Research report

Altered insular activation and increased insular functional connectivityduring sad and happy face processing in adolescent majordepressive disorder

Eva Henje Blom a,b,1, Colm G. Connolly a,n,1, Tiffany C. Ho a, Kaja Z. LeWinn a,Nisreen Mobayed a, Laura Han a,c, Martin P. Paulus d,e, Jing Wu a,f,Alan N. Simmons d,g, Tony T. Yang a

a Department of Psychiatry, Division of Child and Adolescent Psychiatry, University of California San Francisco, San Francisco, CA, USAb Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Swedenc Institute for Interdisciplinary Studies, Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, The Netherlandsd Department of Psychiatry, University of California, San Diego, CA, USAe Laureate Institute for Brain Research, Tulsa, OK, USAf Department of Bioengineering at the University of Washington, Seattle, WA, USAg The Veterans Affairs Health Care System of San Diego, La Jolla, CA, USA

a r t i c l e i n f o

Article history:Received 26 February 2015Received in revised form2 March 2015Accepted 5 March 2015Available online 14 March 2015

Keywords:Adolescent major depressive disorderAnterior insular cortexFunctional magnetic resonance imagingFunctional connectivity

a b s t r a c t

Background: Major depressive disorder (MDD) is a leading cause of disability worldwide and occurscommonly first during adolescence. The insular cortex (IC) plays an important role in integrating emotionprocessing with interoception and has been implicated recently in the pathophysiology of adult andadolescent MDD. However, no studies have yet specifically examined the IC in adolescent MDD duringprocessing of faces in the sad–happy continuum. Thus, the aim of the present study is to investigate theIC during sad and happy face processing in adolescents with MDD compared to healthy controls (HCL).Methods: Thirty-one adolescents (22 female) with MDD and 36 (23 female) HCL underwent a well-validated emotional processing fMRI paradigm that included sad and happy face stimuli.Results: The MDD group showed significantly less differential activation of the anterior/middle insularcortex (AMIC) in response to sad versus happy faces compared to the HCL group. AMIC also showedgreater functional connectivity with right fusiform gyrus, left middle frontal gyrus, and right amygdala/parahippocampal gyrus in the MDD compared to HCL group. Moreover, differential activation to sad andhappy faces in AMIC correlated negatively with depression severity within the MDD group.Limitations: Small age-range and cross-sectional nature precluded assessment of development of theAMIC in adolescent depression.Conclusions: Given the role of the IC in integrating bodily stimuli with conscious cognitive and emotionalprocesses, our findings of aberrant AMIC function in adolescent MDD provide a neuroscientific rationalefor targeting the AMIC in the development of new treatment modalities.

& 2015 Elsevier B.V. All rights reserved.

1. Introduction

Major depressive disorder (MDD) is considered the fourth leadingglobal cause of disease burden (Ustun et al., 2004) and it is the leadingcause of disability among Americans aged 15 to 44, with about 11% ofU.S. adolescents having a depressive disorder by age 18 (Merikangaset al., 2010). Adolescent-onset depression is related to more sev-ere depressive symptoms and an elevated risk of future recurrent

episodes (Berndt et al., 2000; Hollon et al., 2006; Zisook et al., 2007),compared to adult onset of MDD. Elucidating the neurobiologicalmechanisms of emotional processing early in the disease course mayhelp us more precisely target future treatment of adolescent depres-sion and thereby more efficiently reduce prevalence of and decreasethe propensity for recurrent depressive episodes.

Prior studies examining the neurobiological mechanisms of MDDhave demonstrated that using emotional face tasks in conjunctionwith fMRI reliably activates the neural circuitry implicated in MDD(Fusar-Poli et al., 2009; Joormann and Gotlib, 2006; Mayberg, 1997;Phillips et al., 2003; Stuhrmann et al., 2011). Differences at multiplelevels in the processing of emotional faces have been shown betweenMDD and healthy controls: 1. sensory occipital areas such as the

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Journal of Affective Disorders

http://dx.doi.org/10.1016/j.jad.2015.03.0120165-0327/& 2015 Elsevier B.V. All rights reserved.

n Corresponding author. Tel.: þ1 415 476 9861.E-mail address: colm.connolly@ucsf.edu (C.G. Connolly).1 These authors contributed equally to this work.

