Functional coherence of insula networks is associated with externalizing behavior.

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J Abnorm Psychol. Author manuscript; available in PMC 2015 December 01. Published in final edited form as: J Abnorm Psychol. 2015 November ; 124(4): 1079–1091. doi:10.1037/abn0000078.

Functional Coherence of Insula Networks is Associated with Externalizing Behavior Samantha V. Abrama, Krista M. Wisnera, Rachael G. Grazioplenea, Robert F. Kruegera, Angus W. MacDonald IIIa,b, and Colin G. DeYounga aUniversity

of Minnesota, Twin Cities, Department of Psychology, 75 East River Parkway, Minneapolis, MN 55455

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bUniversity

of Minnesota, Twin Cities, Department of Psychiatry, 717 Delaware Street SE, Suite 516, Minneapolis, MN 55414

Abstract

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The externalizing spectrum encompasses a range of maladaptive behaviors, including substance use problems, impulsivity, and aggression. While previous literature has linked externalizing behaviors with prefrontal and amygdala abnormalities, recent studies suggest insula functionality is implicated. The present study investigated the relation between insula functional coherence and externalizing in a large community sample (N=244). Participants underwent a resting functional magnetic resonance imaging scan. Three non-artifactual intrinsic connectivity networks (ICNs) substantially involving the insula were identified after completing independent components analysis. Three externalizing domains—general disinhibition, substance abuse, and callous aggression—were measured with the Externalizing Spectrum Inventory. Regression models tested whether within-network coherence for the three insula ICNs was related to each externalizing domain. Posterior insula coherence was positively associated with general disinhibition and substance abuse. Anterior insula/ventral striatum/anterior cingulate network coherence was negatively associated with general disinhibition. Insula coherence did not relate to the callous aggression domain. Follow-up analyses indicated specificity for insula ICNs in their relation to general disinhibition and substance abuse as compared to other frontal and limbic ICNs. This study found insula network coherence was significantly associated with externalizing behaviors in community participants. Frontal and limbic ICNs containing less insular cortex were not related to externalizing. Thus, the neural synchrony of insula networks may be central for understanding externalizing psychopathology.

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Keywords Externalizing; Disinhibition; Substance Use; Insula; Functional Connectivity

Corresponding Author: Dr. Colin G. DeYoung, University of Minnesota, Twin Cities, Department of Psychology, 75 East River Parkway, Minneapolis, MN 55455. Phone: (612) 624-1619. [email protected]. Financial Disclosures All authors report no biomedical financial interests or potential conflicts of interest.

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Introduction The externalizing spectrum encompasses a range of traits and behaviors that includes substance problems, impulsivity, and aggression in both clinical and non-clinical samples (Krueger, Markon, Patrick, Benning, & Kramer, 2007). Externalizing is one of the two general factors explaining comorbidity among common mental disorders (the other being internalizing), and it constitutes the major genetic risk for disorders including drug and alcohol dependence, antisocial personality disorder, conduct disorder, and ADHD (Kendler, Prescott, Myers, & Neale, 2003; King, Iacono, & McGue, 2004; Krueger et al., 2002). Externalizing problems have psychological and functional consequences that generate substantial financial costs to society (Woltering, Granic, Lamm, & Lewis, 2011).

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Despite their importance, a thorough account of the mechanisms and neural bases of externalizing is lacking. Although neuroimaging research has often linked externalizing behaviors with prefrontal cortex and amygdala abnormalities (Crews & Boettiger, 2010; Shehzad, DeYoung, Kang, Grigorenko, & Gray, 2012), multiple studies suggest that the insula may also be implicated in externalizing. The insula, or insular cortex, functions as a neural hub that coordinates a variety of inputs including interoceptive/homeostatic, emotional, and cognitive signals (Augustine, 1996; Cauda et al., 2011; Liang, Zou, He, & Yang, 2013; Mesulam & Mufson, 1982a, 1982b). The insula also plays a key role in synchronizing internal and external sensations, motor, and higher-order processes (Jezzini, Caruana, Stoianov, Gallese, & Rizzolatti, 2012; Kurth, Zilles, Fox, Laird, & Eickhoff, 2010; Mutschler et al., 2009; Stephani, Fernandez-Baca Vaca, MacIunas, Koubeissi, & Lüders, 2011). Moreover, the insula is involved in emotion regulation, particularly reappraisal, as well as reward processing (Beck et al., 2009; Ochsner & Gross, 2005; Tanaka et al., 2004; Villafuerte et al., 2012). Abnormalities in insula function have been found in clinical samples with externalizing psychopathologies, such as substance addiction (Goldstein et al., 2009; Naqvi & Bechara, 2009; Wisner, Patzelt, Lim, & MacDonald, 2013). Further, taskbased activation in the insula has been associated with individual differences in externalizing (Carroll, Sutherland, Salmeron, Ross, & Stein, 2013; Villafuerte et al., 2012). For example, hypoactivation of the insula during reward anticipation among substancedependent individuals has been shown to correlate with impulsivity (Beck et al., 2009).

