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Neuroanatomical correlates of emotion-related impulsivity

  • Author Footnotes
    ∗ M.V.E. and S.A.S.E contributed equally as co-first authors.
    Matthew V. Elliott
    Correspondence
    Corresponding Author: Matthew V. Elliott, Berkeley Way West, 2121 Berkeley Way, 2160, Berkeley, CA 94720
    Footnotes
    ∗ M.V.E. and S.A.S.E contributed equally as co-first authors.
    Affiliations
    Department of Psychology, University of California at Berkeley, Berkeley, CA, USA
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  • Author Footnotes
    ∗ M.V.E. and S.A.S.E contributed equally as co-first authors.
    Serajh A.S. Esmail
    Footnotes
    ∗ M.V.E. and S.A.S.E contributed equally as co-first authors.
    Affiliations
    Department of Psychology, University of California at Berkeley, Berkeley, CA, USA
    Search for articles by this author
  • Kevin S. Weiner
    Affiliations
    Department of Psychology, University of California at Berkeley, Berkeley, CA, USA

    Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, CA, USA
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  • Sheri L. Johnson
    Affiliations
    Department of Psychology, University of California at Berkeley, Berkeley, CA, USA
    Search for articles by this author
  • Author Footnotes
    ∗ M.V.E. and S.A.S.E contributed equally as co-first authors.
Open AccessPublished:August 04, 2022DOI:https://doi.org/10.1016/j.biopsych.2022.07.018

      Abstract

      Background

      Emotion-related impulsivity (ERI) refers to chronically poor self-control during periods of strong emotion. ERI robustly predicts psychiatric disorders and related problems, yet its neuroanatomical correlates are largely unknown. We tested whether local brain morphometry in targeted brain regions that integrate emotion and control could explain ERI severity.

      Methods

      122 adults (aged 18-55) with internalizing or externalizing psychopathology completed a structural MRI scan, the Three Factor Impulsivity Index (TFII), and the Structured Clinical Interview for the DSM-5. The TFII measured two types of ERI and a third type of impulsivity not linked to emotion. Cortical reconstruction yielded cortical thickness and local gyrification measurements. We evaluated whether morphometry in the orbitofrontal cortex, insula, amygdala, and nucleus accumbens was associated with ERI severity. Hypotheses and analyses were pre-registered.

      Results

      Lower cortical gyrification in the right lateral orbitofrontal cortex (OFC) was associated with high ERI severity in a full, pre-registered model. Separate examinations of local gyrification and cortical thickness also showed a positive association between gyrification in left lateral OFC and ERI. An integrated measure of hemispheric imbalance in lateral OFC gyrification (right < left) correlated with ERI severity. These findings were specific to ERI and did not appear with non-emotion-related impulsivity.

      Conclusions

      Local gyrification in the lateral OFC is associated with ERI severity. The current findings fit with theory of OFC function, strengthen the connections between the transdiagnostic literatures in psychiatry and neuroscience, and may guide future treatment development.

      Keywords

      Introduction

      For centuries, religion, philosophy, and science have debated the etiology of impulsivity. All humans behave impulsively, but some more than others. Impulsivity, though, is not a unitary construct, but rather several separable dimensions(
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      We consider four factors likely contributing to the inconsistent cross-study results regarding the neuroanatomical profile of ERI. First, most studies have taken an exploratory, whole-brain approach, which may diminish statistical power and increase the risk of false positive findings. Second, most studies have been limited by small sample sizes (n < 50), with only three studies including more than 100 participants(
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      ), did not provide clarity regarding the discrepancies previously observed in this literature. Third, structural MRI studies that did not include participants with mental health concerns may not represent those with severe ERI – null neurocognitive results have been more common in studies of nonclinical as compared to clinical samples(
      • Johnson S.L.
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      Impulsive Responses to Positive and Negative Emotions: Parallel Neurocognitive Correlates and Their Implications [no. 4].
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      ). As ERI is elevated transdiagnostically, the use of transdiagnostic samples might yield more robust findings and guard against third variable confounds specific to any one diagnosis. Fourth, although researchers have primarily focused on volume-based anatomical features, surface-based features, such as cortical thickness (CT) and local gyrification index (LGI), have enhanced our understanding of other brain-behavior links(
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      ). For example, LGI, which has not been used to study ERI, is a critical marker of cortical development(
      • White T.
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      ). Beyond the need to address inconsistencies in findings, recent work identifies a second form of ERI, Pervasive Influence of Feelings (PIF), with differentiable and robust impacts on mental health outcomes(
      • Johnson S.L.
      • Tharp J.A.
      • Peckham A.D.
      • Carver C.S.
      • Haase C.M.
      A path model of different forms of impulsivity with externalizing and internalizing psychopathology: Towards greater specificity.
      ,
      • Carver C.S.
      • Johnson S.L.
      • Joormann J.
      Major depressive disorder and impulsive reactivity to emotion: Toward a dual-process view of depression.
      ), but neuroimaging studies have considered only Feelings Trigger Action (FTA) (i.e., Urgency) and not PIF.
      The current study addressed each of these critical factors. To do so, we recruited a transdiagnostic sample with a broad range of internalizing and externalizing psychopathologies. We conducted pre-registered, theory-driven analyses focused on specific brain regions and promising surface-based brain measures. We investigated PIF in a neuroimaging study for the first time. We hypothesized that the strongest morphological correlates of ERI would be in the orbitofrontal cortex (OFC). We hypothesized that OFC morphology would not relate to a non-emotion-related facet of impulsivity used as a control comparison.

