Serotonin modulates neuroplasticity, especially during early life, and dysfunctions in both systems likewise contribute to pathophysiology of depression. Recent findings demonstrate that serotonin reuptake inhibitors trigger reactivation of juvenile-like neuroplasticity. How these findings translate to clinical antidepressant treatment in major depressive disorder remains unclear. With this review, we link preclinical with clinical work on serotonin and neuroplasticity to bring two pathophysiologic models in clinical depression closer together. Dysfunctional developmental plasticity impacts on later-life cognitive and emotional functions, changes of synaptic serotonin levels and receptor levels are coupled with altered synaptic plasticity and neurogenesis. Structural magnetic resonance imaging in patients reveals disease-state-specific reductions of gray matter, a marker of neuroplasticity, and reversibility upon selective serotonin reuptake inhibitor treatment. Translational evidence from magnetic resonance imaging in animals support that reduced densities and sizes of neurons and reduced hippocampal volumes in depressive patients could be attributable to changes of serotonergic neuroplasticity. Since ketamine, physical exercise or learning enhance neuroplasticity, combinatory paradigms with selective serotonin reuptake inhibitors could enhance clinical treatment of depression.

The anticipation of favourable or unfavourable events is a key component in our daily life. However, the temporal dynamics of anticipation processes in relation to brain activation are still not fully understood.A modified version of the monetary incentive delay task was administered during separate functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) sessions in the same 25 participants to assess anticipatory processes with a multi-modal neuroimaging set-up.During fMRI, gain and loss anticipation were both associated with heightened activation in ventral striatum and reward-related areas. EEG revealed most pronounced P300 amplitudes for gain anticipation, whereas CNV amplitudes distinguished neutral from gain and loss anticipation. Importantly, P300, but not CNV amplitudes, were correlated to neural activation in the ventral striatum for both gain and loss anticipation. Larger P300 amplitudes indicated higher ventral striatum blood oxygen level dependent (BOLD) response.Early stimulus evaluation processes indexed by EEG seem to be positively related to higher activation levels in the ventral striatum, indexed by fMRI, which are usually associated with reward processing. The current results, however, point towards a more general motivational mechanism processing salient stimuli during anticipation.

Treatment outcomes for major depressive disorder (MDD) need to be improved. Presently, no clinically relevant tools have been established for stratifying subgroups or predicting outcomes. This literature review sought to investigate factors closely linked to outcome and summarize existing and novel strategies for improvement. The results show that early recognition and treatment are crucial, as duration of untreated depression correlates with worse outcomes. Early improvement is associated with response and remission, while comorbidities prolong course of illness. Potential biomarkers have been explored, including hippocampal volumes, neuronal activity of the anterior cingulate cortex, and levels of brain-derived neurotrophic factor (BDNF) and central and peripheral inflammatory markers (e.g., translocator protein (TSPO), interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor alpha (TNFα)). However, their integration into routine clinical care has not yet been fully elucidated, and more research is needed in this regard. Genetic findings suggest that testing for CYP450 isoenzyme activity may improve treatment outcomes. Strategies such as managing risk factors, improving clinical trial methodology, and designing structured step-by-step treatments are also beneficial. Finally, drawing on existing guidelines, we outline a sequential treatment optimization paradigm for selecting first-, second-, and third-line treatments for acute and chronically ill patients. Well-established treatments such as electroconvulsive therapy (ECT) are clinically relevant for treatment-resistant populations, and novel transcranial stimulation methods such as theta-burst stimulation (TBS) and magnetic seizure therapy (MST) have shown promising results. Novel rapid-acting antidepressants, such as ketamine, may also constitute a paradigm shift in treatment optimization for MDD.

Social anxiety disorder (SAD) is characterized by over-reactivity of fear-related circuits in social or performance situations and associated with marked social impairment. We used dynamic causal modeling (DCM), a method to evaluate effective connectivity, to test our hypothesis that SAD patients would exhibit dysfunctions in the amygdala–prefrontal emotion regulation network. Thirteen unmedicated SAD patients and 13 matched healthy controls performed a series of facial emotion and object discrimination tasks while undergoing fMRI. The emotion-processing network was identified by a task-related contrast and motivated the selection of the right amygdala, OFC, and DLPFC for DCM analysis. Bayesian model averaging for DCM revealed abnormal connectivity between the OFC and the amygdala in SAD patients. In healthy controls, this network represents a negative feedback loop. In patients, however, positive connectivity from OFC to amygdala was observed, indicating an excitatory connection. As we did not observe a group difference of the modulatory influence of the FACE condition on the OFC to amygdala connection, we assume a context-independent reduction of prefrontal control over amygdalar activation in SAD patients. Using DCM, it was possible to highlight not only the neuronal dysfunction of isolated brain regions, but also the dysbalance of a distributed functional network.