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The present study extends this research to examine the relation between insula network connectivity and different facets of externalizing. The externalizing spectrum has been shown to encompass three correlated but nonetheless separable domains—general disinhibition, substance abuse, and callous aggression—which are operationalized in the Externalizing Spectrum Inventory (ESI; scales names italicized throughout; Krueger et al., 2007). The general disinhibition domain is associated with various manifestations of impulsivity and irresponsibility. The substance abuse domain is associated with both recreational and problematic use of alcohol, marijuana, and other illicit drugs; higher scores indicate more problematic levels of use (although note that the ESI is not designed to render a formal DSM diagnosis). The callous aggression domain is associated with tendencies toward physical and relational aggression and lack of empathy. These dimensions of externalizing may also differ in their neurobiological substrates, such as the role of insular cortex.

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Evidence suggests that the general disinhibition and substance abuse domains are likely to be linked to insula functioning, and callous aggression may be related more weakly. Numerous studies have posited that the insula’s interoceptive and integrative functions are important for understanding the neural mechanisms responsible for urges, cravings, and selfmonitoring behaviors that contribute to impulsivity and substance abuse (Carroll et al., 2013; Goldstein et al., 2009; Naqvi & Bechara, 2009; Villafuerte et al., 2012; Wisner, Patzelt, et al., 2013). The risky decision-making that enables impulsive behaviors involves cognitive appraisal, but it is also influenced by interoceptive signals such as physical feelings of excitement (Xue, Lu, Levin, & Bechara, 2010). Gambling, for example, may generate physiological arousal that incites urges and further actions via the integration of sensory and reward signals coordinated in the insula. The decreased ability to monitor or inhibit behavior may also stem from aberrant insula functioning and network integration. For instance, two studies found that reduced coupling between insula and striatal networks was associated with self-reported impulsivity among individuals with cocaine dependence (McHugh et al., 2013; Wisner, Patzelt, et al., 2013). Substance abuse and addiction may be similarly tied to the interoceptive insula functions, as increased insula activation has been shown to be positively associated with urges to use substances (Naqvi & Bechara, 2009). Insula damage has antagonistic effects on addiction; patients with insula damage showed a “disruption in smoking addiction,” described as the ability to quit easily without tenacious urges to smoke (Naqvi, Rudrauf, Damasio, & Bechara, 2007). Thus, evidence for the influence of insula functioning on interoceptive signals associated with urges, drives, and disinhibition is reasonably robust.


Considerable research also links the insula with empathy (Kurth, Zilles, et al., 2010; Singer, Critchley, & Preuschoff, 2009), a process that reflects one’s capacity to share or understand the emotional states of other individuals and is thought to be inversely related to callous aggression. Several functional neuroimaging studies have noted insula activation in response to one’s own pain or when observing others in pain (Jackson, Brunet, Meltzoff, & Decety, 2006; Lamm, Batson, & Decety, 2007; Singer et al., 2004). Impairments in empathic abilities are present in a range of psychiatric disorders, including those with core aggressive features like conduct disorder (Decety, Michalska, Akitsuki, & Lahey, 2009). The insula has been implicated in conduct disorder, and, therefore, may represent a shared neuroanatomical substrate that bridges aggression with impaired empathy. More specifically, previous work found reduced insula volume in adolescents with conduct disorder that, in turn, was negatively correlated with self-reported empathy (Sterzer, Stadler, Poustka, & Kleinschmidt, 2007). The authors postulate that these morphometric abnormalities may contribute to aggressive behavior. This growing area of research suggests that the insula’s role in emotional processing may be important in empathy and may contribute to the neural circuitry underlying aggression (Coccaro, Sripada, Yanowitch, & Phan, 2011). However, meta-analysis suggests that most of the association of insula function with empathy is non-specific—that is, it is shared with other emotional and interoceptive processes (Kurth et al., 2010). Hence, we were less confident in the association of insula networks with callous aggression relative to the other externalizing domains.


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Consideration of the insula’s functional architecture may be crucial for elucidating relations between insula functionality and different externalizing domains. Prior studies have parcellated the insula according to its inherent functional organization, to better understand where interoceptive, emotional, and cognitive processes are supported within this structure (Chang, Yarkoni, Khaw, & Sanfey, 2013; C. Kelly et al., 2012). Their findings indicate that the anterior insula is involved in processing cognitive and emotional information, whereas the posterior insula is more responsible for interoceptive and sensorimotor processing. The anterior insula is functionally connected to the frontal cortices, anterior cingulate cortex (ACC), and limbic regions, whereas the posterior insula is functionally connected to motor, somatosensory, and temporal cortices both at rest and during a task (Cauda et al., 2011, 2012).

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Research on the processing hierarchy of the insula suggests that disparate sensory inputs are first received in the posterior insula and then integrated by the anterior insula along with incoming cognitive or emotional signals, thus highlighting the importance of the anterior insula for integrative functions (Cauda et al., 2012; Craig, 2011; Kurth, Eickhoff, et al., 2010). The anterior insula can be further partitioned into a ventral portion that is implicated in social and emotional processing, and a dorsal portion implicated in cognitive processing (e.g., attention) (Chang et al., 2013; C. Kelly et al., 2012; Touroutoglou, Hollenbeck, Dickerson, & Feldman Barrett, 2012). Given this complex functional organization, it is possible that distinct insula subregions are differentially linked to various externalizing behaviors.

