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Behavioral and emotional dysregulation trajectories marked by prefrontal–amygdala function in symptomatic youth

Published online by Cambridge University Press:  27 January 2014

M. A. Bertocci ,
G. Bebko ,
T. Olino ,
J. Fournier ,
A. K. Hinze ,
L. Bonar ,
J. R. C. Almeida ,
S. B. Perlman ,
A. Versace  and
M. Travis
...Show all authors
M. A. Bertocci*
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
G. Bebko
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
T. Olino
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
J. Fournier
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
A. K. Hinze
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
L. Bonar
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
J. R. C. Almeida
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA Alpert Medical School, Brown University, Providence, RI, USA
S. B. Perlman
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
A. Versace
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
M. Travis
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
M. K. Gill
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
C. Demeter
Affiliation:
University Hospitals Case Medical Center/Case Western Reserve University, Cleveland, OH, USA
V. A. Diwadkar
Affiliation:
Department of Psychiatry and Behavioral Neuroscience, Wayne State University, Detroit, MI, USA
R. White
Affiliation:
Department of Psychiatry and Behavioral Neuroscience, Wayne State University, Detroit, MI, USA
C. Schirda
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
J. L. Sunshine
Affiliation:
University Hospitals Case Medical Center/Case Western Reserve University, Cleveland, OH, USA
L. E. Arnold
Affiliation:
Department of Psychiatry, Ohio State University, Columbus, OH, USA
S. K. Holland
Affiliation:
Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
R. A. Kowatch
Affiliation:
The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
B. Birmaher
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
D. Axelson
Affiliation:
Department of Psychiatry, Ohio State University, Columbus, OH, USA
E. A. Youngstrom
Affiliation:
Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
R. L. Findling
Affiliation:
University Hospitals Case Medical Center/Case Western Reserve University, Cleveland, OH, USA Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
S. M. Horwitz
Affiliation:
Department of Child Psychiatry, New York University School of Medicine, New York, NY, USA
M. A. Fristad
Affiliation:
Department of Psychiatry, Ohio State University, Columbus, OH, USA
M. L. Phillips
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
*
*Address for correspondence: M. A. Bertocci, Ph.D., Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O'Hara Street, Loeffler Building, Room 203, Pittsburgh, PA 15213, USA. (Email: bertoccima@upmc.edu)
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Abstract

Background

Neuroimaging measures of behavioral and emotional dysregulation can yield biomarkers denoting developmental trajectories of psychiatric pathology in youth. We aimed to identify functional abnormalities in emotion regulation (ER) neural circuitry associated with different behavioral and emotional dysregulation trajectories using latent class growth analysis (LCGA) and neuroimaging.

Method

A total of 61 youth (9–17 years) from the Longitudinal Assessment of Manic Symptoms study, and 24 healthy control youth, completed an emotional face n-back ER task during scanning. LCGA was performed on 12 biannual reports completed over 5 years of the Parent General Behavior Inventory 10-Item Mania Scale (PGBI-10M), a parental report of the child's difficulty regulating positive mood and energy.

Results

There were two latent classes of PGBI-10M trajectories: high and decreasing (HighD; n = 22) and low and decreasing (LowD; n = 39) course of behavioral and emotional dysregulation over the 12 time points. Task performance was >89% in all youth, but more accurate in healthy controls and LowD versus HighD (p < 0.001). During ER, LowD had greater activity than HighD and healthy controls in the dorsolateral prefrontal cortex, a key ER region, and greater functional connectivity than HighD between the amygdala and ventrolateral prefrontal cortex (p's < 0.001, corrected).

Conclusions

Patterns of function in lateral prefrontal cortical–amygdala circuitry in youth denote the severity of the developmental trajectory of behavioral and emotional dysregulation over time, and may be biological targets to guide differential treatment and novel treatment development for different levels of behavioral and emotional dysregulation in youth.

Keywords

Behavioral and emotional dysregulationemotion regulationemotional n-backfunctional magnetic resonance imaginglatent class growth analysisyouth
Type
Original Articles
Information
Psychological Medicine , Volume 44 , Issue 12 , September 2014 , pp. 2603 - 2615
Copyright
Copyright © Cambridge University Press 2014 

Introduction

Psychiatric disorders characterized by behavioral and emotional dysregulation in youth are often difficult to disentangle nosologically. Behavioral and emotional dysregulation is common among youth seeking treatment, and youth with these behaviors may be diagnosed with a variety of disorders such as bipolar spectrum disorder (BPSD), depressive disorders, attention deficit/hyperactivity disorder (ADHD) and disruptive disorders, or remain undiagnosed (Lewinsohn et al. Reference Lewinsohn, Klein and Seeley2000; Brotman et al. Reference Brotman, Schmajuk, Rich, Dickstein, Guyer, Costello, Egger, Angold, Pine and Leibenluft2006; Stringaris & Goodman, Reference Stringaris and Goodman2009; Findling et al. Reference Findling, Youngstrom, Fristad, Birmaher, Kowatch, Arnold, Frazier, Axelson, Ryan, Demeter, Gill, Fields, Depew, Kennedy, Marsh, Rowles and Horwitz2010). The high rates of co-morbid disorders add challenges to diagnosis and treatment. These factors suggest that behavioral and emotional dysregulation is not well characterized using current diagnostic nomenclature, and may represent a behavioral dimension(s) that cut across different diagnostic categories. Adopting a dimensional approach to the study of behavioral and emotional dysregulation in youth parallels the approach advocated by the National Institute of Mental Health Research Domain Criteria (RDoC; Insel et al. Reference Insel, Cuthbert, Garvey, Heinssen, Pine, Quinn, Sanislow and Wang2010).

Identifying objective biomarkers that reflect pathophysiological processes underlying behavioral and emotional dysregulation (Charney & Babich, Reference Charney and Babich2002; Hasler et al. Reference Hasler, Drevets, Gould, Gottesman and Manji2006) may ultimately provide biological targets to guide treatment choice and treatment development for different levels of severity of behavioral and emotion dysregulation in youth (Phillips & Frank, Reference Phillips and Frank2006). The use of neuroimaging to identify measures of dysfunctional neural circuitry associated with behavioral and emotional dysregulation may be a way to identify such biomarkers. Combining neuroimaging with methodologies such as latent class growth analysis (LCGA) that can identify subgroups of youth defined by different underlying trajectories of behavioral and emotional dysregulation over time may provide a way to identify biomarkers associated with these different subgroups. This approach may lead to better understanding of pathophysiological processes underlying different trajectories of behavioral and emotional dysregulation in youth.

The Longitudinal Assessment of Manic Symptoms (LAMS) study (for a complete description, see Horwitz et al. Reference Horwitz, Demeter, Pagano, Youngstrom, Fristad, Arnold, Birmaher, Gill, Axelson, Kowatch, Frazier and Findling2010) is a multisite study of youth initially aged 6–12 years who at enrollment were seeking treatment for behavioral and emotional dysregulation. The aim of LAMS is to assess relationships among longitudinal symptom course, clinical, and functional outcomes in youth with behavioral and emotional dysregulation who have a variety of diagnoses. For 5 years, youth in the first LAMS phase (LAMS1) were assessed every 6 months in order to characterize developmental trajectories on a range of clinical dimensions. One especially important measure is the Parent General Behavior Inventory 10-Item Mania Scale (PGBI-10M), a 10-item parental report of observed child behaviors associated with difficulty regulating positive mood and energy (Youngstrom et al. Reference Youngstrom, Frazier, Demeter, Calabrese and Findling2008). Families with PGBI-10M scores of ⩾12 plus a demographically matched subset of lower scoring youth were invited to participate in LAMS1. At baseline assessment, PGBI-10M scores were associated with the risk of having BPSD (Frazier et al. Reference Frazier, Youngstrom, Horwitz, Demeter, Fristad, Arnold, Birmaher, Kowatch, Axelson, Ryan, Gill and Findling2011), behavioral extremes, poor overall functioning, and high risk for developing severe psychopathology other than BPSD (e.g. other mood disorders, anxiety disorders, ADHD and disruptive disorders) (Findling et al. Reference Findling, Youngstrom, Fristad, Birmaher, Kowatch, Arnold, Frazier, Axelson, Ryan, Demeter, Gill, Fields, Depew, Kennedy, Marsh, Rowles and Horwitz2010; Horwitz et al. Reference Horwitz, Demeter, Pagano, Youngstrom, Fristad, Arnold, Birmaher, Gill, Axelson, Kowatch, Frazier and Findling2010). The second phase (LAMS2) is an ongoing study that includes neuroimaging and neurocognitive evaluations. A goal of LAMS2 is to examine relationships between functional integrity of neural circuitry supporting emotion regulation (ER) and developmental trajectories of behavioral and emotional dysregulation in youth.