      Methods and Materials

      Participants

      130 adults aged 18-55 (M = 28) participated in a parent study approved by the UC Berkeley Committee for the Protection of Human Subjects. Individuals in the general community experiencing impairment from a wide range of internalizing and externalizing mental health symptoms (Sheehan Disability Scale(
      • Leon A.C.
      • Olfson M.
      • Portera L.
      • Farber L.
      • Sheehan D.V.
      Assessing Psychiatric Impairment in Primary Care with the Sheehan Disability Scale.
      ) > 5 in at least one life setting) were recruited through flyers, online advertising, and referrals from clinicians. Individuals with a history of bipolar disorder or primary psychosis, or with current alcohol or substance use disorders (as assessed by the SCID for DSM-5(43)) were excluded. Other exclusionary criteria included daily use of marijuana or sedating medications (including antipsychotics), lifetime head trauma resulting in loss of consciousness for 5 or more minutes, diminished cognitive abilities (Orientation Memory Concentration Test score < 7 of 12 (
      • Katzman R.
      • Brown T.
      • Fuld P.
      • Peck A.
      • Schechter R.
      • Schiimmel H.
      Validation of a short Orientation-Memory-Concentration Test of cognitive impairment.
      )), MRI safety contraindications (e.g. ferrous metal in body, pregnancy, seizure disorders), neurological disorders, or inability to independently complete cognitive measures due to intellectual or language problems.
      Participants that met these criteria were invited to the university to complete diagnostic, behavioral, and neuroimaging sessions. All participants completed informed consent procedures and urine toxicology screens to exclude those with recent consumption of drugs of abuse before scanning. Of the 130 participants that completed structural MRI scans, eight were removed after visual inspection revealed artifacts in their reconstructed cortical surfaces (N = 122). Table 1 describes the sample demographic and clinical characteristics, and descriptive statistics for the impulsivity measure.
      Table 1Participant Characteristics
      Characteristicn%MSDRange
      Gender
       Female8166.4
       Male3427.9
       Non-Binary64.9
       Declined to respond10.8
      Race
       Asian/Asian American3528.7
       Black/African American86.6
       More than one race2218.0
       White/European American5141.8
       Declined to respond64.9
      Ethnicity
       Hispanic or Latina/o2318.9
       Not Hispanic or Latina/o9981.1
      Age28.08.618-55
      Years of Education15.52.312-21
      SCID-5 Lifetime Diagnosis
       Major Depressive Disorder9981.1
       Anxiety Disorder8267.2
       Alcohol Use Disorder.2722.1
       Substance Use Disorder2419.7
       More than one disorder8267.2
      Impulsivity Subtype
       Pervasive Influence of Feelings3.730.761.92-5.00
       Feelings Trigger Action2.860.751.23-4.94
       Lack of Follow Through3.150.801.00-4.80
      Note. n = 122.

      Measures and MRI Acquisition

      Three Factor Impulsivity IndexTrait impulsivity was measured using the well-validated Three Factor Impulsivity Index(
      • Carver C.S.
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      • Joormann J.
      • Kim Y.
      • Nam J.Y.
      Serotonin Transporter Polymorphism Interacts With Childhood Adversity to Predict Aspects of Impulsivity.
      ,
      • Johnson S.L.
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      • Peckham A.D.
      • Carver C.S.
      • Haase C.M.
      A path model of different forms of impulsivity with externalizing and internalizing psychopathology: Towards greater specificity.
      ), which is comprised of three factor-analytically based subscales: Feelings Trigger Action (FTA), Pervasive Influence of Feelings (PIF), and Lack of Follow Through (LFT). The first two indices measure impulsive responses to emotion, while the third is comprised of items reflecting impulsivity without reference to emotion. More specifically, FTA captures a pattern of overt, regrettable action or speech in response to emotion and is composed of items from the Negative Urgency scale(
      • Whiteside S.P.
      • Lynam D.R.
      The Five Factor Model and impulsivity: using a structural model of personality to understand impulsivity.
      ), Positive Urgency Measure(
      • Cyders M.A.
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      • Fischer S.
      • Annus A.M.
      • Peterson C.
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      ), and Reflexive Reactions to Feelings scale(
      • Carver C.S.
      • Johnson S.L.
      • Joormann J.
      • Kim Y.
      • Nam J.Y.
      Serotonin Transporter Polymorphism Interacts With Childhood Adversity to Predict Aspects of Impulsivity.
      ). PIF captures patterns of unconstrained cognitive and motivational responses and is composed of items from the Generalization(
      • Carver C.S.
      • Voie L.L.
      • Kuhl J.
      • Ganellen R.J.
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      ), Sadness Paralysis(
      • Carver C.S.
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      • Joormann J.
      • Kim Y.
      • Nam J.Y.
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      ), and Emotions Color Worldview(
      • Carver C.S.
      • Johnson S.L.
      • Joormann J.
      • Kim Y.
      • Nam J.Y.
      Serotonin Transporter Polymorphism Interacts With Childhood Adversity to Predict Aspects of Impulsivity.
      ) scales. LFT reflects impulsivity without reference to emotion and is composed of items from the Lack of Perseverance(
      • Whiteside S.P.
      • Lynam D.R.
      The Five Factor Model and impulsivity: using a structural model of personality to understand impulsivity.
      ) and Distractibility(
      • Carver C.S.
      • Johnson S.L.
      • Joormann J.
      • Kim Y.
      • Nam J.Y.
      Serotonin Transporter Polymorphism Interacts With Childhood Adversity to Predict Aspects of Impulsivity.
      ) scales. All three factors show strong internal consistency(
      • Carver C.S.
      • Johnson S.L.
      • Joormann J.
      • Kim Y.
      • Nam J.Y.
      Serotonin Transporter Polymorphism Interacts With Childhood Adversity to Predict Aspects of Impulsivity.
      ,
      • Johnson S.L.
      • Carver C.S.
      • Joormann J.
      Impulsive responses to emotion as a transdiagnostic vulnerability to internalizing and externalizing symptoms.
      ,
      • Auerbach R.P.
      • Stewart J.G.
      • Johnson S.L.
      Impulsivity and Suicidality in Adolescent Inpatients.
      ,
      • Pearlstein J.G.
      • Johnson S.L.
      • Modavi K.
      • Peckham A.D.
      • Carver C.S.
      Neurocognitive mechanisms of emotion-related impulsivity: The role of arousal.
      ). The two scales that reference emotion, FTA and PIF, consistently relate more strongly to measures of psychopathology than does LFT (
      • Johnson S.L.
      • Tharp J.A.
      • Peckham A.D.
      • Carver C.S.
      • Haase C.M.
      A path model of different forms of impulsivity with externalizing and internalizing psychopathology: Towards greater specificity.
      ,
      • Auerbach R.P.
      • Stewart J.G.
      • Johnson S.L.
      Impulsivity and Suicidality in Adolescent Inpatients.
      ,
      • Carver C.S.
      • Johnson S.L.
      • Joormann J.
      Major depressive disorder and impulsive reactivity to emotion: Toward a dual-process view of depression.
      ). Therefore, our hypotheses focus on the emotion-related impulsivity scales, with LFT used as a control comparison. Univariate impulsivity distributions are depicted in Supplementary Figure 1. Intercorrelations of the three factors ranged from r = 0.23 to 0.38, comparable to other published datasets (Supplementary Table 1) (
      • Carver C.S.
      • Johnson S.L.
      • Joormann J.
      • Kim Y.
      • Nam J.Y.
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      ,
      • Javelle F.
      • Wiegand M.
      • Joormann J.
      • Timpano K.R.
      • Zimmer P.
      • Johnson S.L.
      The German Three Factor Impulsivity Index: Confirmatory factor analysis and ties to demographic and health-related variables.
      ).
      Structured Clinical Interview for DSM-5 (SCID-5). The SCID-5 is a common semi-structured interview used to assess psychopathology (