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Our research addresses the question of how brain connectivity within networks containing various insula subdivisions may be associated with disparate externalizing domains. Most related studies have examined clinical samples, and inconsistent results are potentially due to differences in sample characteristics. Investigations in community populations are an important complement to prior research, as they avoid certain methodological issues that arise with clinical externalizing samples, such as neurobiological changes from chronic substance abuse (Ameis et al., 2014; Konova, Moeller, Tomasi, & Goldstein, 2015; Pujol et al., 2014).

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To investigate the relation between network-based insula connectivity and externalizing domains, we examined resting state functional connectivity, which measures the synchrony among voxels of low frequency (< 0.1 Hz) fluctuations in neuronal signal in the absence of cognitive demands (Biswal et al. 1995). We opted to evaluate resting rather than task-based connectivity, as our intention was to investigate relations between connectivity and a broad trait domain rather than a specific mechanism elicited by a cognitive paradigm. More specifically, differences at rest do not necessarily reflect specific task demands, which suggests that resting connectivity findings may capture a broader swath of processes than would be the case for any specific task. At the same time, resting state networks show a high correspondence with several established task-derived functional networks (Mennes et al., 2010; Smith et al., 2009). Researchers have therefore described these networks as “intrinsic connectivity networks,” as they appear to reflect the brain’s inherent functional architecture at rest and during tasks (Smith et al., 2009; Wisner, Atluri, Lim, & MacDonald, 2013).


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We applied independent components analysis (ICA) to the resting state fMRI data (Beckmann, DeLuca, Devlin, & Smith, 2005; Beckmann & Smith, 2004). ICA is a modelfree method of “blind source separation,” where multivariate signals are separated into statistically independent non-Gaussian sources termed “components”. This data-driven procedure generates components reflecting natural patterns of synchronized fluctuations in the signals derived from fMRI data. ICA has the ability to parse variance associated with physiological artifacts (e.g. head motion, cardiac function) from variance associated with neural activity (Beckmann et al., 2005). This bias-reduction feature has made ICA a widely used alternative to traditional seed-based connectivity analyses, which are more vulnerable to artifacts. Given their data-driven nature, ICNs can characterize functional connections between both local and more distal brain regions. In other words, an ICN may include structurally distinct, yet highly functionally integrated, brain regions (e.g., ACC and anterior insula) that are likely to be important drivers of cognitive processes and trait differences.


ICA methods can also capture and separate functionally distinct connectivity patterns within brain regions. This quality is particularly valuable for examining insula functional connectivity, since previous studies indicate distinguishable roles for subdivisions of this structure (Chang et al., 2013; C. Kelly et al., 2012; Touroutoglou et al., 2012). The number of components extracted from fMRI data using ICA, referred to as the dimensionality, can be specified over a wide range. Notably, the selected dimensionality constraint influences ICN fractionation (Ray et al., 2013), which can affect the separation of networks driven by different insula subdivisions. Prior work has shown that high dimensionalities (such as 60) can yield several insula subnetworks (Wisner, Patzelt, et al., 2013), while also producing a reliable set of ICNs overall (Poppe et al., 2013). Thus, for the present analyses, we extracted 60 ICNs from fMRI data to assess relations between insula network connectivity and traits on three major externalizing domains. The present study utilized a nonclinical sample to study the neural correlates of externalizing domains in the community using multivariate linear regression. Specifically, we focused on within-network connectivity, which we will refer to as network coherence. We tested the hypothesis that individual differences in insula network coherence would be associated with the general disinhibition and substance abuse domains. We also tested whether the callous aggression domain would be associated with insula network coherence, based on empathy research.

Methods and Materials Sample Recruitment and Demographics

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A psychiatrically healthy, right-handed community sample aged 20 to 40 (N=306) was collected as part of a larger study examining the neural correlates of personality and cognition. Participants were recruited via CraigsList. A total of 244 participants (50% male, mean age = 26 years) were retained after exclusions: six due to attrition, nine for poor quality data, 31 for excessive movement during the scan (mean absolute displacement above 1.5 mm, or any absolute displacement (translations or rotations) above 2.75 mm), five for not completing the externalizing self-report measure, and 11 for not completing the rest scan. There were no differences between males and females in terms of age, intelligence J Abnorm Psychol. Author manuscript; available in PMC 2015 December 01.