ER neural circuitry includes regions implicated in early appraisal of emotional information during ‘automatic’ or implicit sub-processes of ER: rostral and subgenual regions of the anterior cingulate cortex [ACC; Brodmann areas (BA) 24/25, respectively], the orbitofrontal cortex (BA 11) and dorsomedial prefrontal cortex (medial BA 9/10); and regions involved in more demanding executive and attentional control processes that support effortful, ER processes: the dorsal ACC (dACC; BA 24/32), ventrolateral prefrontal cortex (VLPFC; BA 47) and dorsolateral prefrontal cortex (DLPFC; BA 44/46 and lateral BA 9) (Ochsner & Gross, Reference Ochsner and Gross2005; Phillips et al. Reference Phillips, Ladouceur and Drevets2008). An increasing number of studies have examined ER neural circuitry in youth characterized by behavioral and emotional dysregulation (Pavuluri et al. Reference Pavuluri, O'Connor, Harral and Sweeney2008; Passarotti et al. Reference Passarotti, Sweeney and Pavuluri2010b ; Ladouceur et al. Reference Ladouceur, Farchione, Diwadkar, Pruitt, Radwan, Axelson, Birmaher and Phillips2011; Rich et al. Reference Rich, Carver, Holroyd, Rosen, Mendoza, Cornwell, Fox, Pine, Coppola and Leibenluft2011). For example, abnormally reduced DLPFC and VLPFC activity was reported during a variety of ER tasks, including emotional-face gender labeling, response inhibition and an emotional color–word task in youth with BPSD versus healthy control youth (Pavuluri et al. Reference Pavuluri, O'Connor, Harral and Sweeney2008; Passarotti et al. Reference Passarotti, Sweeney and Pavuluri2010a ; Ladouceur et al. Reference Ladouceur, Farchione, Diwadkar, Pruitt, Radwan, Axelson, Birmaher and Phillips2011). Reduced connectivity relative to healthy youth within prefrontal cortical–amygdalar circuitry was shown in bipolar youth during ER tasks, including a working memory task with emotional distracters, gender labeling and emotional-face identification (Rich et al. Reference Rich, Fromm, Berghorst, Dickstein, Brotman, Pine and Leibenluft2008; Ladouceur et al. Reference Ladouceur, Farchione, Diwadkar, Pruitt, Radwan, Axelson, Birmaher and Phillips2011; Passarotti et al. Reference Passarotti, Ellis, Wegbreit, Stevens and Pavuluri2012); in depressed youth during an ER task (Perlman et al. Reference Perlman, Simmons, Wu, Hahn, Tapert, Max, Paulus, Brown, Frank, Campbell-Sills and Yang2012); and in youth at risk for psychosis during emotion processing (Gee et al. Reference Gee, Karlsgodt, van Erp, Bearden, Lieberman, Belger, Perkins, Olvet, Cornblatt, Constable, Woods, Addington, Cadenhead, McGlashan, Seidman, Tsuang, Walker and Cannon2012).

Our overarching goal in the present study was to identify biomarkers associated with trajectories of behavioral and emotional dysregulation in LAMS youth, to lead to a better understanding of pathophysiological processes underlying these trajectories. We had two main aims.

Aim 1

Aim 1 was to identify, in LAMS youth, subgroups with different developmental trajectories of behavioral and emotional dysregulation symptoms using PGBI-10M scores and LCGA. LCGA is an established technique for classifying longitudinal data into homogeneous and distinct classes within the larger heterogeneous group, based on latent (unobserved) trajectories within the data (Muthén & Muthén, Reference Muthén and Muthén1998–2011; Nylund et al. Reference Nylund, Asparouhov and Muthén2007).

Hypothesis 1

Hypothesis 1 was that LCGA would identify distinct classes of PGBI-10M developmental trajectories in LAMS youth during the 5-year course of LAMS1.

Aim 2

Aim 2 was to identify functional abnormalities in ER neural circuitry that differentiate LCGA-derived subgroups in LAMS youth in hypothesis 1, and that also differentiate LAMS subgroups from healthy control youth (HC). The following hypothesis was guided by reports of reduced activity in prefrontal cortical regions and reduced prefrontal cortical–amygdala connectivity in behaviorally and emotionally dysregulated (BPSD, depressed, ADHD) youth versus HC during ER tasks (Halari et al. Reference Halari, Simic, Pariante, Papadopoulos, Cleare, Brammer, Fombonne and Rubia2009; Passarotti et al. Reference Passarotti, Sweeney and Pavuluri2010a ; Hulvershorn et al. Reference Hulvershorn, Cullen and Anand2011; Ladouceur et al. Reference Ladouceur, Farchione, Diwadkar, Pruitt, Radwan, Axelson, Birmaher and Phillips2011).

Hypothesis 2

Hypothesis 2 was that LAMS youth with more severe PGBI-10M developmental trajectory would show significantly reduced activity in prefrontal cortical regions in ER circuitry and significantly reduced prefrontal cortical–amygdala connectivity during task performance than LAMS youth with a less severe PGBI-10M trajectory and HC.

In exploratory analyses, we aimed to examine how patterns of activity and functional connectivity in ER circuitry were associated with other clinical factors (e.g. diagnosis, medication, other symptoms) and demographic factors [age, gender, socio-economic status (SES)] and task performance.

Method

Participants

A total of 182 youth, recruited from the LAMS1 cohort of 707 youth, and 34 newly recruited HC, participated in the neuroimaging component of LAMS2. All HC were free of any psychiatric disorder; first-degree relatives were free of mood disorders and psychosis, and second-degree relatives were free of BPSD and psychosis. All 128 youth from LAMS1 entered LAMS1 with a variety of symptoms and diagnoses. Inclusion criteria for the LAMS1 cohort were: no out-patient treatment at a LAMS clinic in the last 12 months; 6–12 years of age; and without a sibling who was screened for LAMS1. Families of eligible children completed the PGBI-10M. Children who scored ⩾12 on this scale, and an age–gender-matched group of those who scored <12, were invited to participate in LAMS1. The 128 youth in the LAMS2 neuroimaging component were selected to include approximately equal numbers of youth: (1) with high (⩾12) versus low (<12) PGBI-10M scores; (2) who were older (⩾13 years) versus younger (⩽12 years); (3) who were male versus female (older versus younger and male versus female as above for each PGBI-10M subgroup per site). HC were recruited using local advertising at the three sites: Case Western Reserve University (LAMS: n = 32, HC: n = 13); Cincinnati Children's Hospital (LAMS: n = 48, HC: n = 6); and University of Pittsburgh Medical Center (LAMS: n = 48, HC: n = 15). Institutional review boards approved the study at each site. Parents/guardians provided written informed consent. Youth performed three different neuroimaging tasks; for results from the reward task, see Bebko et al. (Reference Bebko, Bertocci, Fournier, Hinze, Bonar, Almeida, Perlman, Versace, Schirda, Travis, Gill, Demeter, Diwadkar, Ciuffetelli, Rodriguez, Olino, Forbes, Sunshine, Holland, Kowatch, Birmaher, Axelson, Horwitz, Arnold, Fristad, Youngstrom, Findling and Phillips2013).