      First MB (2015): Structured Clinical Interview for the DSM (SCID). In: Cautin RL, Lilienfeld SO, editors. The Encyclopedia of Clinical Psychology. Hoboken, NJ, USA: John Wiley & Sons, Inc., pp 1–6.

      ). Participants completed the SCID-5 in-person, or during the COVID-19 pandemic by Zoom interview. Interviewers were trained by the principal investigators, attained inter-rater reliability, and attended reliability meetings to guard against rater drift. The average inter-rater kappa was 0.82.
      Structural MRI Acquisition. Participants were scanned using a 3T Siemens TIM Trio MRI. Sagittal T1-weighted structural images were acquired using a 32-channel receiver head coil and a 6.1-minute magnetization‐prepared rapid gradient‐echo sequence (MPRAGE). This scan had parameters of TR= 1900ms, TE= 2.89ms, FOV= 256mm, Voxel size= 1mm isotropic voxels, PAT Mode= GRAPPA, PE= 2.
      MRI Data Processing. High resolution, T1-weighted MRI scans were processed using FreeSurfer’s “recon all” function (version 6.0)(
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      • Sereno M.I.
      Cortical Surface-Based Analysis.
      ). “Recon-all” is a built-in FreeSurfer function that converts high resolution 2D anatomical images into 3D inflated and pial cortical reconstructions. In addition to cortical thickness (CT) and subcortical volume, which are default outputs of “recon-all,” we calculated the Local Gyrification Index (LGI) metric, which quantifies the amount of cortex buried in sulci within specific regions of interest (ROI)(

      Schaer M, Cuadra MB, Schmansky N, Fischl B, Thiran J-P, Eliez S (2012): How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index. JoVE 3417.