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(IQ), or proportion that was non-White. During recruitment, potential participants were excluded for current use of psychotropic medications, including anti-psychotics, anticonvulsants, and stimulants, as well as for history of neurologic or psychiatric disorders. Participants were not excluded based on their history of alcohol and recreational drug use, as this behavior was of interest to the study; however, they were excluded if they indicated current drug/alcohol problems. MRI contraindications (e.g., ferromagnetic implants, pacemakers) were also exclusionary. The University of Minnesota institutional review board approved the study and participants provided written informed consent. Externalizing Measurement

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Participants completed the Externalizing Spectrum Inventory, brief form (ESI-BF), a selfreport assessment composed of 160 items (Patrick, Kramer, Krueger, & Markon, 2013), that constitutes a brief version of the original ESI (Krueger et al., 2007). The ESI has been validated using diagnostic interviews and neurobiological assessments, including eventrelated potentials in EEG (33–36). The 160-item version reflects the same organization of the original 415-item ESI, yielding three higher order domains (general disinhibition, substance abuse, and callous aggression) and 23 lower order facets (Krueger et al., 2007). The present study utilized the three higher order domains. Scores on these domains reflect mean responses to items (sum of scores divided by number of items), with general disinhibition having 20 items, substance abuse 18 items, and callous aggression 19 items. Items are rated on a 1–4 scale, where higher values correspond with greater levels of the characteristic. Males scored significantly higher than females on the general disinhibition (t242 = 3.06, p = .002), substance abuse (t242 = 4.47, p < .0001), and callous aggression (t203 = 6.47, p < .0001) domains. We used a Welsch t-test for the callous aggression domain to account for unequal variances between males and females, given a significant Levine’s test (F1,242 = 18.43, p < .001).

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Intelligence IQ was included as a covariate in analyses because of its association with externalizing problems (Koenen, Caspi, Moffitt, Rijsdijk, & Taylor, 2006; Kuntsi et al., 2004). IQ was estimated based on four subtests of the Wechsler Adult Intelligence Scale – Fourth Edition (WAIS-IV): Block Design, Matrix Reasoning, Vocabulary, and Similarities (Wechsler, 2008). Image Acquisition and Pre-Processing

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Neuroimaging data were acquired using a 3T Siemens Trio scanner (Erlangen, Germany) at the University of Minnesota’s Center for Magnetic Resonance Research. Participants remained awake during the rest scan while attending to a basic fixation task: they pushed a button each time a crosshair in the center of the screen changed from white to gray or vice versa (which occurred five times). The additional button presses were a slight departure from some prior protocols, however this kind of approach has been previously used to ensure (and later check) that subjects remained awake while minimizing eye movements (Fair et al., 2007; Fox et al., 2009; Fox and Grecius, 2009; Weng et al., 2010; Craddock et al., 2013). It is unlikely that this additional task demand introduced substantial effects on


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resting state connectivity strength and/or reliability and only small differences have been reported for resting connectivity reliability across various resting conditions (eyes-closed, eyes-open, eyes-open with fixation; Patriat et al., 2013). Sequence parameters for the rest scan included: gradient-echo echo-planar imaging of 150 volumes; repetition time (TR) = 2 s; echo time (TE) = 28 ms; flip angle = 80°; voxel size = 3.5 × 3.5 × 3.5 mm. A high-resolution T1-weighted structural scan was collected for registration. Standard pre-processing was completed using FMRIB Software Library (FSL 4.1.9) that included brain extraction, motion correction, grand mean intensity normalization of the 4D dataset, high pass temporal filtering (at a filtering threshold of 0.1 Hz), and registration of functional images to high-resolution T1-weighted structural images (Wisner, Atluri, et al., 2013; Wisner, Patzelt, et al., 2013). Motion regression was completed as the final step.

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Independent Components Analysis

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ICNs were produced using a meta-ICA pipeline to optimize network consistency (Poppe et al., 2013). In this procedure 25 temporal concatenation (model-free and multi-subject) group-level probabilistic ICAs were completed using the MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components) function in FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC). Each ICA employed a randomly generated subject order containing 80 of the participants as inputs, due to computational/ hardware limitations and to reduce likelihood of over-fitting. We chose a dimensionality constraint of 60 based on prior optimization findings (Poppe et al., 2013), and the desire to examine the subfractionation of large-scale brain networks (Ray et al., 2013; Wisner et al., 2013a, 2013b). The 60 components from each ICA were concatenated into a single file (1500 components total). This file was then employed as the input to a single meta-level MELODIC (meta-ICA) to derive the 60 most consistent group-level components. The procedures outlined by Kelly et al. (2010) were used to identify artifactual components, which included components that were believed to reflect cardiac function, respiration, nonneural fluctuations, white matter tracts, or movement. A final set of 27 non-artifactual components was retained for further investigation. Network Coherence Computations

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Dual-regression was used to derive subject-level spatial maps and timeseries for each individual based on the group-level maps yielded from the ICA (Beckmann, Mackay, Filippini, & Smith, 2009; Filippini et al., 2009; Wisner, Patzelt, et al., 2013; Zuo et al., 2010). First, the full set of group-level spatial maps was used as spatial regressors onto each subject's 4D dataset. This resulted in a set of subject-specific timeseries, one per group-level spatial map per subject. Next, those subject-specific timeseries were used as temporal regressors onto the respective participants’ 4D dataset to derive a set of subject-specific spatial maps, one per subject-specific timeseries (Poppe et al., 2013). The value of each voxel in the subject-level spatial map reflected how well the timeseries of the voxel corresponded to the overall timeseries of the component, for the respective subject.