Journal of Affective Disorders 178 (2015) 215–223

fusiform gyrus and the superior temporal sulcus, 2. a more extendedsystem supporting the interpretation of facial information, includingthe amygdala, insula and orbitofrontal areas and 3. prefrontal areasengaged in cognitive control (Drevets et al., 2008; Haxby et al., 2000;Mayberg, 1997; Price and Drevets, 2012; Stuhrmann et al., 2011). Theliterature also supports the notion that biases in the processing andinterpretation of human facial emotional expressions as social cuesmay be one of the underlying mechanisms of MDD (Gotlib et al.,2004; Joormann and Gotlib, 2006; Joormann et al., 2007; Kujawa etal., 2011). These biases (detailed below) may contribute to thedevelopment, maintenance, and relapse of depressive symptoms(Foland-Ross and Gotlib, 2012; Lai, 2014; Mathews and MacLeod,2005; Stuhrmann et al., 2011). Importantly, depressed individuals donot exhibit impaired performance on emotion identification, butrather show a specific bias to sad (tending to identify neutral facesas sad) and away from happy faces (tending to identify happy faces asneutral) (Arce et al., 2009; Joormann and Gotlib, 2006). Similarly,depressed adolescents do not show deficits in the ability to accuratelyrecognize sad versus happy facial expressions, but a bias towardsperceiving low-intensity expressions as sad (Schepman et al., 2012).Whether this specific depression-related bias in interpreting sad andhappy facial expressions is due to dysfunctions in sensory regions,regions involved in interpretation of facial expressions and/or cogni-tive control remains unclear.

As previously mentioned the insular cortex is a brain area involvedin the interpretation of facial information and it ahs been highlightedas an integrative hub between the sensory, interpretive and cognitiveregions involved in emotional processing (Avery et al., 2013; Manoliuet al., 2013; Sprengelmeyer et al., 2011), Craig and others (Craig, 2009,2011) (Chang et al., 2013; Menon and Uddin, 2010) have suggestedthat the insular cortex utilizes sensory information from ascendinghomeostatic afferents together with contextual input from othercortical brain areas to form a conscious “feeling” or an emotionalmoment in time (Craig, 2011). The posterior part receives informationabout the physiological state of the body, which is the integrated withinformation from higher order sensory cortices, as well as from theamygdala and the anterior cingulate cortex (ACC) in the middle andanterior part of the insular cortex where emotional awareness,self-recognition and other functions are represented (Craig, 2010;Nieuwenhuys, 2012).

Thus, it should not be surprising that the insular cortex is imp-licated in the pathophysiology of depression and may be dysfunc-tional in individuals with affective disorders. For example, structuraldifferences of the anterior insular cortex have been shown in adultMDD compared to healthy controls (Bora et al., 2012; Peng et al.,2011; Soriano-Mas et al., 2011; Sprengelmeyer et al., 2011;Stratmann et al., 2014; Takahashi et al., 2010) and task-based fMRIstudies have shown both increased (Hamilton et al., 2012; Sliz andHayley, 2012) and decreased activation (Townsend et al., 2010) ofthe anterior insular cortex in adults with MDD compared to healthycontrols.

While neuroimaging in adult depression suggests a road map tounderstanding depressive symptoms in the developing brain, directtranslation of the adult findings of differences in insular cortexactivation as characteristics of adolescent depressive symptoms maybe obscured by the major re-organization of the brain occurring frompuberty to early adulthood (Gogtay et al., 2004). Indeed, develop-mental differences account for much of the variation in fMRI activationpatterns between non-depressed adults and adolescents during emo-tion processing (Cho et al., 2012). Furthermore, cortical thickness ofdistinct brain regions develops differently depending on their organi-zation and function (Huttenlocher and Dabholkar, 1997; Shaw et al.,2008). Specifically, changes in the composition of the insular cortexduring adolescence correspond to improved emotional regulatoryskills and refinement of functioning such as improved impulse-control (Sisk and Zehr, 2005; Steinberg et al., 2009). Paradoxically,

the maturation process of the neural circuits involved in emotionalprocessing (Giedd et al., 2006; Kelly et al., 2009; Romeo and McEwen,2006) may contribute to increased vulnerability to depression duringadolescence (Weir et al., 2012). It is also possible that depressive illnessduring this specific developmental period alters neural pruning, whichnormally supports the acquisition of emotion regulatory skills (Weir etal., 2012). Both scenarios may result in decreased ability to integrateinformation about bodily states with higher-order cognitive andemotional processes in depressed adolescents, which may explainthe depressive symptomatology, the recurrent course, and long-termfunctional disability related to this disorder.

There is accumulating evidence that links insular cortex dys-function to adolescent MDD, including aberrant patterns of cere-bral perfusion (Ho et al., 2013), and activation during emotionalface fMRI paradigms (Ho et al., 2014b; Yang et al., 2010) andaltered functional connectivity (FC) during resting state (Connollyet al., 2013; Cullen et al., 2009) as well as during emotionalprocessing (Ho et al., 2015; Perlman et al., 2012). However, toour knowledge, no fMRI study to date has examined the insularcortex activation and FC during the processing of sad versus happyfacial stimuli in adolescent depression. This would contribute toour understanding of the role of the insular cortex specifically inrelation to how aberrant integration of sensory and cognitiveprocessing support the emotional biases in adolescent depression.