      ). The ROIs in each hemisphere of each participant were then labeled using the automatic parcellation annotation file produced in the “recon-all” process(
      • Destrieux C.
      • Fischl B.
      • Dale A.
      • Halgren E.
      Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature.
      ).
      Region-of-Interest Analyses. Pre-registered ROIs included orbitofrontal cortex (OFC), insula, amygdala, and nucleus accumbens. These regions were selected because they are thought to be at the core of emotion generation – amygdala(
      • LeDoux J.
      The amygdala.
      ) and nucleus accumbens(
      • Floresco S.B.
      The Nucleus Accumbens: An Interface Between Cognition, Emotion, and Action.
      ) – or hubs at the intersection of emotion and cognitive control – OFC(25) and insula(
      • Craig A.D.
      How do you feel — now? The anterior insula and human awareness.
      ). These targeted brain regions have each correlated, albeit inconsistently, with ERI in functional MRI studies(
      • Johnson S.L.
      • Elliott M.V.
      • Carver C.S.
      Impulsive Responses to Positive and Negative Emotions: Parallel Neurocognitive Correlates and Their Implications [no. 4].
      ) and have been implicated in psychiatric disorders(
      • Hoptman M.J.
      • Antonius D.
      • Mauro C.J.
      • Parker E.M.
      • Javitt D.C.
      Cortical Thinning, Functional Connectivity, and Mood-Related Impulsivity in Schizophrenia: Relationship to Aggressive Attitudes and Behavior.
      ,
      • Bogerts B.
      • Lieberman J.A.
      • Ashtari M.
      • Bilder R.M.
      • Degreef G.
      • Lerner G.
      • et al.
      Hippocampus-amygdala volumes and psychopathology in chronic schizophrenia.
      ,
      • Shin L.M.
      Amygdala, Medial Prefrontal Cortex, and Hippocampal Function in PTSD.
      ,
      • Johnson S.L.
      • Mehta H.
      • Ketter T.A.
      • Gotlib I.H.
      • Knutson B.
      Neural responses to monetary incentives in bipolar disorder.
      ,
      • Bewernick B.H.
      • Hurlemann R.
      • Matusch A.
      • Kayser S.
      • Grubert C.
      • Hadrysiewicz B.
      • et al.
      Nucleus Accumbens Deep Brain Stimulation Decreases Ratings of Depression and Anxiety in Treatment-Resistant Depression.
      ,
      • Stein M.B.
      • Simmons A.N.
      • Feinstein J.S.
      • Paulus M.P.
      Increased Amygdala and Insula Activation During Emotion Processing in Anxiety-Prone Subjects.
      ,
      • Avery J.A.
      • Drevets W.C.
      • Moseman S.E.
      • Bodurka J.
      • Barcalow J.C.
      • Simmons W.K.
      Major Depressive Disorder Is Associated With Abnormal Interoceptive Activity and Functional Connectivity in the Insula.
      ,
      • Drevets W.C.
      Orbitofrontal Cortex Function and Structure in Depression.
      ). Using the Desikan-Killiany-Tourville (DKT) atlas(
      • Klein A.
      • Tourville J.
      101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol.
      ), we mapped three labels, “medialorbitofrontal,” “lateralorbitofrontal,” and “insula,” onto each hemisphere of each participant’s cortical reconstruction (Figure 1a). The -autorecon2 stage of “recon-all” automatically segments more than 40 subcortical structures in each hemisphere. We used the “amygdala” and “accumbens area” labels to designate the left and right amygdala and nucleus accumbens.
      Figure thumbnail gr1
      Fig. 1Local gyrification in lateral OFC differentially relates to emotion-related impulsivity. A, Anatomical regions were defined using the Desikan-Killiany-Tourville atlas(
      • Klein A.
      • Tourville J.
      101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol.
      ). Example reconstructed surfaces shown as inflated with the atlas outlined (top) and with natural folding (bottom). Cortical regions of interest included medial orbitofrontal cortex (OFC) (yellow), lateral OFC (blue), and insula (green). B, Three-dimensional scatter plots with ordinary least squares planes of best fit. Separate models were built using standardized local gyrification index (LGI) (blue) and cortical thickness (CT) (grey) of the right and left lateral OFC to test associations with Pervasive Influence of Feelings (PIF) severity. PIF severity correlated with low LGI in the right lateral OFC and high LGI in the left lateral OFC. PIF was unrelated to CT in lateral OFC
      For the cortical ROIs, we derived CT and LGI values. We used two FreeSurfer functions (mri_annotation2label and mris_anatomicalstats) to extract the CT of our cortical ROIs. These functions intersected the DKT label files, which had been fitted to each participant’s cortical reconstruction, with their corresponding thickness files produced by “recon-all.” The thickness files contained measurements of the distance between the pial surface and the white matter boundary at each vertex. Each measurement was normalized based on the participant-specific thickest point in the cortex.
      To calculate LGI, we included the -localGI flag within “recon-all.” The -localGI flag creates whole-cortex pial surface overlay files containing gyrification measurements, which reflect the degree of cortical folding. The FreeSurfer function “mri_segstats,” then calculated the LGI statistics for the lateral and medial orbitofrontal cortex and insula as defined by their participant-specific label files. The LGI of each region was calculated as a ratio of the amount of cortex buried in sulci to the amount visible on the surface(

      Schaer M, Cuadra MB, Schmansky N, Fischl B, Thiran J-P, Eliez S (2012): How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index. JoVE 3417.

      ).
      For subcortical ROIs, we measured structural volume. The FreeSurfer function “mri_segstats” calculated segmented volumes (mm3) of the “amygdala” and “accumbens area” labels in each hemisphere of each participant’s cortical reconstruction. Univariate distributions for all brain metrics are depicted in Supplementary Figures 2-4.
      We built multiple regression models to test the comparative ability of these structural brain measures to predict two forms of ERI (PIF and FTA), and as a comparison, LFT. We constructed six sets of analyses using R(64). The first two of these analyses were pre-registered. Beyond the standard alpha of 0.05, we included Bonferroni-corrected alpha levels based on the number of predictors, which if significant, would support our hypotheses.
      In the first analysis, all CT, LGI, and subcortical volume metrics were included as predictor variables. We hypothesized that the strongest associations between brain morphology and ERI would be in OFC. We expected that OFC morphology would not relate to LFT. We did not hypothesize whether our anatomical metrics would differentiate between FTA and PIF due to a lack of previous research. Although the phenotypes are distinct, FTA and PIF are moderately correlated and show some overlap in studies of psychopathology(
      • Johnson S.L.
      • Carver C.S.
      • Joormann J.
      Impulsive responses to emotion as a transdiagnostic vulnerability to internalizing and externalizing symptoms.
      ) and response inhibition(
      • Pearlstein J.G.
      • Johnson S.L.
      • Modavi K.
      • Peckham A.D.
      • Carver C.S.
      Neurocognitive mechanisms of emotion-related impulsivity: The role of arousal.
      ). Therefore, we expected partial concordance in their neuroanatomical correlates. The second analysis tested the addition of quadratic metrics to the primary models. We hypothesized that quadratic (i.e. curvilinear) terms would increase model fits given that the relationship between ERI and performance on cognitive control tasks has been shown to be curvilinear previously (
      • Johnson S.L.
      • Tharp J.A.
      • Peckham A.D.
      • Sanchez A.H.
      • Carver C.S.
      Positive Urgency Is Related to Difficulty Inhibiting Prepotent Responses [no. 5].
      ,
      • Dekker M.R.
      • Johnson S.L.
      Major Depressive Disorder and Emotion-Related Impulsivity: Are Both Related to Cognitive Inhibition? [no. 4].
      ). The third analysis tested the addition of age as a covariate to the primary models given its links to CT(66) and LGI(67). The fourth and fifth analyses isolated subsets of the linear predictor variables, LGI and CT metrics, respectively. For each model, right and left hemisphere metrics were included. Model coefficients were interpreted using null hypothesis significance testing, and we quantified prediction of impulsivity scores collectively across neuroanatomical metrics with adjusted R2 values of the models.
      The sixth analysis extended these regression analyses to test whether imbalanced LGI in left and right lateral orbitofrontal cortex correlated with ERI. We calculated each participant’s LGI “Laterality Ratio” in the lateral orbitofrontal cortex as defined by Hill and colleagues(
      • Hill S.Y.
      • Wang S.
      • Kostelnik B.
      • Carter H.
      • Holmes B.
      • McDermott M.
      • et al.
      Disruption of Orbitofrontal Cortex Laterality in Offspring from Multiplex Alcohol Dependence Families.
      ). The calculation was as follows: RightLeftRight+Left Positive values indicated higher LGI in the right hemisphere relative to LGI in the left hemisphere. We correlated the LGI laterality ratio in the lateral orbitofrontal cortex with each of the three impulsivity factors using Pearson’s r.
      To test the specificity of ERI results, we compared the differences in ERI and non-ERI effect size estimates using bootstrap resampling – 1,000 random samples with replacement. For each brain metric significantly associated with ERI, we estimated a 95% confidence interval from the distribution of bootstrapped effect size difference scores. We interpreted 95% confidence intervals that did not overlap with the null as evidence that brain morphology was significantly more related to ERI than non-ERI.
      Pre-registration document, data, and code are available at: https://osf.io/tfkpb

