The neuroscience of depression: Implications for assessment and intervention
Introduction
Major Depressive Disorder (MDD) is a prevalent psychiatric disorder characterized by significant role impairment, suicide risk, economic burden, and the largest number of years lived with disability in the US (US Burden of Disease Collaborators, 2013). Almost 20% of Americans will experience a major depressive episode in their lifetime (Hirschfeld, 2012); moreover, up to 80% of these individuals will have multiple depressive episodes (Bulloch, Williams, Lavorato, & Patten, 2014). Given its high prevalence, recurrence, and enormous personal and societal costs, it is not surprising that the World Health Organization projects that MDD will be the single most burdensome disease in the world in this century (Moussavi et al., 2007).
In this review, we synthesize multimodal neuroimaging data that inform the diagnosis and intervention of MDD, taking into consideration recent advances in the nosology and treatment of depression. With respect to diagnosis, we specifically consider the advantages of addressing the heterogeneity of MDD by integrating a dimensional Research Domain Criteria (RDoC) approach in evaluating neuroimaging characteristics of depression. In terms of intervention, we review recent research demonstrating the neural effects of the most evidence-based interventions, of rapid-acting antidepressants (e.g. ketamine), and the regional and global effects of targeting neural networks.
According to the Diagnostic and Statistical Manual-Fifth Edition (DSM-5), a diagnosis of MDD requires a persistent disturbance of mood (sadness, and/or in children, irritability) or a loss of interest or pleasure in virtually all activities, in addition to at least four of the following symptoms: sleep disturbance, guilt, loss of energy, impaired concentration, change in appetite, psychomotor agitation or retardation, and suicidal ideation (American Psychiatric Association, 2013). Given these varied symptoms, it is not surprising that depression is a heterogeneous disorder; indeed, each of these symptoms has specific risk factors, severities, and trajectories (Fried, Nesse, Zivin, Guille, & Sen, 2013). Further, both MDD and its individual symptoms often co-occur with other psychiatric disorders (Curry et al., 2014); they also manifest differently both across developmental stages (Dekker et al., 2007) and between males and females (Goodwin & Gotlib, 2004). This heterogeneity and comorbidity has posed significant challenges for the diagnosis and treatment of depression and has hindered our ability to predict long-term outcome of MDD. Although investigators and clinicians have made significant advancements in identifying depressive symptoms and in managing MDD with multimodal pharmacological and psychological treatments, there are wide variations in the efficacy and tolerability of interventions (Perlis, 2014), and problems related to symptom relapse and medication non-adherence (Sato & Yeh, 2013). Further, despite the well-documented burden of MDD, we do not yet understand the pathophysiology of this disorder.
In attempting to address this issue, researchers have begun to examine psychobiological aspects of MDD in the context of RDoC. For example, in order to advance our understanding of the pathophysiology and outcome of MDD, investigators have attempted to deconstruct depression along unitary psychopathological dimensions, such as anhedonia (Downar et al., 2013) or negative affect (Vrieze et al., 2014). In a recent review, Dillon et al. (2014) related anhedonic behavior to deficits in psychological functions that rely heavily on dopamine signaling, especially cost/benefit decision-making and reward learning, influenced negatively both by acute threats and chronic stress. Other studies have documented shared characteristics between MDD and other disorders associated with blunted or negative affect, such as schizophrenia, by focusing on similar reduced expressive behaviors measured by computerized acoustic analysis of speech (Cohen, Najolia, Kim, & Dinzeo, 2012). Although these are recent efforts, these dimensions of depression may aid in predicting treatment outcome. Researchers have also characterized cognitive impairments associated with depression. For example, Gotlib and Joormann (2010) noted that depressed individuals have been found consistently to be characterized by difficulties with inhibition of negative information and deficits in working memory, ruminative responses to negative mood states and life events, and the inability to use positive stimuli to regulate negative mood.
Importantly, recent advances in brain imaging have allowed researchers to augment studies of cognitive and behavioral impairments in MDD with an examination of neural circuit-level mechanisms that may underlie these difficulties (Foland-Ross & Gotlib, 2012). Specifically, neuroimaging studies have documented structural and functional neural characteristics critical to the pathogenesis of MDD (see Hamilton, Chen, & Gotlib, 2013 for a recent review). These studies have also demonstrated that anomalies in distributed, integrated neural networks that involve multiple brain regions, linked structurally and functionally, underlie the disturbances in cognitive functioning that have been documented in MDD (Sacher et al., 2012). Only recently, however, have researchers begun to examine explicitly the nature of the relation between clinical and neural network markers of MDD at various points during the onset and course of the disorder, and to use neural characteristics to predict treatment outcome. We believe that neuroimaging is a promising tool for elucidating the pathogenesis of MDD; it is a safe, noninvasive procedure that is ideally suited for simultaneously identifying aberrant behavior, brain structure, and brain function in MDD. Indeed, with neuroimaging, we can bridge a clinical assessment of depressive symptoms with an examination of brain abnormalities to advance our understanding of the pathophysiology of MDD.
We have three broad goals in this review. We describe anomalies in neural structure and function in adults and youth with MDD and discuss the implications of these abnormalities, first, for the assessment of depression, and second, for approaches to intervention with this disorder. Finally, we offer directions for future research that we believe will advance our understanding of biological factors that are implicated in the etiology and course of MDD, and in recovery from depression. We begin by presenting a brief review of neural aspects of unipolar depression and their implications for assessment of MDD.
Section snippets
How the neuroscience of depression can inform assessment
Researchers have consistently documented impairments in emotional functioning and emotion regulation in MDD (Gotlib & Joormann, 2010). Moreover, these difficulties have been found to predict the early onset (Klein et al., 2013) and the recurrence of depressive episodes (Lewinsohn, Allen, Seeley, & Gotlib, 1999), suggesting that impairments in specific domains of emotional functioning reflect stable vulnerabilities that place individuals at increased risk for experiencing recurrent episodes of
How the neuroscience of depression can inform intervention
Investigators have begun to use imaging techniques to examine neural aspects of different interventions in individuals diagnosed with MDD. Across most imaging studies, treatment such as antidepressants, and especially those affecting the serotonergic system, have been shown to modulate the volumes, functions and biochemistry of brain regions implicated in MDD, including the DLFPC, ACC and amygdala (Bellani, Dusi, Yeh, Soares, & Brambilla, 2011). In the following sections, we describe changes in
Limitations and directions for future research
In this article we have described implications of neuroimaging for assessment and treatment of MDD. There are several challenges for research in this field. For example, patients are diagnosed and treated on the basis of symptom clusters that are heterogeneous and are likely to be caused by numerous and divergent biological factors. Two patients can exhibit non-overlapping clusters of symptoms, but share the same diagnosis and treatment recommendation. Another challenge involves distinguishing
Conflict of interest
Drs Singh and Gotlib report no known conflicts of interest associated with this publication.
Acknowledgments
Preparation of this article was facilitated by National Institute of Mental Health Grants MH085919 to MKS and MH074849, MH101495, and MH101545 to IHG.
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