Thus, the aim of the present study is to investigate the insularcortex and its related circuitry during sad and happy face proces-sing in adolescents with MDD compared to well-matched healthycontrols (HCL). We used an emotional face processing task that hasbeen well-validated in healthy, depressed, anxious, and at-riskadolescents (Beesdo et al., 2009; Monk et al., 2008; Nelson et al.,2003; Pine et al., 2004; Roberson-Nay et al., 2006), which weadapted by substituting sad for angry faces. First, based on thereviewed literature, we hypothesized that the insular cortex,especially the anterior part would show aberrant activation inMDD in response to sad versus happy faces compared to HCLcorresponding to decreased ability to integrate information aboutbodily states with cognitive and emotional processes, (i.e., toprocess and differentiate sad and happy emotions). Second, basedon previous studies examining face processing in MDD(Stuhrmann et al., 2011), we theorized that FC between the insularcortex and other brain areas involved in the different stages offacial emotion processing, including the fusiform gyrus, limbicstructures, and frontal regions, would be altered. We also hypothe-sized that insular cortex activation and FC would correlate withdepression symptom severity.

2. Methods

2.1. Participants

We recruited a total of 81 participants: 43 HCL and 38 withMDD. One participant from each group was dropped due tomissing behavioral data and a further 12 (six from each group)were dropped due to excessive motion during scanning. The finalgroups consisted of 31 (22 female) MDD subjects and 36 (23female) HCL.

All MDD subjects met full criteria for a current primary diagnosis ofMDD according to the DSM-IV (American Psychiatric Association, 2000)and were excluded from the study if they had a primary diagnosis ofany other psychiatric disorder (for full description of exclusion criteria,see Supplement). All adolescents with MDD were naïve to anypsychotropic medications except for five: four had stopped theirmedication more than a year prior to scanning and one subject fourmonths prior to scanning. HCL adolescents were excluded from thestudy for any of the exclusionary criteria for the depressed group, as

E. Henje Blom et al. / Journal of Affective Disorders 178 (2015) 215–223216

well as any current or lifetime Axis I psychiatric disorder or any familyhistory of mood or psychotic disorders in first- or second-degreerelatives. See Table 1 for a full summary of the clinical and demographiccharacteristics of the sample. The institutional review boards of theUniversity of California (UC) San Diego, UC San Francisco, RadyChildren's Hospital, and The County of San Diego approved this study.Participants provided written informed assent and their parent/legalguardian proved written informed consent. All the participants werefinancially compensated.

2.2. Assessment

For potentially depressed participants, an MDD diagnosis wasvalidated with the Schedule for Affective Disorders and Schizo-phrenia for School-Age Children-Present and Lifetime Version Task(Kaufman et al., 1997). The presence of Axis I disorders in HCL wasdetermined using the Diagnostic Interview Schedule for ChildrenVersion 4.0 (Shaffer et al., 2000) and the Diagnostic PredictiveScale (Lucas et al., 2001). In all subjects, depression severity wasmeasured with the Children's Depression Rating Scale-Revised(CDRS-R) (Poznanski, 1996) and Beck Depression Inventory-II(BDI-II) (Beck et al., 1996); anxiety symptoms with the Multi-dimensional Anxiety Scale for Children (MASC) (March et al.,1997), and level of global functioning by the Children's GlobalAssessment Scale (CGAS) (Dyrborg et al., 2000). The MDD and HCLgroups were well-matched on age, gender, general IQ (WASI)(Wechsler, 1999), pubertal status (Tanner, 1963), and socioeco-nomic status (Hollingshead Two Factor Index of Social Position,HSP)(Hollingshead, 1957). A full description of the test battery isavailable elsewhere (Connolly et al., 2013; Ho et al., 2015; Ho et al.,2013; Ho et al., 2014; Yang et al., 2010)