      Results

      OFC Morphology Relates to Both Forms of Emotion-Related Impulsivity

      Consistent with our hypothesis, anatomical features of the OFC predicted ERI. In the full model, lower LGI, but not CT, in the right lateral OFC related to higher PIF (β = -0.315, 95% C.I. [-0.601, -0.028], t(106) = -2.17, p = 0.032). Interestingly, this effect was localized to the lateral aspect of the OFC in the right hemisphere. This effect was significant at the standard, but not Bonferroni-corrected, threshold. The bootstrapped 95% confidence interval comparing the strength of effect in PIF vs. LFT (i.e. non-ERI) overlapped the null (-0.612, 0.011; Supplementary Figure 5a). All other structural brain variables did not significantly predict PIF, and the overall model including all variables showed weak fit (R2 = 0.11, F(16,106) = 0.79, p = 0.70). For FTA and LFT, the set of cortical and subcortical predictors were all nonsignificant (p > 0.05) with weak overall model fits (FTA: R2 = 0.094, F(16,106) = 0.69, p = 0.80; LFT: R2 = 0.086, F(16,106) = 0.62, p = 0.86) (Supplementary Table 2).
      Adding quadratic terms to test for curvilinear prediction did not improve model fits (Supplementary Table 3). Adding age as a covariate did not alter the above findings (Supplementary Table 4). Model diagnostics indicated that assumptions of residual normality and homoscedasticity were met. Because our pre-registered analyses revealed a very specific effect localized to one cortical region (lateral OFC) and one morphological feature (LGI), we did not conduct pre-registered feature selection procedures (i.e., LASSO regression).
      Consistent with previous work(
      • Gautam P.
      • Anstey K.J.
      • Wen W.
      • Sachdev P.S.
      • Cherbuin N.
      Cortical gyrification and its relationships with cortical volume, cortical thickness, and cognitive performance in healthy mid-life adults.
      ), CT and LGI were negatively correlated for five of the six hypothesized ROIs (Pearson’s r’s between -0.15 to -0.50, Supplementary Table 5). Given the potential for the CT and LGI coefficients to be biased by collinearity when examined conjointly, we constructed separate, post-hoc multiple regression models for CT and LGI. Lower LGI in the right lateral OFC and higher LGI in the left lateral OFC correlated with higher PIF (Right: β = -0.248, 95% C.I. [-0.494, -0.002], t(116) = -2.00, p =0.048; Left: β = 0.257, 95% C.I. [0.017, 0.497], t(116) = 2.12, p = 0.036; Figure 1b). Higher LGI in the left lateral OFC was also related to FTA severity (β = 0.254, 95% C.I. [0.012, 0.496], t(116) = 2.08, p = 0.040). Although these effects were significant at α = 0.05, they did not survive Bonferroni correction. A direct comparison suggested a significantly stronger effect for PIF over non-ERI in right lateral OFC (C.I.: -0.553, -0.052), but not for PIF and FTA in left lateral OFC (PIF C.I.: -0.077, 0.510; FTA C.I.: -0.105, 0.510; Supplementary Figure 5b-d). All other regressors were nonsignificant, and the models explained small proportions of the total variances in impulsivity (R2s < 0.07) (Supplementary Table 6). None of the cortical thickness regressors significantly related to the impulsivity measures (Supplementary Table 7).
      Because our results were strongest for PIF, and there have been no prior published studies of the neuroanatomical correlates of this form of ERI, we conducted an exploratory, whole-brain analysis for the purpose of generating future hypotheses. We used the Freesurfer group-level, general linear model analysis and regressed PIF onto LGI across all cortical vertices (see Supplementary Methods and Materials). In addition to confirming the importance of the OFC, this whole-brain group analysis identified temporal pole, lateral prefrontal cortex, frontal pole, and temporo-parietal junction as potential correlates of PIF at a less stringent (p < 0.05) vertex-wise cluster detection threshold (Supplementary Table 8a, Supplementary Figure 6a). The temporal pole and frontal pole clusters survived a more conservative (p < 0.001) vertex-wise cluster detection threshold (Supplementary Table 8b, Supplementary Figure 6b).