Yearly assessments throughout LAMS1 and LAMS2 included the parent/guardian's reported PGBI-10M over the last 6 months (Youngstrom et al. Reference Youngstrom, Meyers, Demeter, Youngstrom, Morello, Piiparinen, Feeny, Calabrese and Findling2005, Reference Youngstrom, Frazier, Demeter, Calabrese and Findling2008), parent and child reported Screen for Child Anxiety Related Emotional Disorders (SCARED) to assess anxiety symptoms (Birmaher et al. Reference Birmaher, Brent, Chiappetta, Bridge, Monga and Baugher1999) over the last 6 months, and parent and child report of manic and depressive symptom severity, respectively, using the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS) for School-Age Children Mania Rating Scale (KMRS; Axelson et al. Reference Axelson, Birmaher, Brent, Wassick, Hoover, Bridge and Ryan2003), and the K-SADS Depression Rating Scale (KDRS; Kaufman et al. Reference Kaufman, Birmaher, Brent, Rao, Flynn, Moreci, Williamson and Ryan1997). The PGBI-10M and SCARED were also reported biannually. Additionally, SCARED, KDRS and KMRS were performed on the day of the magnetic resonance (MR) scan.

For exclusion criteria, see online Supplementary material.

Data loss on the challenging emotional face n-back (EFNBACK) task was due to head movement >4 mm during scanning (Morgan et al. Reference Morgan, Olino, McMakin, Ryan and Forbes2013), task accuracy <75% and the inability to complete both task runs. Of the subjects, 61 LAMS and 24 HC successfully completed the task [mean age: LAMS = 13.41 (s.d. = 2.21) years, HC = 14.11 (s.d. = 1.93) years, females: 26 LAMS, 11 HC]. Clinical measures, medication use and demographic variables for participants who successfully completed the scan are shown in Table 1. Over half (33/61, 54%) of LAMS participants were using one or more medications on the scan date, including antidepressants, antipsychotic medication, mood stabilizers, non-stimulant ADHD medications and stimulant medications (Table 1). As a whole, completers and non-completers did not differ on gender, SES, clinical variables (PGBI-10M score, SCARED, KDRS, KMRS) or site (all p's > 0.12; see online Supplementary Table S1). Completers were, however, significantly older (p = 0.001) and had higher intelligence quotient (IQ) (p = 0.039). For individual class completion statistics, see online Supplementary Table S2.

Table 1. Demographic information, clinical measures and current medication usage describing latent classes of LAMS2 imaging sample and HC youth

LAMS2, Longitudinal Assessment of Manic Symptoms study second phase; HighD, youth with a high and decreasing developmental trajectory of behavioral and emotional dysregulation; LowD, youth with a low and decreasing developmental trajectory of behavioral and emotional dysregulation; HC, healthy control; s.d., standard deviation; IQ, intelligence quotient; SES, socio-economic status; GED, General Educational Development; LAMS1, Longitudinal Assessment of Manic Symptoms study first phase; PGBI-10M, Parent General Behavior Inventory 10-Item Mania Scale; n.a., not applicable; K-SADS, Kiddie Schedule for Affective Disorders and Schizophrenia; KDRS, K-SADS Depression Rating Scale; KMRS, K-SADS for School-Age Children Mania Rating Scale; SCARED, Screen for Child Anxiety Related Emotional Disorders.

LCGA

Patterns of PGBI-10M scores were evaluated to determine class membership using LCGA in Mplus6 (Muthén & Muthén, Reference Muthén and Muthén1998–2011), by defining the number of subgroups within the data that were distinct from each other. A total of 12 biannual PGBI-10M scores collected over 5 years of LAMS1 were used to define class membership for the total sample of 128 LAMS youth with neuroimaging. Three model fit indices were used: the Bayesian information criterion, a measure of relative fit (Nylund et al. Reference Nylund, Asparouhov and Muthén2007); the Vuong–Lo–Mendell–Rubin likelihood ratio, a test of improvement of k from k-1 classes; and entropy (range: 0–1), to determine distinctiveness of the classes. Convergence was aided by increasing the number of iterations and using random start values. The 61 LAMS participants who successfully completed neuroimaging were assigned to their appropriate latent classes according to these analyses.

Diagnoses: LCGA subgroups

For diagnoses of the LCGA subgroups, see online Supplementary Table S3.

Paradigm

The emotional n-back (EFNBACK) task was used to examine the recruitment of prefrontal cortical systems in the context of simultaneously presented emotionally salient distracting stimuli during working memory (Ochsner & Gross, Reference Ochsner and Gross2005; Ladouceur et al. Reference Ladouceur, Silk, Dahl, Ostapenko, Kronhaus and Phillips2009a ) (see online Supplementary material).

Neuroimaging data acquisition and preprocessing

For details of neuroimaging data acquisition and preprocessing, see the online Supplementary material.

Neuroimaging data analysis: activity

Using Statistical Parametric Mapping software (SPM8; http://www.fil.ion.ucl.ac.uk/spm), a two-level random-effects region of interest (ROI) analysis was conducted. At the first level, a mixed model was used with each trial event modeled separately, given the jittered nature of the inter-symbol interference between trials in each block with only correct trials included in the analyses; global signal normalization was also performed to improve model fit assumptions (see the ‘Combining data across sites’ section). Individual whole-brain statistical maps were then constructed to evaluate the main 2-back conditions of interest: 2-back with fear-face distracter; 2-back with happy-face distracter; and 2-back with neutral-face distracter. Movement parameters from the realignment stage served as no-interest covariates.

At the second level of blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) data analysis, a 3 [groups: 2 LAMS subgroups derived from LCGA (see below) and HC] × 3 (conditions: 2-back:fear, 2-back:happy, and 2-back:neutral) analysis of variance (ANOVA) examined neural activity during ER within one single large ROI mask, comprising the bilateral amygdala, DLPFC (BA 9/46), dACC (BA 24/32) and VLPFC (BA 47). Anatomical masks for these bilateral ROIs were created from the WFU PickAtlas (Wake Forest University, USA) (Maldjian et al. Reference Maldjian, Laurienti, Kraft and Burdette2003). Covariates were age, gender, IQ and scanning site. A voxelwise threshold of p < 0.01, with an AlphaSim cluster level correction threshold of p < 0.01 (AlphaSim; B. Ward, 2002; National Institute of Mental Health, USA) to correct for multiple voxelwise comparisons across the entire mask, was used.

Significant effects from the model above were further examined using post-hoc, pairwise between-group comparisons on activity in the bilateral ROI mask, using Bonferroni-corrected voxelwise thresholds as appropriate. For example, to control for three post-hoc pairwise group comparisons to interpret any significant overall main effect of group, we used a voxelwise threshold of p < 0.003 (0.01/3), AlphaSim cluster level corrected p < 0.01.