2.3. Experimental stimuli and task

All subjects underwent a well-validated emotional face processingfMRI paradigm for adolescents (Beesdo et al., 2009; Monk et al., 2008;Nelson et al., 2003; Pine et al., 2004; Roberson-Nay et al., 2006).However, we modified the task and replaced angry faces with sadfaces. In brief the task consisted of two phases: encoding and recall.The encoding phase required participants to view 32 adult actorsdepicting four sad, happy, fear, and neutral (8 instances each). Thepairings of actor with face-emotion were randomized across partici-pants; thus, participants saw the same set of 32 actors but each actordisplayed a different emotion for each participant. All together, theencoding phase constituted a 160 trial run that lasted 14min 20 sec.The 160 trials were divided into four 40-trial epochs that were furthersubdivided into four blocks, one for each of the four instructional sets:(1) the sadness level of the face (“How sad is the face?”); (2) theparticipant's emotional reaction to the face (“How sad does the facemake you feel?”); (3) the size of a non-emotional facial feature (“Howwide is the nose?”); (4) passive viewing of the face. With theexception of the passive viewing condition, subjects made ratingsaccording to each instruction based on a 4-point scale. Each instruc-tion block consisted of 10 pseudo-randomly presented trials: eight ofwhich were faces plus two fixation events. Instructions lasted3000 ms and were presented prior to each block. Each face imagewas presented for 4000 ms, during which participants made theirresponses. Each event (face or fixation) was followed by a variableinter-trial interval of 750–1250ms. The same image of each actor waspresented to each participant for each of the four viewings. Fig. 1illustrates the task. The recall phase took the form of an unannouncedmemory test that occurred outside the scanner at the end of thescanning session. The image set used in the test consisted of 24previously seen actors and 24 novel actors, each depicting a neutralexpression, which participants rated as old or new.

Table 1Characteristics of depressed subjects and healthy controls.

Characteristic Control a MDD a Statistic b,c p Value Effect size (95% CI) d

Number of participants recruited (n) 43 38Overall recruitment gender (M/F) 18/25 10/28Rejected due to missing data (n) 1 1Rejected due to excessive motion and outlier count (n)* 6 6Number of participants (n) 36 31Gender (M/F) 13/23 9/22 χ2(1.00)¼0.13 0.72Age at time of scan (years) 16.170.2 (13.1–17.9) 1670.3 (13.1–18.1) t(56.38)¼0.41 0.69Hollingshead socioeconomic score 29716.3 (0–77)† 30717.8 (11–70)† W¼458 0.21Tanner score 470.7 (3–5)† 470.7 (3-5)† W¼535 0.77Wechsler abbreviated scale of intelligence 107.273.4 (84�204) 100.772 (82–120) t(55.69)¼1.66 0.1Children's Global Assessment Scale 9075.9 (75–100)† 65713.3 (27–85)† W¼1107 o0.001 PS(MDD4NCL)¼0.004 (0, 0.03)Children's Depression Rating Scale (Standardized) 33.670.9 (30–55) 73.371.4 (61–85) t(51.64)¼�23.96 o0.001 g¼�5.8 (�6.91, �4.69)Beck Depression Inventory II 2.970.6 (0–12) [1] 29.171.7 (13–45) t(37.15)¼�14.70 o0.001 g¼�3.56 (�4.34, �2.78)Multidimensional Anxiety Scale for Children (Standardized) 4371.6 (26–61) [2] 59.871.7 (40–78) t(61.92)¼�7.35 o0.001 g¼�1.78 �2.35, �1.21)

Comorbid Diagnoses in the MDD GroupNo current comorbid diagnoses e 25Generalized anxiety disorder (Current) 9Generalized anxiety disorder (Past) 8Specific phobia (Current) 3Specific phobia (Past) 3Anxiety disorder not otherwisespecified (Current) 1Anxiety disorder not otherwise specified (Past) 1Posttraumatic stress disorder (Current) 2Posttraumatic stress disorder (Past) 4

Abbreviations: CI, Confidence interval; SEM, standard error of the mean; MAD, median absolute deviation; PTSD, Post-traumatic stress disorder; ADHD, Attention deficithyperactivity disorder; M, male; F, female.

a Mean7SEM (min–max) or median7MAD (min – max) if indicated by †. The optional number in [] indicates the number of missing data points.b Statistic: W, Wilcox rank sum test; χ2, χ2 test for equality of proportions; t, Welch t test.c Statistics for clinical scales refer only to participants with behavioral data and those surviving motion and outlier correction.d Effect size: g, Hedge's g; PS, Probability of superiority. Effect sizes are provided only where po0.1.e Refers to the absence of current and past diagnoses of: PTSD, ADHD, enuresis, conduct disorder, and oppositional defiance disorder.

E. Henje Blom et al. / Journal of Affective Disorders 178 (2015) 215–223 217

2.4. MR data acquisition and preprocessing

Scanning was performed on a 3 T GE MR750 (Milwaukee, WI).One 14 min 20 sec T2n-weighted echo planar image (EPI) scan(430 volumes TR/TE¼2 s/30 ms, flip angle¼901, 64�64 matrix,3�3�3 mm3 voxels, 40 axial slices) was acquired. A T1-weightedscan (TR/TE¼8.1 ms/3.17 ms, flip angle¼121, 256�256 matrix,1�1�1 mm3 voxels, 168 sagittal slices) was acquired to permitspatial normalization and functional localization.