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Network coherence, also known as the mean connectivity score, was computed for each subject using the subject-level spatial maps derived by dual regression (Moodie, Wisner, & MacDonald, 2014; Poppe et al., 2013; Wisner, Atluri, et al., 2013; Wisner, Patzelt, et al., 2013). To calculate this metric, group-level components were normalized by the maximum value, then thresholded at zmax > 0.30 based on prior work (Poppe et al., 2013). Thresholded group-level component maps were binarized and applied as masks to the respective subjectlevel spatial maps. For each subject, the mean of all voxels within the respective group-level mask was calculated for each of the 60 components separately. These values reflect the overall coherence of each component for each subject; that is, larger values represented more integrated dynamics across voxels and thus greater network coherence. Network coherence values were tested for normality using the Shapiro-Wilk test (Shapiro & Wilk, 1965); a log transform was applied to components with positive skew, which was found in all component distributions. Lastly, network coherence values were standardized to z-score form to enable the comparison of coefficient magnitudes in the regression models. Insula Network Analyses ICNs containing insula were identified using a probabilistic insular cortex mask as defined by the Harvard-Oxford Atlas (25% threshold) (C. Kelly et al., 2012). Nine data-driven nonartifactual components included insular cortex (see Supplemental Table 1). Three ICNs were selected for analysis based on an insula-overlap of at least 750 voxels in order to ensure substantial insula involvement (Figure 1); these included: 1) posterior insula and Heschl’s gyrus (posterior insula network), 2) anterior insula, ventral striatum, and ACC (AI-VStrACC network), and 3) anterior insula and lateral orbitofrontal cortex (AI-OFC network). The remaining six ICNs containing fewer insula voxels are illustrated in Supplemental Figure 1.

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We first produced three multivariate regression models to examine whether insula network coherence was associated with each externalizing domain. Each model included main effects for the network coherence of the three insula ICNs, plus age, IQ, and sex. As a second step, we tested for the presence of insula by sex interactions. These 2-way interactions were examined because of the extensive literature indicating greater prevalence and liability for externalizing in males (Eaton et al., 2012; Kramer, Krueger, & Hicks, 2008; NolenHoeksema, 2004), as well as notable sex differences in insula function and morphology (Lee, Chan, Leung, Fox, & Gao, 2009; Moriguchi, Touroutoglou, Dickerson, & Barrett, 2014; Stevens & Hamann, 2012; Tanabe et al., 2013). Frontal/Limbic Network Analyses

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We also assessed whether there was a specific role for insula network coherence in relation to externalizing, when compared to other frontal or limbic networks. The decision to examine frontal and limbic networks was based on research linking those areas with impulsivity and related psychopathology (Crews & Boettiger, 2010; Davis et al., 2013; Kerr et al., 2014; Xie et al., 2011). This entailed building two additional sets of regression models, one that included frontal ICNs and a second that included limbic ICNs. Data-driven frontal ICNs were as follows: 1) right frontal-parietal, 2) bilateral frontal-parietal, 3) frontal pole, 4) ventromedial prefrontal cortex, and 5) orbitofrontal cortex. Data-driven limbic ICNs were as follows: 1) putamen, amygdala, and thalamus, and 2) hippocampus, amygdala, and


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temporal pole. These models also included the three covariates used in the insula network analyses (age, IQ, sex). Potential Movement Confounds

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We took three approaches to account for potential movement confounds. First, we conducted motion correction and motion regression during the pre-processing stage to reduce within-subject movement. Second, we computed correlations between movement parameters with externalizing, connectivity metrics of interest, and ICN timeseries; these calculations are provided in Supplementary Materials.1 Movement was computed as the root mean square head position change (RMS movement). This is a summary statistic that reflects the average displacement across six movement parameters, which include three translational displacements across the X, Y, and Z axes and three rotational displacements of pitch, yaw, and roll (Power, Barnes, Snyder, Schlaggar, & Petersen, 2012). Because movement was correlated with some connectivity metrics, we entered mean RMS movement as a covariate in all regression models. Third, we repeated the meta-ICA processes using subjects with a more stringent mean absolute displacement cutoff of 0.5 mm (N = 218). This entailed deriving a new set of 60 ICNs, re-running dual regression, and re-calculating within-network coherence metrics. Using procedures described by Poppe et al. (2013), we computed Dice similarity coefficients to identify the three insula ICNs from the reduced sample that had the greatest spatial overlap with those produced using the entire sample (N = 244). We then generated new insula regression models using the metrics associated with the new insula ICNs from the specified subsample, and repeated analyses within this subsample.

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Insula Network Analyses Table 1 presents regression models for insula coherence with each of the three externalizing domains. Our results revealed significant overall models for the general disinhibition (F7,236 = 5.90, p < .0001) and substance abuse (F7,236 = 5.08, p < .0001) domains. In the general disinhibition model, we found significant main effects for the posterior insula network, the AI-VStr-ACC network, and the AI-OFC network. We also found that coherence within the posterior insula network was related to scores on the substance abuse domain. Insula network coherence was not related to callous aggression.