Neuroimaging data analysis: psychophysiological interactions (PPI)

PPI analysis was conducted in SPM8 to examine connectivity of the amygdala seed region with bilateral prefrontal–anterior cingulate target regions (described above) during ER. For each task condition, we created a PPI vector by multiplying mean time series from the seed region by task condition vector. Single-subject first-level analyses were then run for each 2-back:emotion condition with the following regressors: PPI vector; seed region time-course vector; and task-condition vector. Resulting contrast maps, weighted 1 (positive modulation) were used in a 3 (group: two LAMS subgroups and HC) × 3 (PPI conditions: 2-back:fear, 2-back:happy, and 2-back:neutral) full-factorial model at the second level to examine functional connectivity during ER within our single ROI target mask: bilateral DLPFC (BA 9/46), dACC (BA 24/32) and VLPFC (BA 47). Covariates were age, gender, IQ and site. A voxelwise threshold of p < 0.01, and p < 0.01 cluster-level correction, were used.

Significant main effects of group, emotion, or group × emotion interaction, were further examined using post-hoc, pairwise between-group comparisons on PPIs in the bilateral ROI target mask, using Bonferroni-corrected voxelwise thresholds as appropriate.

Further analyses

In parallel analyses we performed a full-factorial 3 (group: LowD, HighD, HC) × 2 (cognitive loads: 0-back and 2-back) × 3 (emotional conditions: fear, happy, neutral) ANOVA model. Here we used the same voxelwise and clusterwise thresholds as in the above 2 (group) × 3 (emotional condition) ANOVA.

Exploratory analyses

Exploratory analyses examined whole-brain activity and connectivity to 2-back conditions: (voxelwise threshold of p < 0.005, cluster-level corrected threshold of p < 0.01). Significant main effects of group, emotion, or group × emotion interaction, were examined using post-hoc tests, using Bonferroni-corrected voxelwise thresholds as appropriate.

We also examined relationships between clusters of activity and measures of functional connectivity showing a significant main effect of group from the main analyses focusing on the 2-back conditions and diagnosis, medication use, KMRS, KDRS, SCARED scores, age, gender, IQ, SES and task performance.

Combining data across sites

Studies report that merging neuroimaging data from multiple sites is feasible (Magnotta & Friedman, Reference Magnotta and Friedman2006; Segall et al. Reference Segall, Turner, van Erp, White, Bockholt, Gollub, Ho, Magnotta, Jung, McCarley, Schulz, Lauriello, Clark, Voyvodic, Diaz and Calhoun2009). We used the following procedures to control for inter-site scanner variability and to combine neuroimaging data across our three sites. First, to improve the degree to which the first-level models met model assumptions at each site, global normalization was implemented (Eklund et al. Reference Eklund, Andersson, Josephson, Johannesson and Knutsson2012). Normality of the residuals was calculated using the Shapiro–Wilk test separately for each first-level model with and without global normalization, averaged over all voxels in the single a priori bilateral ROI. Non-parametric tests showed significant improvement in normality of residuals after global normalization (Z = − 5.133, p < 0.001); and the Durbin–Watson test showed improvements in serial independence of the residuals (χ 2 = 9.276, p = 0.002). Second, standards published by the Biomedical Informatics Research Network (BIRN; http://www.nbirn.net) for data acquisition and information sharing were implemented. Using a BIRN phantom, scanner signal:noise ratio was collected and monitored for stability monthly at each scanner site (Friedman & Glover, Reference Friedman and Glover2006; Friedman et al. Reference Friedman and Glover2006) (see online Supplementary Fig. S1). Third, we used scanning site as a covariate in all analyses.

Results

LCGA

A two-class model was revealed as acceptable and compatible with neuroimaging analysis sample requirements (Table 2), where power analyses suggest that a group of at least 12 is needed to provide 80% power at p < 0.01 for fMRI data analysis (Desmond & Glover, Reference Desmond and Glover2002). In the total sample of 128 LAMS youth, we identified two latent-class subgroups of PGBI-10M trajectory: youth with a high and decreasing developmental trajectory of behavioral and emotional dysregulation (HighD; n = 49, with 22 successfully completed the neuroimaging protocol); and youth with a low and decreasing developmental trajectory of behavioral and emotional dysregulation (LowD; n = 79, with 39 successfully completed the neuroimaging protocol; Fig. 1). HighD and LowD did not differ significantly on age, gender, IQ, SES, KDRS or SCARED, or on antidepressant, stimulant or non-stimulant ADHD medication use. The two subgroups that completed neuroimaging differed on PGBI-10M at baseline (entry into LAMS1; p = 0.001), PGBI-10M nearest to scan (p = 0.001), KMRS (p = 0.012) and use of antipsychotic (p = 0.031) and mood stabilizer medications (p = 0.011; Table 1). Of note, prior analyses (Findling et al. Reference Findling, Jo, Frazier, Youngstrom, Demeter, Fristad, Birmaher, Kowatch, Arnold, Axelson, Ryan, Hauser, Brace, Marsh, Gill, Depew, Rowles and Horwitz2013) using the complete LAMS1 cohort (n = 707) and four PGBI-10M time points identified four latent LAMS classes, with the two largest classes defined as high and decreasing (38.5%) and low and decreasing (47.2%), reflecting class distinctions observed in the present analysis (Fig. 1).

Fig. 1. Spaghetti plot of latent class models based on latent class growth analysis of 12 Parent General Behavior Inventory 10-Item Mania Scale (PGBI-10M) reports over 5 years of the Longitudinal Assessment of Manic Symptoms study first phase (LAMS1). ▲, Latent trajectory of the high and decreasing (HighD) behavioral and emotional dysregulation trajectory; ●, latent trajectory of the low and decreasing (LowD) behavioral and emotional dysregulation trajectory.

Table 2. Latent class growth analysis model fit indices

BIC, Bayesian Information Criterion.

Behavioral data

Performance on the 2-back with emotional faces task was good (mean accuracy = 89.4%). Performance differed by group, with HC (accuracy = 92%) and LowD (accuracy = 91%) performing more accurately than HighD (accuracy = 84%) (F 2,82 = 6.32, p = 0.003). LowD and HC did not differ significantly on task performance. Performance for the entire neuroimaging sample showed the same pattern of between-group differences in accuracy (see online Supplementary material).

Activity

There was a significant main effect of group in two clusters in the bilateral DLPFC (peak voxel, right: F 2,241 = 9.92, p < 0.001, corrected; left: F 2,241 = 6.40, p = 0.002, corrected). There was no significant main effect of emotion or group × emotion interaction (Table 3 and Fig. 2).

Fig. 2. Bilateral dorsolateral prefrontal cortex (DLPFC; Brodmann area 9) activity for main effect of group on neural activity across all emotional distracters in the entire bilateral region of interest (ROI) mask. Peak voxel right DLPFC (cluster 66): Montreal Neurological Institute (MNI): x = 36, y = 28, z = 42, k = 66 (p < 0.001); left DLPFC (cluster 30): MNI: x = − 30, y = 30, z = 38, k = 30 (p = 0.002); left DLPFC (cluster 27): MNI: x = − 22, y = 42, z = 40, k = 27 (p = 0.003). The color bar represents F values. Values are means and 95% confidence intervals. LowD, Youth with a low and decreasing developmental trajectory of behavioral and emotional dysregulation; HighD, youth with a high and decreasing developmental trajectory of behavioral and emotional dysregulation; HC, healthy control youth.

Table 3. Between-group differences in activity and PPI functional connectivity during a working memory task with emotional distracters

PPI, Psychophysiological interactions; BOLD, blood oxygen level-dependent; BA, Brodmann area; MNI, Montreal Neurological Institute; DLPFC, dorsolateral prefrontal cortex; LowD, youth with a low and decreasing developmental trajectory of behavioral and emotional dysregulation; HighD, youth with a high and decreasing developmental trajectory of behavioral and emotional dysregulation; HC, healthy control youth; VLPFC, ventrolateral prefrontal cortex; dACC, dorsal anterior cingulate cortex.