2.5. MR data analysis

Analyses were conducted using AFNI (Cox, 1996) and FSL (Smithet al., 2004). T1-weighted images were skull-stripped and transformedto MNI152 space using an affine transform (Jenkinson et al., 2002;Jenkinson and Smith, 2001) followed by nonlinear refinement(Andersson et al., 2007a; Andersson et al., 2007b). EPI images wereslice-time and motion corrected, aligned to the T1 images (Saad et al.,2009), smoothed with a 4.2 mm full-width at half-maximum (FWHM)Gaussian filter, grand-mean scaled, and transformed to MNI152 spaceat 3�3�3mm3 resolution. Censoring of outlier volumes and thosewith excessive motion was performed (see Supplement for details).Preprocessed time-series were subjected to generalized least squaresregression that estimates the serial correlation structure of the noisewith an ARMA(1, 1) model. Time-series of interest were created suchthat they started at the onset of a face-emotion image and endedwhen the next image was displayed. Four regressors-of-interest weredefined: happy, fearful, neutral, and sad. Regressors were convolvedwith a gamma-variate prototypical hemodynamic response function(HRF) (Boynton et al., 1996) and normalized to a peak amplitude of 1.All time-points not accounted for by these regressors constituted thebaseline. Six nuisance motion regressors (three translation and threerotational) and a 2nd order Lagrange polynomial (accounting for slowsignal drift) were included in the baseline. General linear tests (GLTs)of each pair of face-emotions (e.g., sad versus happy) were computedfor each participant. Finally, brain activation was operationally definedas percentage signal change relative to baseline.

Given our hypotheses on sad versus happy face processing, wefocused our analyses on GLTs involving sad and happy face stimuli (i.e.,happy versus sad, sad versus neutral, happy versus neutral). Between-group voxel-wise analyses of the GLTs were accomplished using linearmixed effects models (LMEs) implemented in R (R Development CoreTeam, 2014) where participant was treated as a random effect. Follow-up post-hoc t-tests were conducted to ascertain the source of differ-ences in regions-of-interest identified from this analysis.

2.6. Functional connectivity analysis

We assessed the functional connectivity (FC) between theanterior middle insular cortex (AMIC) and the whole brain usingthe psychophysiological interaction (PPI) method (Friston et al.,1997) using the same method as described in our previouslypublished work (Fonzo et al., 2010; Ho et al., 2015; Ho et al.,2014; Perlman et al., 2012; Simmons et al., 2008). See theSupplement for details. Briefly, the interaction time-series for eachGLT were convolved with a gamma-variate function HRF (Boyntonet al., 1996). The 10 regressors detailed above, the seed (AMIC)time-series, and the interaction regressor (happy versus sad) werethen subjected to multiple linear regression analysis. Correlationcoefficients representing the correlation between each interactionregressor and every other voxel were calculated and converted toz-scores using Fisher's r-to-z transform. These resulting Z-scoreswere then subject to LMEs, as outlined above, to assess differencesin connectivity related to between-group differential face-emotionprocessing.

2.7. Voxel-wise thresholds and correction for multiple comparisons

For both task-related and functional connectivity analyses, allsignificant voxels were required to pass a voxel-wise statistical thre-shold (F(1, 65)¼3.99, po0.05, uncorrected) and were further requiredto be part of a cluster of at least 1669 μL to control for multiplecomparisons. The cluster threshold was determined using a MonteCarlo simulation that, in combination with the voxel-wise threshold,resulted in a 5% probability of that cluster surviving due to chance (i.e.,cluster-wise po0.05).

2.8. Behavioral analysis

We used the d' measure from signal detection theory to assessparticipants' ability to correctly distinguish previously viewed actors(“signal”) from those never seen before (“noise”). Between-groupdifferences in false alarm rate were examined using a Welch t-test.A one-sample t-test determined whether overall recall differedsignificantly from chance. An ANOVA was conducted to determinewhether group, face-emotion, and their interaction influenced recall.For a more detailed description see the supplement.

2.9. Demographic and clinical scales analysis

Between-group differences in age, WASI, BDI-II, CDRS-R and MASCwere assessed using Welch t-tests. Effect sizes for these measures

Fig. 1. The experimental design for the emotional face processing task.