Reflecting one's mental self is a fundamental process for evaluating the personal relevance of life events and for moral decision making and future envisioning. Although the corresponding network has been receiving growing attention, the driving neurochemical mechanisms of the default mode network (DMN) remain unknown. Here we combined positron emission tomography and functional magnetic resonance imaging to investigate modulations of the DMN via serotonin-1A receptors (5-HT 1A ), separated for 5-HT autoinhibition (dorsal raphe nucleus) and local inhibition (heteroreceptors in projection areas). Using two independent approaches, regional 5-HT 1A binding consistently predicted DMN activity in the retrosplenial cortex for resting-state functional magnetic resonance imaging and the Tower of London task. On the other hand, both local and autoinhibitory 5-HT 1A binding inversely modulated the posterior cingulate cortex, the strongest hub in the resting human brain. In the frontal part of the DMN, a negative association was found between the dorsal medial prefrontal cortex and local 5-HT 1A inhibition. Our results indicate a modulation of key areas involved in self-referential processing by serotonergic neurotransmission, whereas variations in 5-HT 1A binding explained a considerable amount of the individual variability in the DMN. Moreover, the brain regions associated with distinct introspective functions seem to be specifically regulated by the different 5-HT 1A binding sites. Together with previously reported modulations of dopamine and GABA, this regional specialization suggests complex interactions of several neurotransmitters driving the default mode network.functional connectivity | resting-state networks | neurotransmitter modulation

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Our results reflect that during early anticipation uncertainty is strongly associated with affective mechanisms and seems to be a more salient event compared to certain anticipation. During the last 2 s before stimulation, attentional control mechanisms are initiated related to the increased salience of uncertainty. Furthermore, stimulus-specific preparatory mechanisms during certain anticipation also shaped the response to stimulation, underlining the adaptive value of stimulus-targeted preparatory activity which is less likely when facing an uncertain event.

Treatment outcomes for major depressive disorder (MDD) need to be improved. Presently, no clinically relevant tools have been established for stratifying subgroups or predicting outcomes. This literature review sought to investigate factors closely linked to outcome and summarize existing and novel strategies for improvement. The results show that early recognition and treatment are crucial, as duration of untreated depression correlates with worse outcomes. Early improvement is associated with response and remission, while comorbidities prolong course of illness. Potential biomarkers have been explored, including hippocampal volumes, neuronal activity of the anterior cingulate cortex, and levels of brain-derived neurotrophic factor (BDNF) and central and peripheral inflammatory markers (e.g., translocator protein (TSPO), interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor alpha (TNFα)). However, their integration into routine clinical care has not yet been fully elucidated, and more research is needed in this regard. Genetic findings suggest that testing for CYP450 isoenzyme activity may improve treatment outcomes. Strategies such as managing risk factors, improving clinical trial methodology, and designing structured step-by-step treatments are also beneficial. Finally, drawing on existing guidelines, we outline a sequential treatment optimization paradigm for selecting first-, second-, and third-line treatments for acute and chronically ill patients. Well-established treatments such as electroconvulsive therapy (ECT) are clinically relevant for treatment-resistant populations, and novel transcranial stimulation methods such as theta-burst stimulation (TBS) and magnetic seizure therapy (MST) have shown promising results. Novel rapid-acting antidepressants, such as ketamine, may also constitute a paradigm shift in treatment optimization for MDD. Depression: a major and relentless burdenMajor depressive disorder (MDD) is the most common psychiatric disease and a worldwide leading cause of years lived with disability 1,2 . In addition, the bulk of suicides are linked to a diagnosis of MDD. Despite the high prevalence rate of MDD and ongoing efforts to increase knowledge and skills for healthcare providers, the illness remains both underdiagnosed and undertreated 3 . Many novel strategies with potentially broad impact are not yet ready for 'prime time', as they are either in early experimental stages or undergoing regulatory processes for approval. This review sought to: (1) provide a synopsis of key factors associated with outcomes in MDD, and (2) synthesize the existing literature on novel treatment strategies for depression. A literature search was conducted using the search terms 'depression', 'antidepressant', 'outcome', 'predictor', '(bio)marker', 'treatment-resistant depression (TRD)', and 'chronic depression' in addition to combinations of these terms. The search was conducted in PubMed, Scopus, and Google Scholar with no restrictions on time period and concluded in October 2018. Notably, ...