Post-hoc analyses, using a Bonferroni-corrected voxelwise threshold of p < 0.003 (0.01/3) to control for three pairwise between-group comparisons, revealed that LowD showed greater bilateral DLPFC activity than HC (right: t 241 = 4.20, p = 0.001; left: t 241 = 3.46, p = 0.001, corrected) and greater left DLPFC activity than HighD (t 241 = 3.46, p = 0.001, corrected; Table 3). HC and HighD did not differ significantly.

PPI

PPI analysis revealed a significant main effect of group on functional connectivity between the amygdala and left VLPFC (F 2,241 = 7.58, p = 0.001, corrected). Post-hoc analyses, using a Bonferroni-corrected voxelwise threshold of p < 0.003 (0.01/3) to control for three pairwise between-group comparisons, revealed significantly reduced positive functional connectivity in HighD than LowD between the bilateral amygdala and left VLPFC (t 241 = 3.87, p < 0.001, corrected), and between the bilateral amygdala and two clusters in the left dACC (t 241 = 3.49 and t 241 = 3.05, p = 0.001, corrected; Table 3 and Fig. 3 a, b). The magnitude of functional connectivity among these regions in HC was intermediate between that shown by the two LAMS subgroups, but did not differ significantly from either LowD or HighD.

Fig. 3. Functional connectivity between amygdala seed (not shown) and bilateral region of interest (ROI) mask target regions. (a) Main effect of group for functional connectivity across all emotional distracters: amygdala–left ventrolateral prefrontal cortex (Brodmann area 47, cluster 48) connectivity. Peak voxel Montreal Neurological Institute (MNI): x = − 42, y = 30, z = − 14, k = 48 (p = 0.001, corrected). The color bar represents F values. Values are means and 95% confidence intervals. (b) Post-hoc analysis of LowD versus HighD for amygdala–left VLPFC connectivity (peak voxel MNI: − 42, 30, − 14, k = 67, p < 0.001, corrected) and amygdala-left dACC connectivity (peak voxel MNI: − 2, 6, 40, k = 106, p < 0.001, corrected and MNI: 2, 38, 8, k = 30, p < 0.002, corrected). Color bar represents t values. Bars represent the 95% confidence interval.

There was also a main effect of emotional condition on functional connectivity between the bilateral amygdala and bilateral DLPFC (right: F 2,241 = 8.70, p < 0.001; left: F 2,241 = 8.42, p < 0.001, corrected; Table 3 and Fig. 3). Post-hoc analyses, using a Bonferroni-corrected voxelwise threshold of p < 0.003 (0.01/3) to control for three pairwise between-emotion condition comparisons, revealed significantly greater functional connectivity between the bilateral amygdala and bilateral DLPFC to the fear distracter than to the neutral distracter across all participants (right: t 241 = 4.14, p < 0.001; left: t 241 = 4.08, p < 0.001, corrected; Table 3).

Further analysis

Findings from the full-factorial 3 (groups: LowD, HighD, HC) × 2 (cognitive loads: 0-back and 2-back) × 3 (emotional conditions: fear, happy, neutral) ANOVA for activity revealed a similar pattern of a significant main effect of group in the right DLPFC [BA 9; F 2,487 = 9.57, p < 0.001, corrected, 64 voxels, Montreal Neurological Institute (MNI): x = 34, y = 26, z = 42]. For group comparisons, see the online Supplementary material.

Findings from the full factorial model for functional connectivity revealed a similar pattern of a significant main effect of group on functional connectivity between the bilateral amygdala and left VLPFC (BA 47; F 2,487 = 11.65, p < 0.001, corrected, 67 voxels, MNI: x = –34, y = 32, z = − 14) and between the bilateral amygdala and bilateral dACC (BA 24; left: F 2,487 = 9.25, p < 0.001, corrected, 140 voxels, MNI: x = − 2, y = 6, z = 40; right: F 2,487 = 8.44, p < 0.001, corrected, 171 voxels, MNI: x = 4, y = 8, z = 38). For group comparisons, see the online Supplementary material.

Exploratory analysis

Given the between-group difference in task accuracy, LAMS not taking versus LAMS taking mood stabilizer medication (p = 0.03) and LAMS without versus those with a BPSD diagnosis (p = 0.03) (online Supplementary Table S4), we covaried for these in additional analyses. For results with significant covariates, see the online Supplementary material.

For whole-brain results, see online Supplementary Tables S5 and S6.

Discussion

The goal of this study was to identify biomarkers associated with different trajectories of behavioral and emotional dysregulation in LAMS to lead to a better understanding of pathophysiological processes underlying developmental trajectories. We used LCGA and neuroimaging measures of functional integrity of ER neural circuitry in a large group of LAMS and HC youth. In support of our first hypothesis, LCGA of 12 PGBI-10M reports over 5 years revealed two latent-class subgroups: LAMS participants with an initially high, then gradually decreasing (HighD) developmental trajectory of behavioral and emotional dysregulation symptoms; and LAMS participants with an initially low yet also decreasing (LowD) developmental trajectory of behavioral and emotional dysregulation symptoms. In partial support of our second hypothesis, these two groups were differentiated by patterns of activity and functional connectivity in our a priori ROI involved in ER. The results of the analyses converged, showing a common pattern of greater activity and functional connectivity by LowD relative to HighD in important prefrontal and cingular regions as predicted. These findings provide a novel, data-driven understanding of previous developmental trajectories of behavioral and emotional dysregulation and associated patterns of activity and functional connectivity in ER neural circuitry.

LowD showed significantly greater bilateral DLPFC activity during ER task performance than either HighD or HC to the demanding 2-back cognitive load. By contrast, HighD not only showed significantly less DLPFC activity than LowD during ER task performance, but also failed to complete the task at the same performance level as either HC or LowD. These findings suggest that recruiting the DLPFC to a greater than normal extent during ER task performance may be necessary to help compensate for behavioral and emotional dysregulation and equate task performance with that of HC in LAMS youth. Thus, LowD recruited the DLPFC to a greater extent than HC to maximize task performance, HighD failed to do this, resulting in poorer task performance than either of the other groups. Although differences observed in HighD may alternatively reflect inattention to task, the high accuracy rate for this group, and the fact that they succeeded in remaining still for this fMRI paradigm, suggests that HighD did, in fact, attend to task. Furthermore, analyses covarying for accuracy revealed similar patterns of between-group differences in activity. Previous reports of significantly decreased DLPFC activity on ER tasks in youth with severe pathology evidenced by BPSD diagnoses in these samples (Passarotti et al. Reference Passarotti, Sweeney and Pavuluri2010a ; Ladouceur et al. Reference Ladouceur, Farchione, Diwadkar, Pruitt, Radwan, Axelson, Birmaher and Phillips2011) provide further support for this interpretation of findings, and suggest that more severely behaviorally and emotionally dysregulated youth may be less able to recruit prefrontal cortical regions during cognitive task performance.