E. Henje Blom et al. / Journal of Affective Disorders 178 (2015) 215–223218

were determined using Hedge's g (Hedges & Olkin 1985). Wilcox ranksum test was used to assess group differences on Tanner stage, CGAS,and HSP, with effect sizes computed using the probability of super-iority (PS; range 0–1) (Mahwah, 2005 ). PS represents the probabilitythat a randomly selected MDD participant reported a greater value onthe corresponding measure than a randomly selected control. Con-fidence intervals for the PS were computed using a bootstrap method(Ruscio and Mullen, 2012). Differences in the number of participantsper group and gender ratios were assessed using χ2 test of equalproportions.

2.10. Correlational analysis

Within the MDD group, the relationship between BDI-II and thecontrast of activation in the AMIC to happy and sad faces wasexamined using Spearman's rank correlation test. A similar investiga-tion was conducted to assess the relationship between BDI-II and ageof MDD onset with the average Z-score from each 3 regions (rightfusiform gyrus (FG), left middle frontal gyrus (MFG), and rightamygdala/parahippocampal gyrus (PHG); see results below) showingconnectivity differences with the AMIC seed as identified by the PPIanalysis. Bonferroni correction for multiple comparisons was applied:p¼0.05/2¼0.025 for the correlation tests involving the happy-sadcontrast differences in the AMIC and p¼0.05/6¼0.008 for the PPIresults.

3. Results

3.1. Demographics and clinical scales

MDD and HCL groups did not differ significantly on age, gender,HSP, Tanner stage, or WASI (all p40.1). Higher depression symp-tom scores on the BDI-II and CDRS-R (all po0.05) and higher anxietyscores on MASC (po0.001) and lower levels of global functioningwith CGAS (po0.001) were observed in the MDD compared to theHCL. See Table 1.

3.2. Behavioral task results

No group differences in false alarm rate were present (t(60.93)¼0.06, p40.1). Collapsing across groups, the overall d' significantlydiffered from 0 (t(66)¼17.09, po0.001), indicating that participantswere better at recalling previously seen actors than chance levels.Upon examining the effect of group, face-emotion and their interac-tion on d', there was no effect of group (F(1, 260)¼0.23, p40.1) orgroup� face-emotion interaction (F(1, 260)¼0.73, p40.1). However,face-emotion was significant (F(1, 260)¼9.85, po0.001) with follow-uppairwise t-tests indicating that recall for neutral faces was greater thanall non-neutral faces (all po0.05).

3.3. fMRI task Results

We found several regions showing between-group differences inthe contrast happy versus sad faces (Table S1). First, the happy-sadcomparison revealed significant differences in a cluster composed ofthe left claustrum extending laterally to include anterior and middleinsular cortex (AMIC) (2322 μL, x¼31, y¼�7, z¼0, average F(1, 65)¼6.00; see Fig. 2). The happy–sad contrast also showed significantdifferences in the right lingual gyrus (LG), bilateral FG, right thalamus,and right declive of the cerebellum (Table S1).

Post-hoc tests examining the directionality of these effects(Table S2), showed the following: the left AMIC from the happy–sad contrast showed lower activation levels in the MDD comparedto the HCL group. Compared to HCL, the MDD group exhibitedreduced activation in the right LG, bilateral FG and right declive of

the cerebellum, while exhibiting greater activation in the rightthalamus during happy versus sad face processing. Within theMDD group, sad faces elicited stronger activation than happy facesin the left and right FG and the right declive of the cerebellum.

3.4. Functional connectivity results

Among the regions identified by the task-related analysis(Table 2), we specifically focused on the AMIC region. The leftAMIC seed identified in the happy–sad contrast was positivelyconnected to the right FG, left MFG, and right amygdala/PHG in theMDD group but was negatively connected to these same regions inthe HCL group (Fig. 3).

3.5. Correlational analysis results

Those MDD individuals who showed relatively less activationleft AMIC cluster identified from the happy–sad contrast reportedhigher BDI-II scores (ρ¼�0.58, po0.001). No significant correla-tions between BDI-II or age of MDD onset and the strength offunctional connectivity of the AMIC seed and connected regions(i.e., right FG, left MFG, or right amygdala/PHG) were observedwithin the MDD group (all p40.1).

4. Discussion

The present study aimed to elucidate the activation patterns of theinsular cortex in response to sad versus happy facial exposure usingfMRI in a sample of adolescents diagnosed with MDD compared towell-matched HCL. Our investigation yielded several important find-ings. First, the differential activation in the left anterior/middle insularcortex (AMIC) during sad versus happy face processing was lesspronounced in the MDD group compared to HCL (Fig. 2). Second, inthe MDD group, there was greater functional connectivity betweenAMIC and right fusiform gyrus, left middle frontal gyrus, and rightamygdala/parahippocampal gyrus (PHG) during sad versus happy faceprocessing. Third, reduced differential activation to sad and happyfaces in this same AMIC region correlated negatively with depressionsymptom severity scores in the depressed adolescents. Taken together,these findings are consistent with a dysfunction of the AMIC whenprocessing emotional information in adolescents with MDD. More-over, given the role of the AMIC integrating bodily states andemotions, this finding raises the possibility that adolescents withMDD show poorer integration of emotion processing with bodily sen-sations.