      Imbalance in Hemispheric Orbitofrontal Gyrification Correlates with PIF

      The opposing directionality of the lateral OFC coefficients in the right and left hemispheres fit with work by Hill and colleagues who found that smaller right OFC volume relative to left OFC volume predicted higher general impulsivity scores(
      • Hill S.Y.
      • Wang S.
      • Kostelnik B.
      • Carter H.
      • Holmes B.
      • McDermott M.
      • et al.
      Disruption of Orbitofrontal Cortex Laterality in Offspring from Multiplex Alcohol Dependence Families.
      ). Using their laterality ratio calculation – positive ratios indicated higher LGI in the right lateral OFC relative to the left – we correlated LGI laterality with the three impulsivity factors. LGI laterality was negatively correlated with PIF scores (r = -0.216, 95% C.I. [-0.379, -0.040], t(120) = -2.42, p = 0.017; Figure 2a), such that lower right as compared to left hemisphere LGI in the lateral OFC related significantly to PIF, at standard and Bonferroni-corrected thresholds. The correlation of LGI laterality was significantly stronger for PIF as compared with non-ERI (C.I.: -0.408, -0.009; Figure 2b). Findings for FTA showed a nonsignificant trend in the same direction as PIF (r = -0.153, 95% C.I. -0.322, 0.025, t(120) = -1.70, p = 0.091). LGI laterality in the lateral OFC was not significantly related to LFT (r = -0.017, 95% C.I. [-0.194, 0.162], t(120) = -0.18, p = 0.856).
      Figure thumbnail gr2
      Fig. 2Imbalanced gyrification between left and right lateral OFC relates to PIF severity. A, Participants with a low LGI hemispheric ratio in the lateral OFC – less gyrification in the right hemisphere compared to the left – were more likely on average to have higher PIF severity. Gray shading represents the 95% confidence interval around the line of best fit. *p < 0.05. B, The correlation between LGI hemispheric ratio and PIF was stronger than a control comparison correlation LFT (non-ERI). Vertical dashed lines illustrate the 95% confidence interval of the difference in the PIF and LFT effect sizes (Pearson’s r) from bootstrap resampling, 95% C.I. = [-0.408, -0.009].