PPI analysis similarly showed that prefrontal and anterior cingulate cortical regions were differentially connected with the amygdala during task performance across the two LAMS subgroups. Here, HighD showed significantly reduced positive amygdala–left VLPFC and reduced positive amygdala–left dACC functional connectivity than LowD, even after covarying for task accuracy. Furthermore, this between-group difference in functional connectivity resulted from HighD showing significantly greater inverse functional connectivity between these regions than LowD, while the magnitude of functional connectivity among these regions in HC was intermediate between that shown by LowD and HighD (Fig. 3 a). In the context of emotionally distracting material, a combination of decreased positive/increased inverse functional coupling among the amygdala, VLPFC and dACC and decreased DLPFC activity may thus represent a neural mechanism for impaired ER task performance that may in turn be associated with more severe behavioral and emotional dysregulation in youth. By contrast, greater positive functional coupling and activity in this circuitry than HC may represent a compensatory response to help optimize ER task performance, but is shown only by youth with less severe behavioral and emotional dysregulation. Again, evidence of decreased positive amygdala–prefrontal functional connectivity was previously reported in youth with severe dysregulation such as mood disorders and at-risk for psychosis (Cusi et al. Reference Cusi, Nazarov, Holshausen, Macqueen and McKinnon2012; Gee et al. Reference Gee, Karlsgodt, van Erp, Bearden, Lieberman, Belger, Perkins, Olvet, Cornblatt, Constable, Woods, Addington, Cadenhead, McGlashan, Seidman, Tsuang, Walker and Cannon2012; Passarotti et al. Reference Passarotti, Ellis, Wegbreit, Stevens and Pavuluri2012). The present study is the first to our knowledge to examine dimensions of dysregulation across diagnoses and to use LCGA to characterize subgroups of youth based on previous developmental trajectories of behavioral and emotional regulation symptoms. Further it is the first to our knowledge to examine how these subgroups are differentiated by patterns of activity and functional connectivity in ER neural circuitry.

Interestingly, similar patterns of between-group differences in DLPFC activity and amygdala–VLPFC and amygdala–dACC functional connectivity were shown across both 0-back and 2-back cognitive loads in the full factorial analyses. The 0-back condition, while less difficult than the 2-back condition, still requires an ability to redirect attention from emotional distracters toward the task-relevant stimulus, and thus requires intact attentional resources. Our findings suggest between-group differences in recruitment of neural circuitry for performance of the 0-back condition as for the 2-back condition.

Critically, we were able to show significant differences in both activity and functional connectivity between LAMS subgroups, even though at the time of scanning, PGBI-10M severity had decreased since study entry in both subgroups. Furthermore, findings remained after covarying for clinical measures that differed between LAMS subgroups on the scan date: mood stabilizer medication and having a BPSD diagnosis, with greater amygdala–left VLPFC and amygdala–dACC functional connectivity still observed in LowD than HighD. Together, these findings suggest that previous developmental trajectories of behavioral and emotional dysregulation make an impact on the functional integrity of ER neural circuitry, irrespective of present diagnosis or medication, and highlight the importance of examining the contribution of developmental trajectories in neuroimaging studies of behaviorally and emotionally dysregulated youth.

The significance of the left-lateralized nature of bilateral amygdala–prefrontal cortical functional connectivity across groups is unclear. The VLPFC has a specific role in supporting reversal learning and set shifting (Rygula et al. Reference Rygula, Walker, Clarke, Robbins and Roberts2010) and the left hemisphere is involved in activities requiring attention to distinctive features and judgment (Haxby et al. Reference Haxby, Ungerleider, Horwitz, Rapoport and Grady1995). Thus, recruitment of the left VLPFC during this task may be required to allow redirection of attention away from facial features during facial emotion processing to facilitate task performance.

Interestingly, all youth showed greater functional connectivity between the bilateral amygdala and bilateral DLPFC to fearful than to neutral distracters. Given our previous report that youth are slower to perform the task in the presence of fearful than other distracters (Ladouceur et al. Reference Ladouceur, Silk, Dahl, Ostapenko, Kronhaus and Phillips2009b ), these findings suggest that greater amygdala–prefrontal cortical functional connectivity was required by all youth to maintain 2-back working memory performance in the presence of fearful face distracters.

Limitations include the inability to determine the temporal sequence of neuroimaging measure differentiation and the development of behavioral and emotional dysregulation. Future research should directly test this question by performing longitudinal clinical assessments after neuroimaging assessments in youth. Data loss was significant, although accuracy on the task for the entire group was similar to the subset successfully completing neuroimaging, and youth who were able to complete the task, versus those who were not, differed only in age and IQ; older and higher-IQ youth were more successful at task completion, suggesting that generalizability was not compromised by the data loss. A careful comparison of completers and non-completers in each subgroup (LowD, HighD and HC) showed that, in each group, age was related to completion, with older youth being more successful. LowD completers had higher depression scores than LowD non-completers, however, suggesting that LowD completers may in fact have been more depressed at the time of scanning than LowD non-completers. Future neuroimaging studies of these high-risk populations may benefit by limiting the scanning session length. We employed an ROI approach for activity and functional connectivity analyses. We used a single, large bilateral ROI for analyses. Exploratory whole-brain analyses provided findings largely in support of these ROI analyses, however. Multiple sites were included, allowing for recruitment of larger numbers of youth, and greater generalizability. We accounted for the potential effect of scanner site upon neuroimaging measures by following BIRN recommendations for multi-site data collection and scanner signal:noise ratio monitoring, by ensuring model assumptions were met, and covarying for site in analyses.

Identifying objective biological markers that reflect underlying pathophysiological processes in pediatric psychiatric disorders is vital to identify biological targets to guide treatment choices and novel treatment development. The opportunity to recruit a subset of youth from the large LAMS study of youth with behavioral and emotional dysregulation symptoms provided a unique opportunity to examine neural correlates of the developmental trajectories of these symptoms, regardless of diagnosis, an approach that parallels the dimensional approach of the RDoC. Our findings suggest differential patterns of underlying prefrontal cortical activity and prefrontal cortical–amygdala connectivity associated with developmental trajectories of behavioral and emotional dysregulation. These findings may ultimately provide biological targets to guide treatment for different levels of severity of behavioral and emotional dysregulation in youth.

Supplementary material

For supplementary material accompanying this paper visit http://dx.doi.org/10.1017/S0033291714000087.

Acknowledgements

This research was supported by the National Institute of Mental Health grant 2R01 MH73953-06A1 (B.B. and M.L.P., University of Pittsburgh).

Declaration of Interest

R.L.F. receives or has received research support, acted as a consultant and/or served on a speakers’ bureau for Alexza Pharmaceuticals, American Psychiatric Press, AstraZeneca, Bracket, Bristol-Myers Squibb, Cognition Group, Forest, GlaxoSmithKline, Guilford Press, Johns Hopkins University Press, Johnson & Johnson, KemPharm, Lilly, Lundbeck, Merck, National Institutes of Health (NIH), Novartis, Noven, Otsuka, Pfizer, Physicians Postgraduate Press, Rhodes Pharmaceuticals, Roche, Sage, Seaside Pharmaceuticals, Shire, Stanley Medical Research Institute, Sunovion, Supernus Pharmaceuticals, Transcept Pharmaceuticals, Validus and WebMD. L.E.A. has received research funding from Curemark, Forest, Lilly and Shire, advisory board honoraria from Biomarin, Novartis, Noven, Roche, Seaside Therapeutics and Shire, has acted as a consultant for Tris Pharma, and received travel support from Noven. E.A.Y. has consulted with Lundbeck and received travel support from Bristol-Myers Squibb, as well as grant support from the NIH. M.A.F. receives royalties from Guilford Press, Inc., APPI and CFPSI. B.B. has or will receive royalties for publications from Random House, Inc. (New Hope for Children and Teens with Bipolar Disorder) and Lippincott Williams & Wilkins (Treating Child and Adolescent Depression). He is employed by the University of Pittsburgh and the University of Pittsburgh Medical Center and receives research funding from the National Institute of Mental Health (NIMH). R.A.K. is a consultant for Forest Pharmaceutical, Astra-Zeneca and the REACH Foundation. He receives research support from the NIMH. He is employed by Ohio State University and is an editor for Current Psychiatry. J.L.S. receives research support from Siemens Healthcare.

