Specificity of anhedonic alterations in resting-state network connectivity and structure: A transdiagnostic approach
Introduction
Depression is a pervasive disorder affecting more than 400 million people globally (Whiteford et al., 2013) with a lifetime prevalence of 16.6% in the United States (Kessler et al., 2015). High relapse rates (30–50% within one year; (Kessler and Bromet, 2013)), significant economic burden ($210.5 billion annually;(Greenberg et al., 2015)), and serious personal costs (e.g., risk for suicide and functional impairment in work, home, and social activities; (Kessler et al., 2015; Pratt, 2014)) demonstrate that depression is a public health concern. Despite substantial research efforts, the etiology and pathophysiology of depression remains unclear. Advances in neuroimaging approaches have recently focused on dysfunction in brain circuitry. However, findings are inconsistent, demonstrating patterns of both hypo- and hyperconnectivity within broad diagnostic categories of depression (e.g., major depressive disorder, MDD; (Brakowski et al., 2017; Kaiser et al., 2015; Mulders et al., 2015; Sundermann et al., 2014)). As the National Institute of Mental Health Research Doman Criteria (RDoC) highlights limitations of investigating complex psychiatric disorders as a unitary construct (e.g., major depressive disorder), identifying the neurobiological mechanisms of individual symptoms has implications for understanding depression etiology, defining alternative pathways for intervention. Here, we take an RDoC approach to distinguish the specificity of anhedonia from other internalizing symptoms on functional brain circuits implicated in depression and related psychopathology.
Anhedonia, or a reduced ability to experience positive affect or pleasure, is a core symptom of mood disorders that is associated with treatment resistance, prolonged course of mood disturbance, and increased risk for suicide (Ballard et al., 2016; McMakin et al., 2012; Nock and Kazdin, 2002; Uher et al., 2012). Although anhedonia has received little attention relative to major depressive disorder, it is also characteristic of affective, psychotic, substance use, and post-traumatic stress disorders, and it is present in nonclinical populations, making it a critical transdiagnostic construct (Harvey et al., 2007; Keller et al., 2013). Existing medications and most first-line psychological treatments are relatively ineffective at addressing the motivational and reward-processing deficits that characterize anhedonia, rendering it a lingering disturbance that limits an individual's ability to effectively interact with and benefit from positive experiences in the environment (Craske et al., 2016; Der-Avakian and Markou, 2012; Downar et al., 2014). Thus, anhedonia is a clinically relevant symptom dimension of interest that represents an unmet therapeutic need. Given its transdiagnostic significance and associated adverse mental health outcomes, it is critical to characterize the neural circuits that instantiate anhedonia to build a framework for understanding its emergence, course, and pathophysiology.
Relatively few neuroimaging studies have examined the specific effects of anhedonia within mood disorders and even fewer have examined anhedonia as a dimensional, transdiagnostic construct. Emerging evidence suggests that anhedonia is characterized by dysregulated motivational and reward-processing systems, highlighting associated dysfunction in the nucleus accumbens, amygdala, anterior cingulate cortex, prefrontal cortex, basal ganglia, thalamus, and hippocampus (Drevets et al., 2008; Jaworska et al., 2015; Phillips et al., 2015; Pizzagalli et al., 2006; Schlaepfer et al., 2008). While a growing body of literature indicates that anhedonia is associated with problematic modulation of neural networks in a context-specific manner, such as deficits in posterior ventromedial prefrontal cortex connectivity in response to reward-based or appetitive stimuli (Young et al., 2016), the specific effects of anhedonia on intrinsic (resting-state) connectivity are not well known. In MDD samples, elevations in anhedonia have been associated with decreased resting-state functional connectivity between nucleus accumbens and other regions associated with reward-based processing (e.g., orbitofrontal cortex, middle frontal gyrus, inferior parietal lobe) (Liu et al., 2021) as well as no effects on resting-state connectivity (Young et al., 2016). In trauma-exposed individuals, higher levels of anhedonia were associated with increased resting-state functional connectivity between nucleus accumbens and dorsomedial prefrontal regions (Olson et al., 2018). Resting-state and task-based fMRI activity may index distinct sources of individual differences, with resting-state architecture demonstrating significant stability across contexts (Gratton et al., 2018). Understanding the effects of anhedonia on intrinsic architecture may provide insight into context-dependent alterations in network dynamics. Here, we test the effects of anhedonia on intrinsic network dynamics, using a large-scale network approach, in a diagnostically diverse sample.