      Discussion

      Hundreds of published studies have established ERI as a robust correlate of psychopathology, including aggression(
      • Johnson S.L.
      • Carver C.S.
      Emotion-relevant impulsivity predicts sustained anger and aggression after remission in bipolar I disorder.
      ), substance use disorders(
      • Settles R.E.
      • Fischer S.
      • Cyders M.A.
      • Combs J.L.
      • Gunn R.L.
      • Smith G.T.
      Negative urgency: A personality predictor of externalizing behavior characterized by neuroticism, low conscientiousness, and disagreeableness.
      ), depression(
      • Smith G.T.
      • Guller L.
      • Zapolski T.C.B.
      A Comparison of Two Models of Urgency: Urgency Predicts Both Rash Action and Depression in Youth.
      ), self-harm(
      • Peterson C.M.
      • Fischer S.
      A prospective study of the influence of the UPPS model of impulsivity on the co-occurrence of bulimic symptoms and non-suicidal self-injury.
      ,
      • Allen K.J.D.
      • Hooley J.M.
      Negative Emotional Action Termination (NEAT): Support for a Cognitive Mechanism Underlying Negative Urgency in Nonsuicidal Self-Injury.
      ), and suicide(
      • Auerbach R.P.
      • Stewart J.G.
      • Johnson S.L.
      Impulsivity and Suicidality in Adolescent Inpatients.
      ), yet its relationship to brain morphometry is not well understood. To address gaps in the literature that may have contributed to past inconsistencies we (i) used a targeted and pre-registered region-of-interest approach, (ii) recruited a sizable sample (N = 122), (iii) examined effects within a sample that included a broad range of internalizing and externalizing syndromes, and (iv) used surface-based cortical metrics – LGI and CT. This study was the first to investigate ERI in relation to LGI, and also the first to investigate the neuroanatomical correlates of PIF impulsivity. Together, the present findings provide an important step in clarifying the neuroanatomical correlates of ERI, a foundation that can be built upon in future studies.
      As hypothesized, of the brain regions we examined, the orbitofrontal cortex (OFC) was most strongly associated with ERI. In the right hemisphere, lower LGI in lateral OFC correlated with PIF. In the left hemisphere, higher LGI in lateral OFC correlated with both PIF and FTA severities. To our knowledge, this is the first neurobiological finding that explains some of the shared variance observed between these two ERI phenotypes. Regarding specificity, we found no significant associations between non-ERI and brain morphology in the areas we studied, and in direct comparison, local gyrification of right lateral OFC correlated with PIF more strongly than with non-ERI.
      Given the lack of prior research on PIF, we conducted an exploratory whole-brain analysis to aid future hypothesis generation. In addition to replicating the association between LGI in lateral OFC and PIF, we identified the temporal pole, frontal pole, dorsolateral prefrontal cortex, and temporo-parietal junction as candidates for future research on the neuroanatomical correlates of ERI. For example, as the right temporal pole is involved in emotion processing and regulation(
      • Sonkusare S.
      • Nguyen V.T.
      • Moran R.
      • van der Meer J.
      • Ren Y.
      • Koussis N.
      • et al.
      Intracranial-EEG evidence for medial temporal pole driving amygdala activity induced by multi-modal emotional stimuli.
      ,
      • Taylor M.J.
      • Robertson A.
      • Keller A.E.
      • Sato J.
      • Urbain C.
      • Pang E.W.
      Inhibition in the face of emotion: Characterization of the spatial-temporal dynamics that facilitate automatic emotion regulation.
      ), as well as has robust connectivity with the lateral OFC via the uncinate fasciculus(
      • Olson I.R.
      • Heide R.J.V.D.
      • Alm K.H.
      • Vyas G.
      Development of the uncinate fasciculus: Implications for theory and developmental disorders.
      ), a promising approach for future studies would be to examine how functional and anatomical features of lateral OFC and temporal pole together contribute to ERI.
      A laterality ratio, which integrated LGI in the right and left hemispheres, demonstrated that imbalanced cortical gyrification in the lateral OFC corresponded with higher PIF. Use of the laterality ratio appeared to increase the robustness and specificity of our findings in two main ways. First, the correlation of OFC laterality and PIF passed multiple comparison correction, whereas some separate OFC hemisphere coefficients were significant only at the standard alpha level. Second, LGI laterality was more strongly related to PIF than to non-ERI. This result extends the important findings of Hill and colleagues with increased neuroanatomical (i.e., lateral OFC) and psychological specificity (i.e., emotion-related impulsivity)(
      • Hill S.Y.
      • Wang S.
      • Kostelnik B.
      • Carter H.
      • Holmes B.
      • McDermott M.
      • et al.
      Disruption of Orbitofrontal Cortex Laterality in Offspring from Multiplex Alcohol Dependence Families.
      ). Our results were specific to LGI and did not extend to other morphological features, such as CT. The cluster of null effects across the other ROIs in our pre-registered analyses (medial OFC, insular cortex, amygdala, nucleus accumbens) highlights the unique importance of lateral OFC gyrification for understanding ERI.
      Although the OFC has been identified in only a minority of structural MRI studies of ERI (
      • Johnson S.L.
      • Elliott M.V.
      • Carver C.S.
      Impulsive Responses to Positive and Negative Emotions: Parallel Neurocognitive Correlates and Their Implications [no. 4].
      ), our findings fit with the broader literature on the OFC and human behavior beyond studies of ERI. For example, brain lesion studies have long shown that damage to the OFC can lead to deficits in emotional functioning(
      • Pujara M.S.
      • Philippi C.L.
      • Motzkin J.C.
      • Baskaya M.K.
      • Koenigs M.
      Ventromedial Prefrontal Cortex Damage Is Associated with Decreased Ventral Striatum Volume and Response to Reward.
      ,
      • Hornak J.
      Changes in emotion after circumscribed surgical lesions of the orbitofrontal and cingulate cortices.
      ,
      • Bramham J.
      • Morris R.G.
      • Hornak J.
      • Bullock P.
      • Polkey C.E.
      Social and emotional functioning following bilateral and unilateral neurosurgical prefrontal cortex lesions.
      ) and disinhibited behavior in the context of emotion(
      • Bechara A.
      • Tranel D.
      • Damasio H.
      Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions.
      ,
      • Rolls E.T.
      • Hornak J.
      • Wade D.
      • McGrath J.
      Emotion-related learning in patients with social and emotional changes associated with frontal lobe damage.
      ,
      • Viskontas I.V.
      • Possin K.L.
      • Miller B.L.
      Symptoms of Frontotemporal Dementia Provide Insights into Orbitofrontal Cortex Function and Social Behavior.
      ). Functional and structural MRI studies have demonstrated that the OFC is involved in value-based decision-making and emotion regulation(
      • Hiser J.
      • Koenigs M.
      The Multifaceted Role of the Ventromedial Prefrontal Cortex in Emotion, Decision Making, Social Cognition, and Psychopathology.
      ,
      • Spechler P.A.
      • Chaarani B.
      • Orr C.
      • Mackey S.
      • Higgins S.T.
      • Banaschewski T.
      • et al.
      Neuroimaging Evidence for Right Orbitofrontal Cortex Differences in Adolescents With Emotional and Behavioral Dysregulation.
      ), and one recent study found that direct electrical stimulation of lateral OFC attenuated depression symptoms(
      • Rao V.R.
      • Sellers K.K.
      • Wallace D.L.
      • Lee M.B.
      • Bijanzadeh M.
      • Sani O.G.
      • et al.
      Direct Electrical Stimulation of Lateral Orbitofrontal Cortex Acutely Improves Mood in Individuals with Symptoms of Depression.
      ). These studies do not directly test ERI, yet they describe its core characteristics in relation to the OFC. The current findings are also compatible with research linking inhibitory control to both ERI and right OFC (
      • Johnson S.L.
      • Tharp J.A.
      • Peckham A.D.
      • Sanchez A.H.
      • Carver C.S.
      Positive Urgency Is Related to Difficulty Inhibiting Prepotent Responses [no. 5].
      ,
      • Allen K.J.D.
      • Hooley J.M.
      Negative Emotional Action Termination (NEAT): Support for a Cognitive Mechanism Underlying Negative Urgency in Nonsuicidal Self-Injury.
      ,
      • Rudebeck P.H.
      • Rich E.L.
      Orbitofrontal cortex.
      ,
      • Schoenbaum G.
      • Roesch M.R.
      • Stalnaker T.A.
      • Takahashi Y.K.
      A new perspective on the role of the orbitofrontal cortex in adaptive behaviour.
      ). Furthermore, the association of lower LGI in right lateral OFC with higher ERI is consistent with previous work showing that lower LGI is associated with worse cognitive and behavioral functioning in other domains(
      • Mathias S.R.
      • Knowles E.E.M.
      • Mollon J.
      • Rodrigue A.
      • Koenis M.M.C.
      • Alexander-Bloch A.F.
      • et al.
      Minimal Relationship between Local Gyrification and General Cognitive Ability in Humans.
      ,
      • Kilpatrick L.A.
      • Joshi S.H.
      • O’Neill J.
      • Kalender G.
      • Dillon A.
      • Best K.M.
      • et al.
      Cortical gyrification in children with attention deficit-hyperactivity disorder and prenatal alcohol exposure.
      ). In sum, our results dovetail with literatures on ERI, OFC function, and inhibitory control.
      Beyond the conceptual convergence, our findings derive import from 1) being pre-registered, 2) having anatomical specificity within the OFC, 3) using surface-based cortical metrics that are sensitive to neurodevelopment, and 4) connecting to specific, differentiable, and stable impulsivity phenotypes, PIF and FTA, that have well-established and transdiagnostic relationships to psychiatric disorders. Indeed, our results in the lateral OFC appear to bridge parallel transdiagnostic literatures in psychiatry and neuroscience. While ERI has been championed as a transdiagnostic phenotype in clinical literatures(
      • Carver C.S.
      • Johnson S.L.
      • Timpano K.R.
      Toward a Functional View of the p Factor in Psychopathology.
      ,
      • Smith G.T.
      • Atkinson E.A.
      • Davis H.A.
      • Riley E.N.
      • Oltmanns J.R.
      The General Factor of Psychopathology.
      ,
      • Caspi A.
      • Moffitt T.E.
      All for One and One for All: Mental Disorders in One Dimension.
      ), the study of lateral OFC morphology and psychopathology appears to be more siloed with few studies testing for similarities across diagnostic boundaries(
      • Drevets W.C.
      Orbitofrontal Cortex Function and Structure in Depression.
      ,
      • Eckart C.
      • Stoppel C.
      • Kaufmann J.
      • Tempelmann C.
      • Hinrichs H.
      • Elbert T.
      • et al.
      Structural alterations in lateral prefrontal, parietal and posterior midline regions of men with chronic posttraumatic stress disorder.
      ,
      • Cardenas V.A.
      • Durazzo T.C.
      • Gazdzinski S.
      • Mon A.
      • Studholme C.
      • Meyerhoff D.J.
      Brain Morphology at Entry into Treatment for Alcohol Dependence Is Related to Relapse Propensity.
      ,
      • Rogers J.C.
      • De Brito S.A.
      Cortical and Subcortical Gray Matter Volume in Youths With Conduct Problems: A Meta-analysis.
      ,
      • Patti M.A.
      • Troiani V.
      Orbitofrontal sulcogyral morphology is a transdiagnostic indicator of brain dysfunction.
      ). Therefore, we hope that the current findings will inform psychological frameworks that integrate the myriad studies linking lateral OFC morphology to separate psychiatric diagnoses. For example, one possible model would be that ERI is an intermediate psychological phenotype that emerges from the cortical development of the lateral OFC and leads to psychopathology. Longitudinal research will be needed to test such models. The transdiagnostic makeup of our sample may have been key to unlocking this link, as neuroanatomical characteristics specific to any one diagnosis were less likely to interfere with the shared ERI signal. We hope that both the methodological approach and empirical results from this study can support future work on ERI and generalize to other research domains that seek to integrate the transdiagnostic study of psychopathology at two crucial levels of analysis – psychiatry and neuroscience.
      Beyond its strengths, this study has several limitations. Replication of the current findings is needed, which we expect given the consistency of the findings with previous studies and theory. The current approach provided less clarity about FTA than it did for PIF. This may be related to the higher proportion of participants in this sample with internalizing psychopathology, which is more closely related to PIF than FTA(7). Other imaging techniques that measure the ways that brain regions dynamically respond to emotion may also be important for building the full picture. Current findings also do not help understand when these structural correlates of ERI arise in development. LGI, like ERI, is sensitive to environmental insults(
      • Kelly P.A.
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      ). An important next step will be to use longitudinal designs to study parallels in the cortical maturation of the lateral OFC and the emergence of ERI.
      Despite limitations, the current findings have implications for mental health treatment. The lateral OFC and ERI have both been shown to be responsive to existing treatments, such as mindfulness(
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      ,
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      ), and cognitive-behavioral therapy(
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      ,
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      • et al.
      A brief online intervention to address aggression in the context of emotion-related impulsivity for those treated for bipolar disorder: Feasibility, acceptability and pilot outcome data.
      ). These clinical literatures complement the empirical link between lateral OFC LGI and ERI severity, and they imply that interventions targeting the lateral OFC may be promising for treating psychopathology transdiagnostically. With replication, the current findings can help clinical scientists target a core feature of psychopathology at neurobiological and psychological levels of intervention, which could alleviate suffering and save lives.