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We then repeated these analyses in a reduced sample (N=218) that only included subjects with mean absolute displacement of < 0.5 mm. Overall, we observed similar associations between insula coherence and externalizing relative to the results in the sample of 224 subjects (Supplemental Table 3). However, the AI-OFC network was no longer significantly associated with general disinhibition. Given the consistent results for the posterior insula

1Analyses to assess whether motion parameter estimates accounted for variance in the ICA timeseries were as follows: First, we correlated the displacement timeseries vectors for each subject, with their respective ICA timeseries. Second, we used one-sample ttests to determine whether these correlations were significantly different from zero for the three insula networks (Wisner et al., 2013b): the posterior insula (t243 = −1.77, p = .08) and AI-VStr-ACC (t243 = −1.39, p = .19) networks were non-significant, whereas the AI-OFC network (t243 = −5.54, p < .001) was significant. Further details on these analyses are found in the Supplemental Materials. J Abnorm Psychol. Author manuscript; available in PMC 2015 December 01.

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and AI-Vtr-ACC networks with externalizing, we limit our interpretation to those findings (see Supplemental Materials for more information).

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Given significant differences between males and females in the three externalizing factors, we added insula-by-sex interaction terms to the regression models (full sample, N = 244). We then performed a change in F-test for each externalizing domain to determine whether the additional terms yielded significant increases in R2. We found non-significant increases in model fit for the general disinhibition (F3,233 = 1.28, p = .28), substance abuse (F3,233 = 1.10, p = .35), and callous aggression (F3,233 = 0.21, p = .89) models. Additionally, all individual interaction terms were non-significant. Nonetheless, given the documented sex differences in insula structure and function (Lee et al., 2009; Moriguchi et al., 2014; Stevens & Hamann, 2012; Tanabe et al., 2013), we have provided three supplemental tables characterizing sex differences for each externalizing domain (see Supplemental Tables 4A– 4C). The results suggest that the association between insula coherence and externalizing may be stronger in males, although our study could not detect any significant difference in effect size (which would have been indicated by a significant interaction term). This should be a focus of future research in larger samples.

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To enhance our interpretation of the insula findings, we examined zero-order correlations between the posterior insula and AI-VStr-ACC networks with general disinhibition scores in the full sample (N = 244). These particular correlations were examined based on significant but opposing relations between the two insula ICNs and the criterion. The results revealed a cooperative suppression effect, where the beta coefficients from the model for both predictors were larger than their corresponding pairwise correlations (Hicks & Patrick, 2006; Verona, Hicks, & Patrick, 2005). Here, posterior insula network coherence had a small positive correlation with general disinhibition (r = .16, p = .02) and AI-VStr-ACC network coherence had a negligible correlation (r = −.05, p = .42). Both pairwise correlations are notably smaller than the regression weights reported in Table 1: posterior insula (β = .34, p < .001) and AI-VStr-ACC (β = −.23, p = .02). The cooperative suppression effect is illustrated in Figure 2. We also computed partial correlations to determine the unique contributions of both the posterior insula (r = .24, p < .001) and AI-VStr-ACC (r = −. 15, p = .02) networks for explaining variance in the general disinhibition model.

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We also constructed a series of multivariate regression models to better understand the null finding for callous aggression, (Supplemental Tables 2A and 2B). Specifically, these models assessed whether insula network coherence was differentially associated with the callous aggression facets (e.g., empathy versus aggression). These results indicated significant associations for insula coherence with physical aggression (F7,236 = 7.53, p < .0001), and destructive aggression (F7,236 = 6.31, p < .0001). However, no significant relations were present between insula coherence and empathy, honesty, excitement seeking, or relational aggression. Frontal/Limbic Network Analyses Our results indicate that other frontal and limbic networks were not significantly associated with the three externalizing domains (see Tables 2 and 3). As a last step, we produced alternative models that included all previously tested insula (3 ICNs), frontal (5 ICNs) and J Abnorm Psychol. Author manuscript; available in PMC 2015 December 01.

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limbic (2 ICNs) networks in a single model (total of 10 ICNs). This demonstrated that no new significant findings emerged due to suppression between network pairs including frontal and limbic ICNs. Therefore, the relations between insula network coherence and externalizing domains described above remained significant when accounting for other frontal and limbic networks.

Discussion

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In this study, we examined associations between insula network coherence and three externalizing domains in a large non-clinical sample. Our results indicate that individual differences in insula network coherence accounted for significant variation in general disinhibition and substance abuse. Specifically, our primary regression models indicate that the general disinhibition domain of externalizing was associated with greater coherence in the posterior insula ICN and reduced coherence in the anterior insula, ventral striatum, and ACC ICN. Greater coherence in the posterior insula ICN was also related to the substance abuse domain. The callous aggression domain was not associated with insula coherence. Follow-up analyses indicate that frontal and limbic networks containing relatively fewer or zero insula voxels were not significantly associated with the three externalizing domains. These findings suggest a specific role for networks containing a substantial portion of the insula in capturing two externalizing domains that measure problems related to poor impulse control and drug use. Therefore, the insula may be an important target in understanding the development and manifestation of impulsivity and recreational or problematic substance use, as these associations were found using a non-clinical sample.


