Common and Distinct Amygdala-Function Perturbations in Depressed vs Anxious Adolescents

Abstract

Context Few studies directly compare amygdala function in depressive and anxiety disorders. Data from longitudinal research emphasize the need for such studies in adolescents. Objective To compare amygdala response to varying attention and emotion conditions among adolescents with major depressive disorder (MDD) or anxiety disorders, relative to adolescents with no psychopathology. Design Case-control study. Setting Government clinical research institute. Participants Eighty-seven adolescents matched on age, sex, intelligence, and social class: 26 with MDD (14 with and 12 without anxiety disorders), 16 with anxiety disorders but no depression, and 45 without psychopathology. Main Outcome Measures Blood oxygen level–dependent signal in the amygdala, measured by means of event-related functional magnetic resonance imaging. During imaging, participants viewed facial expressions (neutral, fearful, angry, and happy) while attention was constrained (afraid, hostility, and nose-width ratings) or unconstrained (passive viewing). Results Left and right amygdala activation differed as a function of diagnosis, facial expression, and attention condition both when patients with comorbid MDD and anxiety were included and when they were excluded (group × emotion × attention interactions, P≤.03). Focusing on fearful face–viewing events, patients with anxiety and those with MDD both differed in amygdala responses from healthy participants and from each other during passive viewing. However, both MDD and anxiety groups, relative to healthy participants, exhibited similar signs of amygdala hyperactivation to fearful faces when subjectively experienced fear was rated. Conclusions Adolescent MDD and anxiety disorders exhibit common and distinct functional neural correlates during face processing. Attention modulates the degree to which common or distinct amygdala perturbations manifest in these patient groups, relative to healthy peers.

Effect of Schizophrenia Risk-Associated Alleles in SREB2 (GPR85) on Functional MRI Phenotypes in Healthy Volunteers

“…In all cases, emotional circuitry was more activated in the carriers of the schizophrenia risk alleles than in the non-risk homozygotes. Increased activation of the amygdala and other regions of the emotional circuitry in response to stimuli with negative valence has been observed in several patient populations with mood or anxiety disorders (eg, Beesdo et al, 2009;Nitschke et al, 2009;Sheline et al, 2001;Stein et al, 2002), but not in patients with schizophrenia, who tend to present the opposite phenotype (Aleman and Kahn, 2005). Finally, recent data from our group have shown that abnormal activation of the amygdala during the FMT is not associated with increased genetic risk for schizophrenia (Rasetti et al, 2009).…”

Integrating research on development and fear learning: a vision for clinical neuroscience?

“…First, studies show that pediatric anxiety disorders involve enhanced sensitivity in the fear circuit, particularly the amygdala and vPFC. [8][9][10] Second, as with developmental data from longitudinal studies, fMRI studies find signs of both specificity and nonspecificity in the correlates of various psychopathologies. Thus, this work does find signs of specificity: fear-circuit perturbations in pediatric anxiety disorders do not occur in pediatric behavior disorders.…”

“…[11] On the other hand, similar forms of fear-circuit dysfunction manifest in youths with anxiety disorders, major depressive disorder, and risk factors for these conditions related to temperament, genetics, or family history. [9,12,13] Each of these groups, unlike some children with certain behavior disorders, also manifests amygdala hyperactivity to fear faces. Finally, the data suggest that fear-circuit dysfunction relates to perturbations in plasticity.…”

“…[14,15] In other contexts, when children are cued to attend to other aspects of threats, both pediatric anxiety and major depressive disorders can appear similarly hypersensitive to threats, whereas in still other contexts, all three groups of children can appear distinct, in terms of their amygdala-based sensitivities. [9] Thus, experimental manipulation of the psychological context can either acutely unmask or obscure anxiety-related between-group differences in fear-circuit function. fMRI work reveals fear-circuit dysfunction in pediatric anxiety-related states to represent the failure to adapt appropriately to the varied contexts in which threats are processed, rather than an overall tendency across many contexts to exhibit enhanced fear-circuit activity.…”

Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study

“…Supervised machine learning algorithms such as support vector machines (SVM) have been used to investigate the potential use of these biomarkers for separating different disorders based on their neural correlates (Grotegerd et al., 2013; Lim et al., 2013; Lueken, Hilbert, Wittchen, Reif, & Hahn, 2015; MacMaster, Carrey, Langevin, Jaworska, & Crawford, 2014; Ota et al., 2013; Pantazatos, Talati, Schneier, & Hirsch, 2014; Schnack et al., 2014; Serpa et al., 2014; Takizawa et al., 2014). Given that GAD and MD do not only show common but also separate neural correlates (Beesdo et al., 2009; Canu et al., 2015; Etkin & Schatzberg, 2011; Oathes, Patenaude, Schatzberg, & Etkin, 2015), machine learning might also be successfully applied to the problem of recognizing GAD patients and separating them from MD patients. Biomarkers do not have to be restricted, however, to neural information (Boksa, 2013; Singh & Rose, 2009).…”