated differences in within-network effective connectivity and these alterations were related to mood variability. Within each network, we determined connections that appear to predict mood variability in patients. Determining the causal nature of network dynamics underlying emotion regulation is an important step forward to informing intervention research through biomarkers of SUD risk factors.

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      ,
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      Acknowledgements

      Agence Nationale de la Recherche (ANR-SH2-0012) and the Fondation pour la Recherche Médicale (DPA20140629807).
      Disclosures
      The authors report no biomedical financial interests or potential conflicts of interest.

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