A notable aspect of our findings was the contrasting directionality for the associations between the anterior and posterior insula ICNs with general disinhibition. These differences likely reflect the differential functional architecture within this cortical structure. The midposterior insula has been described as an integrative sensorimotor region, receiving a range of inputs including exteroreceptive (e.g. temperature, touch, pain) and interoceptive information (e.g. visceral sensations) (Chang et al., 2013). Thus, greater mid-to-posterior insula coherence may reflect heightened sensitivity to physiological sensations or urges. This interpretation is consistent with work by Cole et al., (2010) and Sutherland et al. (2013), who found that decreased insula and striatal connectivity was associated with reductions in withdrawal symptoms (during nicotine replacement therapy). In contrast, the anterior insula is implicated in higher-order cognitive and emotional processing. Craig (Craig, 2010) dubbed the anterior insula the “sentient self” given its role in conscious interoceptive monitoring (Critchley, Wiens, Rotshtein, Ohman, & Dolan, 2004; Kurth, Zilles, et al., 2010; Zaki, Davis, & Ochsner, 2012). The anterior insula and ACC are involved in a “salience detection network,” which is believed to select internal and external information most pertinent to maintaining homeostasis and goal driven behavior (Chang et al., 2013; Menon & Uddin, 2010; Seeley et al., 2007). The ACC is also involved in error monitoring (Carter et al., 1998). Thus, reduced anterior insula-ACC coherence may reflect weakened ability to attend to and incorporate stimuli congruent with one’s goals. The combination of increased posterior insula network coherence and reduced anterior insula network coherence may reflect aberrant interoceptive processing and self-monitoring leading to maladaptive disinhibition of behaviors related to various forms of urges and J Abnorm Psychol. Author manuscript; available in PMC 2015 December 01.

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impulses. Moreover, the observed cooperative suppression effect suggests complementary roles for anterior and posterior regions of the insula for these behaviors, whereby both are needed to capture general disinhibition adequately.

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Posterior insula ICN coherence was also related to substance abuse scores. Given the posterior insula’s role in pain and interoception of physiological processes (Cauda et al., 2011; Chang et al., 2013), one might speculate that intensified physical withdrawal sensations are related to the coherence of this network, and lead to increased desire to procure or use drugs. This explanation is consistent with the literature on the insula and drug cravings (Naqvi & Bechara, 2009), such as the withdrawal-insula associations described above. The posterior insula has also been linked with relapse and substance addiction. For example, connectivity between the posterior insula and striatal regions was found to predict relapse in cocaine-addicted individuals (McHugh et al., 2013). The lack of association with insula ICNs containing OFC, ACC, or striatum and the substance abuse domain in our study may be due to the non-clinical sample, particularly given that previous substance addiction studies have highlighted these associations in clinical contexts (Wisner, Patzelt, et al., 2013). Some effects might have been stronger had our sample emphasized diagnosed substance abusers.

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In contrast to the associations observed for general disinhibition and substance abuse, insula coherence was unrelated to the callous aggression domain. A more in-depth exploration of this domain revealed no association with the empathy facet, despite previous studies linking empathy to insula function (Bernhardt & Singer, 2012; Kurth, Zilles, et al., 2010; Singer et al., 2004; Zaki, Ochsner, Hanelin, Wager, & Mackey, 2007). The only significant associations in this analysis were between insula network coherence and the physical and destructive aggression facets. This pattern of findings is reasonably consistent with that of Cope et al. (2014), who found drug-related hemodynamic activity was more related to the impulsivity and behavior problems factor of the Psychopathy Checklist Revised (PCL-R) than to the interpersonal and affective trait factor that includes lack of empathy.

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It is interesting that frontal and limbic ICNs containing fewer insula voxels were not significantly associated with general disinhibition or substance abuse. Superficially, these results may seem to depart from a number of studies linking orbitofrontal and limbic regions with externalizing problems (Ameis et al., 2014; Bechara, Damasio, Damasio, & Anderson, 1994; Berlin, Rolls, & Kischka, 2004; Fellows & Farah, 2005; Woolley et al., 2007). However, the anterior insula component that was significant in our study contained a nontrivial number of voxels in limbic regions, including the ventral striatum and ACC. Coherence in insula networks may be more strongly associated with general disinhibition than coherence within frontal or limbic regions that do not involve insula, possibly due to insula’s capacity for integrating information received from frontal and limbic regions with interoceptive and sensory information. Additionally, the present findings reflect brain-behavior relationships that were derived from resting-state data, which may contribute to the divergence from previous task-based studies implicating frontal and limbic networks in externalizing. Although ICNs have been shown to map onto task-based activation with similar network structure, this methodological


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difference might have contributed to slightly different conclusions. Our method may have emphasized relevant networks associated with trait-level neurobiology rather than more specific regions involved in task-driven mechanisms. Thus, while frontal and limbic networks remain important to understanding the manifestation of externalizing, the present findings suggest that considering the insula’s role may advance our current conceptualization of the neurobiology of externalizing. Limitations