References

Axelson, DA, Birmaher, B, Brent, DA, Wassick, S, Hoover, C, Bridge, J, Ryan, ND (2003). A preliminary study of the Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children mania rating scale for children and adolescents. Journal of Child and Adolescent Psychopharmacology 13, 463–470.Google Scholar
Bebko, G, Bertocci, MA, Fournier, JC, Hinze, AK, Bonar, L, Almeida, JR, Perlman, SB, Versace, A, Schirda, C, Travis, M, Gill, MK, Demeter, C, Diwadkar, VA, Ciuffetelli, G, Rodriguez, E, Olino, T, Forbes, E, Sunshine, JL, Holland, SK, Kowatch, RA, Birmaher, B, Axelson, D, Horwitz, SM, Arnold, LE, Fristad, MA, Youngstrom, EA, Findling, RL, Phillips, ML (2013). Parsing dimensional vs diagnostic category-related patterns of reward circuitry function in behaviorally and emotionally dysregulated youth in the longitudinal assessment of manic symptoms study. JAMA Psychiatry. Published online 27 November 2013 . doi:10.1001/jamapsychiatry.2013.2870.Google Scholar
Birmaher, B, Brent, DA, Chiappetta, L, Bridge, J, Monga, S, Baugher, M (1999). Psychometric properties of the Screen for Child Anxiety Related Emotional Disorders (SCARED): a replication study. Journal of the American Academy of Child and Adolescent Psychiatry 38, 1230–1236.CrossRefGoogle ScholarPubMed
Brotman, M, Schmajuk, M, Rich, B, Dickstein, D, Guyer, A, Costello, E, Egger, H, Angold, A, Pine, D, Leibenluft, E (2006). Prevalence, clinical correlates, and longitudinal course of severe mood dysregulation in children. Biological Psychiatry 60, 991–997.Google Scholar
Charney, DS, Babich, KS (2002). Foundation for the NIMH strategic plan for mood disorders research. Biological Psychiatry 52, 455–456.CrossRefGoogle ScholarPubMed
Cusi, A, Nazarov, A, Holshausen, K, Macqueen, G, McKinnon, M (2012). Systematic review of the neural basis of social cognition in patients with mood disorders. Journal of Psychiatry Neuroscience 37, 154–169.Google Scholar
Desmond, J, Glover, G (2002). Estimating sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses. Journal of Neuroscience Methods 118, 115–128.Google Scholar
Eklund, A, Andersson, M, Josephson, C, Johannesson, M, Knutsson, H (2012). Does parametric fMRI analysis with SPM yield valid results? An empirical study of 1484 rest datasets. NeuroImage 61, 565–578.CrossRefGoogle ScholarPubMed
Findling, RL, Jo, B, Frazier, TW, Youngstrom, EA, Demeter, CA, Fristad, MA, Birmaher, B, Kowatch, RA, Arnold, E, Axelson, DA, Ryan, N, Hauser, JC, Brace, DJ, Marsh, LE, Gill, MK, Depew, J, Rowles, BM, Horwitz, SM (2013). The 24-month course of manic symptoms in children. Bipolar Disorders 15, 669–679.Google Scholar
Findling, RL, Youngstrom, EA, Fristad, M, Birmaher, B, Kowatch, R, Arnold, LE, Frazier, TW, Axelson, DA, Ryan, ND, Demeter, C, Gill, MK, Fields, B, Depew, J, Kennedy, S, Marsh, L, Rowles, B, Horwitz, SM (2010). Characteristics of children with elevated symptoms of mania: the Longitudinal Assessment of Manic Symptoms (LAMS) study. Journal of Clinical Psychiatry 71, 1664–1672.Google Scholar
Frazier, TW, Youngstrom, EA, Horwitz, SM, Demeter, C, Fristad, M, Arnold, LE, Birmaher, B, Kowatch, R, Axelson, DA, Ryan, ND, Gill, MK, Findling, RL (2011). Relationship of persistent manic symptoms to the diagnosis of pediatric bipolar spectrum disorders. Journal of Clinical Psychiatry 72, 846–853.Google Scholar
Friedman, L, Glover, G (2006). Report on a multicenter fMRI quality assurance protocol. Journal of Magnetic Resonance Imaging 23, 827–839.Google Scholar
Friedman, L, Glover, G; fBIRN Consortium (2006). Reducing interscanner variability of activation in a multicenter fMRI study: controlling for signal-to-fluctuation-noise-ratio (SFNR) differences. NeuroImage 33, 471–481.CrossRefGoogle Scholar
Gee, D, Karlsgodt, K, van Erp, T, Bearden, C, Lieberman, M, Belger, A, Perkins, D, Olvet, D, Cornblatt, B, Constable, T, Woods, S, Addington, J, Cadenhead, K, McGlashan, T, Seidman, L, Tsuang, M, Walker, E, Cannon, T (2012). Altered age-related trajectories of amygdala–prefrontal circuitry in adolescents at clinical high risk for psychosis: a preliminary study. Schizophrenia Research 134, 1–9.CrossRefGoogle ScholarPubMed
Halari, R, Simic, M, Pariante, C, Papadopoulos, A, Cleare, A, Brammer, M, Fombonne, E, Rubia, K (2009). Reduced activation in lateral prefrontal cortex and anterior cingulate during attention and cognitive control functions in medication-naive adolescents with depression compared to controls. Journal of Child Psychology and Psychiatry 50, 307–316.Google Scholar
Hasler, G, Drevets, WC, Gould, TD, Gottesman, II, Manji, HK (2006). Toward constructing an endophenotype strategy for bipolar disorders. Biological Psychiatry 60, 93–105.Google Scholar
Haxby, J, Ungerleider, L, Horwitz, B, Rapoport, S, Grady, C (1995). Hemispheric differences in neural systems for face working memory: a PET-rCBF study. Human Brain Mapping 3, 68–82.Google Scholar
Horwitz, SM, Demeter, C, Pagano, M, Youngstrom, EA, Fristad, M, Arnold, LE, Birmaher, B, Gill, MK, Axelson, DA, Kowatch, R, Frazier, T, Findling, RL (2010). Longitudinal Assessment of Manic Symptoms (LAMS) study: background, design, and initial screening results. Journal of Clinical Psychiatry 71, 1511–1517.Google Scholar
Hulvershorn, L, Cullen, K, Anand, A (2011). Toward dysfunctional connectivity: a review of neuroimaging findings in pediatric major depressive disorder. Brain Imaging Behavior 5, 307–328.CrossRefGoogle ScholarPubMed
Insel, T, Cuthbert, B, Garvey, M, Heinssen, R, Pine, D, Quinn, K, Sanislow, C, Wang, P (2010). Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. American Journal of Psychiatry 167, 748–751.Google Scholar
Kaufman, J, Birmaher, B, Brent, DA, Rao, U, Flynn, C, Moreci, P, Williamson, D, Ryan, ND (1997). Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. Journal of the American Academy of Child and Adolescent Psychiatry 36, 980–988.CrossRefGoogle ScholarPubMed
Ladouceur, C, Silk, JS, Dahl, RE, Ostapenko, L, Kronhaus, DM, Phillips, ML (2009 a). Fearful faces influence attentional control processes in anxious youth and adults. Emotion 9, 855–864.Google Scholar
Ladouceur, CD, Farchione, T, Diwadkar, V, Pruitt, P, Radwan, J, Axelson, D, Birmaher, B, Phillips, ML (2011). Differential patterns of abnormal activity and connectivity in the amygdala–prefrontal circuitry in bipolar-I and bipolar-NOS youth. Journal of the American Academy of Child and Adolescent Psychiatry 50, 1275–1289.CrossRefGoogle ScholarPubMed
Ladouceur, CD, Silk, JS, Dahl, RE, Ostapenko, L, Kronhaus, D, Phillips, ML (2009 b). Fearful faces influence attentional control processes in anxious youth and adults. Emotion 9, 855–864.Google Scholar
Lewinsohn, P, Klein, D, Seeley, J (2000). Bipolar disorder during adolescence and young adulthood in a community sample. Bipolar Disorders 2, 281–293.CrossRefGoogle Scholar
Magnotta, V, Friedman, L (2006). Measurement of signal-to-noise and contrast-to-noise in the fBIRN multicenter imaging study. Journal of Digital Imaging 19, 140–147.CrossRefGoogle ScholarPubMed
Maldjian, JA, Laurienti, PJ, Kraft, RA, Burdette, JH (2003). An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 19, 1233–1239.Google Scholar
Morgan, J, Olino, TM, McMakin, D, Ryan, ND, Forbes, EE (2013). Neural response to reward as a predictor of increases in depressive symptoms in adolescence. Neurobiological Disorders 52, 66–74.Google Scholar
Muthén, LK, Muthén, BO (1998–2011). Mplus User's Guide, sixth edn. Muthén & Muthén: Los Angeles, CA.Google Scholar
Nylund, KL, Asparouhov, T, Muthén, BO (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Structural Equation Modeling 14, 535–569.CrossRefGoogle Scholar
Ochsner, KN, Gross, JJ (2005). The cognitive control of emotion. Trends in Cognitive Sciences 9, 242–249.Google Scholar
Passarotti, A, Ellis, J, Wegbreit, E, Stevens, M, Pavuluri, M (2012). Reduced functional connectivity of prefrontal regions and amygdala within affect and working memory networks in pediatric bipolar disorder. Brain Connectivity 2, 320–334.Google Scholar
Passarotti, A, Sweeney, J, Pavuluri, M (2010 a). Emotion processing influences working memory circuits in pediatric bipolar disorder and attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry 49, 1064–1080.CrossRefGoogle ScholarPubMed
Passarotti, A, Sweeney, J, Pavuluri, M (2010 b). Neural correlates of response inhibition in pediatric bipolar disorder and attention deficit hyperactivity disorder. Psychiatry Research: Neuroimaging 181, 36–43.CrossRefGoogle ScholarPubMed
Pavuluri, M, O'Connor, M, Harral, E, Sweeney, J (2008). An fMRI study of the interface between affective and cognitive neural circuitry in pediatric bipolar disorder. Psychiatry Research: Neuroimaging 162, 244–255.Google Scholar
Perlman, G, Simmons, A, Wu, J, Hahn, K, Tapert, S, Max, J, Paulus, M, Brown, G, Frank, G, Campbell-Sills, L, Yang, T (2012). Amygdala response and functional connectivity during emotion regulation: a study of 14 depressed adolescents. Journal of Affective Disorders 139, 75–84.Google Scholar
Phillips, M, Ladouceur, C, Drevets, W (2008). A neural model of voluntary and automatic emotion regulation: implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Molecular Psychiatry 13, 833–857.Google Scholar
Phillips, ML, Frank, E (2006). Redefining bipolar disorder: toward DSM5. American Journal of Psychiatry 163, 1135–1136.Google Scholar
Rich, B, Carver, F, Holroyd, T, Rosen, H, Mendoza, J, Cornwell, B, Fox, N, Pine, D, Coppola, R, Leibenluft, E (2011). Different neural pathways to negative affect in youth with pediatric bipolar disorder and severe mood dysregulation. Journal of Psychiatry Research 45, 1283–1294.Google Scholar
Rich, B, Fromm, S, Berghorst, L, Dickstein, D, Brotman, M, Pine, D, Leibenluft, E (2008). Neural connectivity in children with bipolar disorder: impairment in the face emotion processing circuit. Journal of Child Psychology and Psychiatry 49, 88–96.CrossRefGoogle ScholarPubMed
Rygula, R, Walker, S, Clarke, H, Robbins, T, Roberts, A (2010). Differential contributions of the primate ventrolateral prefrontal and orbitofrontal cortex to serial reversal learning. Journal of Neuroscience 30, 14552–14559.CrossRefGoogle ScholarPubMed
Segall, J, Turner, J, van Erp, T, White, T, Bockholt, H, Gollub, R, Ho, B, Magnotta, V, Jung, R, McCarley, R, Schulz, S, Lauriello, J, Clark, V, Voyvodic, J, Diaz, M, Calhoun, V (2009). Voxel-based morphometric multisite collaborative study on schizophrenia. Schizophrenia Bulletin 35, 82–95.CrossRefGoogle Scholar
Stringaris, A, Goodman, R (2009). Mood lability and psychopathology in youth. Psychological Medicine 39, 1237–1245.CrossRefGoogle ScholarPubMed
Youngstrom, EA, Frazier, T, Demeter, C, Calabrese, J, Findling, RL (2008). Developing a 10-item mania scale from the Parent General Behavior Inventory for children and adolescents. Journal of Clinical Psychiatry 69, 831–839.CrossRefGoogle ScholarPubMed
Youngstrom, EA, Meyers, O, Demeter, C, Youngstrom, J, Morello, L, Piiparinen, R, Feeny, N, Calabrese, J, Findling, RL (2005). Comparing diagnostic checklists for pediatric bipolar disorder in academic and community mental health settings. Bipolar Disorders 7, 507–517.Google Scholar
Figure 0

