Brain, networks, depression, and more

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Abstract

Depression is a heterogeneous disorder with a highly variable course. Individual responses to treatment are inconsistent, and an established mechanism remains elusive. The classical hypothesis of depression posits that mood disorders are caused by a chemical imbalance in the brain that can be corrected with antidepressant drugs. However, recent evidence indicates that information-processing dysfunction within neural networks might underlie depression, and antidepressant drugs induce plastic changes in neuronal connectivity that gradually lead to improvements in neuronal information processing and recovery. This review presents the major current approaches to understanding the biological mechanisms of major depression, with a focus on complex brain networks.

Introduction

Depression is a syndrome related to the normal emotions of sadness and affliction. However, its mood symptoms are disproportionate and do not remit when the external cause ceases. Indeed, the classical severe states of depression often have no external precipitating cause such as psychosocial events (endogenous depression) (Wakefield et al., 2007, Belmaker and Agam, 2008).

A diagnosis of a major depressive disorder (MDD) requires a distinct mood modification characterized by sadness or irritability and accompanied by at least several psychophysiological changes, such as disturbances in sleep, appetite, or libido, constipation, weight loss and/or gain, the loss of the ability to experience pleasure during work or with friends (anhedonia), crying, suicidal thoughts, and the slowing of speech and actions. These changes must persist a minimum of 2 weeks and interfere considerably with work and family relations. Based on this broad definition, the lifetime incidence of depression is more than 12% in men and 20% in women (Kessler et al., 2003). Some have advocated a much narrower definition of severe depression, which is termed melancholia or vital depression (van Praag, 1987, Belmaker and Agam, 2008).

Despite the prevalence and considerable impact of depression, knowledge about its pathophysiology is rudimentary compared to other common, chronic, and multifactorial conditions, such as type-2 diabetes. There are mainly two major explanations for this discrepancy. First and foremost, observing pathological changes within the living brain remains markedly more difficult compared to other organs. The available techniques to document aberrant brain circuit function are post mortem studies, which have numerous limitations, and neuroimaging techniques, which rely on detecting neuronal activity differences by using indirect markers of activation. Although these approaches have provided important insights into candidate brain regions, simple increases or decreases in regional brain activity most likely are insufficient for explaining the complex array of depressive symptoms. Several animal models have also been used to explore the neural circuitry of depression, but there are important challenges regarding how the information gained from these models should be interpreted (Bouchard, 1994, Belmaker and Agam, 2008, Krishnan and Nestler, 2008).

Second, most depression is idiopathic. The limited understanding of its etiology is reflected by the list of risk factors associated with depression, such as stressful life events, endocrine abnormalities (hypothyroidism and hypercortisolism), cancers (pancreatic adenocarcinoma and breast tumors), and drug side effects (e.g., isotretinoin for acne and interferon-α for hepatitis C), and many others (Nestler et al., 2002, Evans et al., 2005). Genetic association studies have not uncovered strong or consistent genetic risk modifiers (Lopez-Leon et al., 2007), perhaps because of the sheer heterogeneity of depressive syndromes (Nestler et al., 2002, Rush, 2007). Thus, ‘depression genes’ that could be used to generate rodent disease models have not yet been identified. Genetic predispositions are thought to interact with environmental risk factors, such as stressful life events, which can initiate depressive episodes in some patients (Kendler et al., 1999). Still, the tendency to live in a high-stress environment might also be partly heritable (as is the case for ‘risk- or sensation-seekers’) (Mill and Petronis, 2007), emphasizing the strong genetic contribution to all depressive episodes, even those that are ‘environmentally precipitated’ (Krishnan and Nestler, 2008).

Diagnosing depression is subjective and rests on the documentation of a certain number of symptoms that significantly impair function for a given duration (Kessler et al., 2003, Belmaker and Agam, 2008). These diagnostic criteria overlap with anxiety disorders, which have substantial comorbidities with depression (Caspi et al., 2003). Therefore, two ‘depressed’ patients might have only one symptom in common, and a manic episode in one patient, even later in life, switches the diagnosis to bipolar disorder, which is presumably a distinct pathophysiological entity. This symptom-based diagnostic approach clouds the interpretation of genome-wide association studies and neuroimaging and post mortem investigations (Kendler et al., 2006, Belmaker and Agam, 2008, Krishnan and Nestler, 2008).