Research supports the existence of large-scale, task-positive and task-negative (“resting- state”) neural networks across clinical samples and healthy controls (Damoiseaux et al., 2006; De Luca et al., 2006; Glasser et al., 2016; Greicius et al., 2003; Yeo et al., 2011). The default network is most active when the brain is not engaged in a goal-directed task, supporting self-referential thought, emotion regulation, and memory processing (Andrews-Hanna et al., 2014; Cavanna and Trimble, 2006; Leech and Sharp, 2014; Mulders et al., 2015). Default network hyperconnectivity is thought to reflect ruminative states and difficulties with emotion regulation associated with depression (Alexopoulos et al., 2012; Kaiser et al., 2015; Li et al., 2013). The salience, dorsal attention, and frontoparietal networks are broadly involved in allocating attention to salient stimuli (salience), sustaining attention (dorsal attention), and making executive decisions to process external stimuli (frontoparietal; (Corbetta and Shulman, 2002; Fox et al., 2006; Hermans et al., 2014; Rogers et al., 2004; Seeley et al., 2007; Uddin, 2016)). Specifically, the salience network integrates external stimuli with internal states, playing a role in emotional processing and regulation, and modulates other core networks involved in attention and cognition (Hermans et al., 2014; Menon and Uddin, 2010; Seeley et al., 2007; Uddin, 2016). Depression-related dysfunction of the salience network is theorized to be a mechanism of poor engagement with external stimuli and difficulties with emotion regulation (Brakowski et al., 2017; Manoliu et al., 2014; Mulders et al., 2015). The dorsal attention network is associated with top-down directed attention. Aberrant connectivity within depressed individuals may contribute to greater internal focus of attention and limited engagement with external stimuli (Corbetta and Shulman, 2002; Fox et al., 2006; Kaiser et al., 2015; Sambataro et al., 2017). The frontoparietal network demonstrates increased activity during cognitively demanding tasks with speculation that its dysfunction in depression represents poor cognitive flexibility (e.g., rigidity) and difficulty maintaining goal-directed behavior (Corbetta and Shulman, 2002; Fox et al., 2006; Rogers et al., 2004).
As reviewed above, anhedonia is thought to result from alterations in reward-based circuitry and large-scale networks supporting self-referential thought (default), detection of stimuli (salience), attentional control (dorsal attention), and cognitive control (frontoparietal). Although depression is generally associated with default network hyperconnectivity (Alexopoulos et al., 2012; Greicius et al., 2007; Kaiser et al., 2015; Li et al., 2013; Manoliu et al., 2014; Sundermann et al., 2014; Zhu et al., 2012), other network findings have yielded inconclusive or even opposing results (e.g., frontoparietal hypoconnectivity (Alexopoulos et al., 2012; Kaiser et al., 2015; Liston et al., 2014; Lui et al., 2011) vs. frontoparietal hyperconnectivity (Manoliu et al., 2014; Sheline et al., 2001)). Possible explanations for these inconsistencies include inherent limitations in categorical classification nosology to account for heterogeneity of symptom presentation and, in practice, unmeasured disorder and/or symptom co-occurrence (e.g., anxiety disorders and/or symptoms).
The present study evaluated within- and between-network functional connectivity and network composition in the default, frontoparietal, salience, and dorsal attention networks in relation to anhedonia. Symptom measures of worry and anxious arousal, anxiety internalizing symptoms that frequently co-occur with mood symptoms, were used as covariates in all analyses to provide greater specificity of anhedonia modulation. Based on previous research and the internalizing nature of depression, default network hyperconnectivity was anticipated, as well as between-network disruptions.
Section snippets
Participants
65 adults with varying degrees of anxiety and/or depression were recruited from the local community and a community-based mental health clinic. 12 participants were excluded as they did not complete the fMRI session. Our final sample size included 53 adults aged 18–53 years (M = 30.92; SD = 9.45). Demographic and diagnostic characteristics of the sample are provided in Table 1 and Supplementary Methods.
Clinical assessment and symptom measures
Participants completed a structured clinical interview (Structured Clinical Interview for
Analysis of mood and anxiety symptoms
Mood and anxiety symptom measures demonstrated good to excellent reliability (MASQ-AA α = 0.897; MASQ-AD α = 0.942; PSWQ α = = 0.96). As expected, anxious arousal, worry, and depression symptoms were all significantly correlated (Table 2).
Group independent components analysis
The group ICA from 53 participants produced 32 independent components. After comparing spatial maps of the components to the Yeo 7-network parcellation (Yeo et al., 2011), 21 components were identified as significantly correlated (r > 0.2) with at least one
Discussion
Anhedonia plays a prominent role in the onset and duration of mental illness (Kessler and Bromet, 2013; McMakin et al., 2012; Uher et al., 2012), treatment resistance (Carl et al., 2016; Downar et al., 2014; Wardenaar et al., 2012), and risk for suicide (Ballard et al., 2016; Loas et al., 2000; Nock and Kazdin, 2002). However, the neurocircuit mechanisms that characterize anhedonia are poorly understood as previous studies rarely distinguish the effects of anhedonia from other co-occurring
Contributors
W. N. Geller and S. L. Warren developed the study concept and design. W. N. Geller executed all data analyses under the guidance and primary mentorship of S. L. Warren. W. N. Geller and K. Liu drafted the initial version of the manuscript, which was revised by S. L. Warren. All authors approved the final version of the manuscript for submission.
This study was submitted in partial fulfillment of dissertation requirements at the Palo Alto University for Whitney N. Geller.
CRediT authorship contribution statement
Whitney N. Geller: Conceptualization, Data curation, Formal analysis, Software, Visualization, Writing – original draft, Writing – review & editing. Kevin Liu: Writing – original draft, Writing – review & editing. Stacie L. Warren: Conceptualization, Data curation, Methodology, Project administration, Funding acquisition, Supervision.
Declaration of Competing Interest
The authors declare no conflict of interests.
Acknowledgements
Funding: This research was supported by seed funding awarded to S. L. Warren through the Department of Psychology at Palo Alto University.
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