Fig. 2. Between-group differences in mean percent signal change during thehappy-sad condition in a cluster composed of the left claustrum extending laterallyto include primarily anterior and middle insula.

E. Henje Blom et al. / Journal of Affective Disorders 178 (2015) 215–223 219

Our first finding that significantly less differential activation in theanterior/middle insular cortex to sad versus happy face stimuli wereseen in the depressed group compared to HCL may correspond to adecreased ability in adolescents with MDD to differentiate betweenthese differently valenced emotional stimuli and thereby contribute to

difficulties in interpreting social cues. We also found that the left AMICshowed less activation to sad faces in the MDD group compared to theHCL, which is contradicting previous findings in adult MDD, whereanterior insular cortex is shown relatively hyperactive during emotionprocessing (Hamilton et al., 2012; Sliz and Hayley, 2012). Thisdiscrepancy suggests that the decreased activation of AMIC inadolescent depression in response to sad faces may be developmen-tally specific and corresponds to a limited tendency to successfullyprocess and interpret sad emotions in MDD adolescents. Indeed,recent work suggests that ongoing development of the anteriorinsular cortex during adolescence may differentially impact cognitiveand affective functioning of this structure in adolescents compared toadults (Dupont et al., 2003; Smith et al., 2014). This finding may alsobe explained by the theory that adolescents with MDD show lessactivation to mood congruent stimuli, analogous with previousfindings showing increase attentional deficit in MDD subjects whenexposed to mood incongruent stimuli (Erickson et al., 2005).

Our second finding was a positive functional connectivity (FC) inthe MDD group between the left AMIC cluster to other key emotionprocessing areas: namely, the right FG, right amygdala/parahippocam-pus gyrus (PHG) and left MFG. In contrast, the functional connectionsbetween the AIMC and these regions were negative in the HCL.Importantly, these findings imply that the integrative function of theAMIC in relation to these areas, representing different aspects of facialemotion processing, is altered in adolescent MDD. Further supportingthis idea is our finding that the FG, a part of the visual systemspecialized for facial recognition (Kanwisher et al., 1997), showed agreater activation for sad than happy faces in the depressed group. Thisimplies that aberrant activation in AMIC and FG, and the strength oftheir functional connection may contribute to biased processing of sadstimuli, as previously described in adolescent depression (Schepman etal., 2012). The positive FC between left AMIC and the amygdala in ourdepressed sample may indicate dysfunctional processing of bodilysensations to emotional experiences, and more specifically implyincreased interaction between perception of emotional cues in theamygdala and increased arousal in the insula, which may be corre-sponding to the symptomatology of general emotion dys-regulation inadolescent MDD. Finally, the positive FC between AMIC and the MFG,in the adolescent MDD groupmay reflect a compensatory effort of top-down cognitive control when the insular cortex is activated in responseto emotionally valenced stimuli. Interestingly, studies in adult depres-sion showed that increasing levels of sadness were associated withincreased activation of anterior insula and decreased activation ofprefrontal regions, but that this pattern reverses with recovery fromdepression (Kennedy et al., 2001; Mayberg et al., 1999).

Lastly, we report that more severe depression symptomatologywithin the MDD group was significantly associated with less of a

Table 2Mean connectivity z-scores between the left insular seed and functionally connected brain structures in the happy–sad contrast in the depressed group (MDD) and healthycontrols (HCL).

Structure Hemisphere Volume (μL) Center of mass Average F value a Mean connectivity z-score

X Y Z MDD HCL

Happy-sad Contrast: L Insula seedDeclive L 14,040 20 60 �19 5.93 0.02 �0.01Fusiform gyrus R 13,797 �28 56 �13 6.18 0.02 �0.02Middle frontal gyrus L 13,068 42 �18 30 6.19 0.02 �0.01Precuneus L 5994 12 76 37 5.85 0.02 �0.01Culmen R 5373 �3 41 �28 6.57 0.02 �0.01Inferior parietal lobule L 3672 58 41 26 5.98 0.02 �0.02Precuneus R 2997 �18 67 25 5.43 0.02 �0.02Amygdala/parahippocampal Gyrus R 2538 �29 1 �29 6.15 0.02 �0.02Cingulate gyrus L 2322 12 16 35 7.00 0.02 �0.02precuneus R 2160 �21 81 41 6.00 0.02 �0.01

a F(1, 65)

Fig. 3. PPI results of the left anterior/middle insular cortex/claustrum seedidentified in the happy–sad contrast in the depressed adolescents (MDD) and inthe healthy controls (HCL).