Thus, depression is a heterogeneous disorder with a highly variable course, an inconsistent response to treatment, and no established mechanism.

This review summarizes the current understanding of the neural and molecular mechanisms of depression, focusing on the leading hypotheses related to brain network theories, and critically examines their strengths and weaknesses in light of recent preclinical and translational studies. Finally, this review highlights new insights garnered from network theories that promise to extend the understanding of depression and improve its treatment.

Section snippets

The monoamine-deficiency hypothesis

The ‘monoamine hypothesis’ of depression originated from the early clinical observations and posits that depression is caused by decreased monoamine levels in the brain (Berton and Nestler, 2006, Pittenger and Duman, 2008). Historically, two structurally unrelated compounds developed for non-psychiatric conditions, iproniazid and imipramine, had potent antidepressant effects in humans and were later shown to enhance central serotonergic or noradrenergic transmission. At the opposite, Reserpine,

Brain networks and depression

Several brain regions and circuits regulate emotion, and executive function. Dysfunctional modifications within these highly interconnected ‘limbic’ regions have been implicated in depression and antidepressant effects (Berton and Nestler, 2006). A large body of post-mortem and neuroimaging studies in depressed patients have reported reductions in the gray-matter volume and glial density of the prefrontal cortex and the hippocampus, which are regions thought to mediate the cognitive aspects of

The network hypothesis of depression: what is it about?

Observations made during the last few years have indicated that there may be an alternative to the chemical view of depression and the action of antidepressants (specifically, an integration of the two) (Castrén, 2005).

This new hypothesis, the network hypothesis, proposes that problems in activity-dependent neuronal communication might underlie depression, and antidepressants may function by improving information processing in the affected neural networks (Castrén, 2005). A key aspect of this

The network hypothesis of depression: pro and con arguments

Perhaps the most important evidence for the network hypothesis is the recent observation that antidepressants increase the production of new neurons in the rodent hippocampus (Malberg et al., 2000, Castrén, 2005). Importantly, the increased neurogenesis facilitated by chronic antidepressant treatment correlates with the behavioral effects produced by antidepressants. Newly generated neurons differentiate over time and are insufficiently mature to participate in information processing until

Human brain dynamics, brain networks, and brain plasticity: an integrative architecture of depression

Recent research has suggested that depressive disorder is associated with disruptions in the topological organization of functional brain networks and that this disruption may contribute to disturbances in mood and cognition in MDD patients (Leistedt et al., 2007a, Leistedt et al., 2007b, Leistedt et al., 2009; Zhang et al., 2011; Price and Drevets, 2012.

Leistedt et al., 2007a, Leistedt et al., 2007b used the Detrended Fluctuation Analysis (DFA) method to analyze and describe the fractal

The network hypothesis of depression: implications for treatment and future insights

The view of mood disorders, especially MDD, as a problem of neuronal networks and information processing in the brain has several important implications (Castrén, 2005).

As described earlier, the most important evidence for this hypothesis is the recent observation that antidepressants increase the production of new neurons in the rodent hippocampus (Malberg et al., 2000). Newly generated neurons differentiate over time and are only sufficiently mature to participate in information processing

Conclusion

Knowledge of the pathophysiology of depression has evolved substantially over the years, from speculations in antiquity about an excess of black bile, to theories focused on ‘chemical imbalances’, and, finally, to the more current hypotheses that incorporate gene–environment interactions, neuronal networks, endocrine, immunological and metabolic mediators, and the cellular, molecular and epigenetic forms of plasticity. However, enormous gaps in our understanding of depression and mostly how to

Role of funding source

Fond national de la recherche scientifique (FRS—FNRS).

Contributors

The authors were actively involved in the review of the scientific literature and content, had full editorial control during the writing of the manuscript and are entirely responsible for the scientific content of this article.

Conflict of interest

The authors declare no conflicts of interest.

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