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First, our study was restricted to a self-report outcome measure of externalizing psychopathology. Although the ESI has been validated against diagnostic interviews (Patrick et al., 2013), it would be useful to replicate the current findings in studies using other data sources, such as interviews or informant reports. Second, all participants underwent task-based functional scans prior to the resting state scan. It is therefore possible that the preceding scan influenced our resting connectivity findings, as there is emerging evidence that prior task engagement influences the coherence of certain networks at rest (Rosassa and Minati, 2011). Third, our sample included only community participants, which prevented us from evaluating how our findings generalize to individuals with clinical diagnoses. Lastly, we did not collect a toxicology screen or a measure of past substance abuse severity to include as a covariate in our analyses. Thus, we could not assess how past use influenced our brain connectivity metrics. Future Directions

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Future work might examine how insula network coherence relates to task-based behavioral performance in addition to self-report externalizing. These analyses could also be repeated using ICA on task-based data to determine whether network coherence for task-driven networks similarly explains individual differences in externalizing. Researchers might also consider including a motion parameter estimate that is unbiased to eye-movement, BOLD activation, or pulsation to further reduce movement confounds (Freire & Mangin, 2001). Conclusions Our findings support the association of insula network coherence with individual differences in general disinhibition and substance abuse, as indicated by the Externalizing Spectrum Inventory. These results may provide insight into the neural mechanisms that underlie externalizing behaviors, which occur across clinical and non-clinical populations. Additionally, this work may have implications for treatment programs that target externalizing psychopathology, and in particular, impulse- and substance-related problems.

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Supplementary Material Refer to Web version on PubMed Central for supplementary material.

Acknowledgments This study was supported by grants to Colin DeYoung from the National Institute on Drug Abuse (NIDA) (R03 DA029177-01A1) and from the National Science Foundation (NSF) (SES-1061817).


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General Scientific Summary Neuroimaging was used to investigate patterns of brain activity associated with externalizing behavior, including general disinhibition and impulsivity, substance abuse, and aggression. The functional coherence of neural networks involving the insula was associated with variation in externalizing in a community sample.

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Figure 1. ICA-Derived Insula Intrinsic Connectivity Networks (Primary Networks)

Note: Three bilateral intrinsic connectivity networks (ICNs) overlapping at least 750 voxels with atlas-defined insula (red mask), including: A) posterior insula and Heschl’s gyrus (purple), B) anterior insula, ventral striatum, and anterior cingulate cortex (orange), and C) anterior insula and orbitofrontal cortex (green). The images are in neurological convention (i.e., left is left).


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Figure 2. Suppression Effects for Posterior and Anterior Insula Portions with General Disinhibition

Note: Illustration of cooperative suppression between two insula intrinsic connectivity networks (ICNs) with the general disinhibition domain. All variables are in z-score form. A) Small to negligible pairwise correlations between the posterior insula and anterior insula, ventral striatum, and anterior cingulate cortex (AI-VStr-ACC) networks with general disinhibition domain. B) Increased coefficients for both the posterior insula and AI-VStrACC when included in the regression model together. Turquoise points represent the general disinhibition scores along the gray, fitted regression plane. The ICN coefficients in the regression model displayed were computed using only two predictors (which does not include the covariates used in the main analyses).

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Author Manuscript F7,236 5.89

.16 (.07) Adj. R2 .12

Mean RMS Movement

2.87

Increased Functional Connectivity in an Insula-Based Network is Associated with Improved Smoking Cessation Outcomes.

Major depressive disorder is associated with abnormal interoceptive activity and functional connectivity in the insula.

Damage to the insula is associated with abnormal interpersonal trust.

Intergenerational Transmission of Externalizing Behavior.

Impaired insula functional connectivity associated with persistent pain perception in patients with complex regional pain syndrome.

Increased insula coactivation with salience networks in insomnia.

Infant risk factors associated with internalizing, externalizing, and co-occurring behavior problems in young children.

Functional Connectivity in Multiple Cortical Networks Is Associated with Performance Across Cognitive Domains in Older Adults.

Aberrant functional connectivity in dissociable hippocampal networks is associated with deficits in memory.

Mindfulness is associated with intrinsic functional connectivity between default mode and salience networks.

Insula-amygdala functional connectivity is correlated with habituation to repeated negative images.

Group Cognitive Behavior Therapy Reversed Insula Subregions Functional Connectivity in Asthmatic Patients.

Functional brain networks associated with eating behaviors in obesity.

Self-regulation assessment among preschoolers with externalizing behavior problems.

Insula-Dorsal Anterior Cingulate Cortex Coupling is Associated with Enhanced Brain Reactivity to Smoking Cues.

Generational Differences in Children's Externalizing Behavior Problems.

Emotional intelligence is associated with reduced insula responses to masked angry faces.

Codevelopment of ADHD and externalizing behavior from childhood to adulthood.

Functional Connectivity of Insula, Basal Ganglia, and Prefrontal Executive Control Networks during Hypoglycemia in Type 1 Diabetes.

Differential Components of Reactivity and Attentional Control Predicting Externalizing Behavior.

Extremity of Externalizing Behavior and Young Adult Outcomes.

Functional specialization of the male insula during taste perception.

Externalizing behavior is prospectively associated with intake of added sugar and sodium among low socioeconomic status preschoolers in a sex-specific manner.

Infant externalizing behavior as a self-organizing construct.