E. Henje Blom et al. / Journal of Affective Disorders 178 (2015) 215–223220

difference in activation between sad and happy faces in the AMIC.This result may have implications for the previously describedbiased processing towards sad stimuli in depressed individuals(Joormann and Gotlib, 2006; Schepman et al., 2012). Studiesin healthy adolescents show that insular cortex thickness(Churchwell and Yurgelun-Todd, 2013; Shaw et al., 2008) andconnectivity patterns continue to develop into young adulthood(McRae et al., 2012). We suggest that early onset of depressionincreases the impact of MDD on AMIC development and itsfunction (Gaffrey et al., 2013) with consequences for the develop-ment of emotion regulation skills during adolescence.

The major limitations of this study are the sample's relativelysmall age-range and the cross-sectional design of this study, whichlimit the possibility of assessing the development of the insularcortex in adolescent depression. Future studies of longitudinaldesign following the structural and functional development of theinsular cortex in depressed adolescents compared to healthycontrols are needed to address these limitations and to confirmthe reproducibility of our findings.

Our findings provide a neuroscientific rationale for testing newtreatment modalities on the level of integrating bodily stimuli toconscious cognitive and emotional processes. Adaptively switchingfrom ruminative and self-referential processing to an interoceptivefocus has been suggested to rely on the insular cortex (Menon andUddin, 2010) and difficulty in switching between these brain states hasbeen suggested as a potential contributing mechanism of depression(Chang et al., 2013; Manoliu et al., 2013). Furthermore, the practice ofswitching from rumination to interoceptive awareness has beenrelated to changes in insular activation patterns in fMRI studies ofhealthy (Farb et al., 2013) and depressed (Farb et al., 2010; Farb et al.,2012; Paul et al., 2013) adults. Thus, an important future direction ofresearch may be to investigate the effects of practicing how to directattention toward momentary bodily and sensory experience foremotion regulation on depression symptom severity in adolescentMDD (Henje Blom et al., 2014). This practice could provide analternative strategy to therapies requiring top-down cognitive controlefforts. The effect of such training on insular cortex activation and FCduring the processing of sad and happy faces wouldmore clearly link apossible practice effect to insular cortex function and the previouslydescribed biased emotion processing in adolescent depression.

5. Conclusion

This study showed less functional differentiation in response tosad versus happy faces in depressed adolescents compared towell-matched healthy controls. These finding differed from pre-vious reports in depressed adults and suggests that MDD may alterthe developmental trajectory of the AMIC in adolescents. Further-more, we report that functional connectivity between the AMICand key brain regions (e.g., the MFG and FG) involved in facialemotion processing and the regulation there of was increasedin the depressed adolescents as compared to healthy controls. Wealso found greater depression severity correlated significantly withreduced differential responses to sad and happy faces in the AMIC.We theorize that the developmental trajectory of the AMIC and itsrelated circuitry is impacted by early onset depression and thatthis may impair the acquisition of emotional regulatory skillsduring adolescence. Together, our results suggest that this regionmay serve as a target in the development of more effective futureinterventions for adolescent depression.

Role of funding

This work was supported by the Swedish Research Council (E.H.B., Grantnumber 350–2012–303); the Swedish Society of Medicine (E.H.B., Grant number

SLS244671) and the Sweden American Association to EHB; the NARSAD foundationand the National Institute of Mental Health (T.T.Y., Grant numbers 7R01MH085734,3R01MH085734-02S1 and R01MH085734-05S1), (K.Z.L., grant number K01MH09-7978) and T.C.H from American Foundation of Suicide Prevention PDF-1–064-13.The funding agencies played no role in the design and conduct of the study;collection, management, analysis, and interpretation of the data; and preparation,review, or approval of the manuscript.

Conflict of interestNone.

Ethical standards

The authors assert that all procedures contributing to this work comply with theethical standards of the relevant national and institutional committees on humanexperimentation and with the Helsinki Declaration of 1975, as revised in 2008.

AcknowledgmentsWe want to thank the teenagers who have participated in the study. We are

grateful to Daniel Pine for providing us with the fMRI task and to both Daniel Pineand Ellen Leibenluft for valuable input. We also wish to thank Kevin Han, MaryLuna, Nisreen Mobayed, Audrey Fortezzo for their help in the acquisition andmanagement of the data for this study.

Appendix A. Supporting information

Supplementary data associated with this article can be found inthe online version at http://dx.doi.org/10.1016/j.jad.2015.03.012.

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