      Uncited reference

      R CT (2018): R: a language and environment for statistical computing, version 3.5.1. Vienna, Austria: R Foundation for Statistical Computing.

      ,
      • Fjell A.M.
      • Grydeland H.
      • Krogsrud S.K.
      • Amlien I.
      • Rohani D.A.
      • Ferschmann L.
      • et al.
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      ,
      • Hogstrom L.J.
      • Westlye L.T.
      • Walhovd K.B.
      • Fjell A.M.
      The Structure of the Cerebral Cortex Across Adult Life: Age-Related Patterns of Surface Area, Thickness, and Gyrification.
      .

      Acknowledgements and Disclosures

      This work was supported by the National Institutes of Health (grant no. R01MH110447 to S.L.J.). The funding agency did not have a role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
      We thank W. Voorhies for her help with data analysis and K. Timpano, K. Modavi, A. Dev, M. Robison, J. Mostajabi, and B. Weinberg for their help with recruitment and data collection. We also thank N. Angelides, H.Y. Tsai, M. Andrews, and J. Giffin for their help with MRI data acquisition.
      All authors report no biomedical financial interests or potential conflicts of interest.
      The data, code, and pre-registration for this study are freely available: (https://osf.io/tfkpb/). The raw data and cortical reconstructions that support the results of this study are available from the corresponding author upon request.

      Supplementary Material

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