Table 1. Demographic information, clinical measures and current medication usage describing latent classes of LAMS2 imaging sample and HC youth

Figure 1

Fig. 1. Spaghetti plot of latent class models based on latent class growth analysis of 12 Parent General Behavior Inventory 10-Item Mania Scale (PGBI-10M) reports over 5 years of the Longitudinal Assessment of Manic Symptoms study first phase (LAMS1). ▲, Latent trajectory of the high and decreasing (HighD) behavioral and emotional dysregulation trajectory; ●, latent trajectory of the low and decreasing (LowD) behavioral and emotional dysregulation trajectory.

Figure 2

Table 2. Latent class growth analysis model fit indices

Figure 3

Fig. 2. Bilateral dorsolateral prefrontal cortex (DLPFC; Brodmann area 9) activity for main effect of group on neural activity across all emotional distracters in the entire bilateral region of interest (ROI) mask. Peak voxel right DLPFC (cluster 66): Montreal Neurological Institute (MNI): x = 36, y = 28, z = 42, k = 66 (p < 0.001); left DLPFC (cluster 30): MNI: x = − 30, y = 30, z = 38, k = 30 (p = 0.002); left DLPFC (cluster 27): MNI: x = − 22, y = 42, z = 40, k = 27 (p = 0.003). The color bar represents F values. Values are means and 95% confidence intervals. LowD, Youth with a low and decreasing developmental trajectory of behavioral and emotional dysregulation; HighD, youth with a high and decreasing developmental trajectory of behavioral and emotional dysregulation; HC, healthy control youth.

Figure 4

Table 3. Between-group differences in activity and PPI functional connectivity during a working memory task with emotional distracters

Figure 5

Fig. 3. Functional connectivity between amygdala seed (not shown) and bilateral region of interest (ROI) mask target regions. (a) Main effect of group for functional connectivity across all emotional distracters: amygdala–left ventrolateral prefrontal cortex (Brodmann area 47, cluster 48) connectivity. Peak voxel Montreal Neurological Institute (MNI): x = − 42, y = 30, z = − 14, k = 48 (p = 0.001, corrected). The color bar represents F values. Values are means and 95% confidence intervals. (b) Post-hoc analysis of LowD versus HighD for amygdala–left VLPFC connectivity (peak voxel MNI: − 42, 30, − 14, k = 67, p < 0.001, corrected) and amygdala-left dACC connectivity (peak voxel MNI: − 2, 6, 40, k = 106, p < 0.001, corrected and MNI: 2, 38, 8, k = 30, p < 0.002, corrected). Color bar represents t values. Bars represent the 95% confidence interval.

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