A stress-sensitive frontostriatal circuit supporting effortful reward-seeking behavior

Microviridae bacteriophages influence behavioural hallmarks of food addiction via tryptophan and tyrosine signalling pathways

Food addiction contributes to the obesity pandemic, but the connection between how the gut microbiome is linked to food addiction remains largely unclear. Here we show that Microviridae bacteriophages, particularly Gokushovirus WZ-2015a, are associated with food addiction and obesity across multiple human cohorts. Further analyses reveal that food addiction and Gokushovirus are linked to serotonin and dopamine metabolism. Mice receiving faecal microbiota and viral transplantation from human donors with the highest Gokushovirus load exhibit increased food addiction along with changes in tryptophan, serotonin and dopamine metabolism in different regions of the brain, together with alterations in dopamine receptors. Mechanistically, targeted tryptophan analysis shows lower anthranilic acid (AA) concentrations associated with Gokushovirus. AA supplementation in mice decreases food addiction and alters pathways related to the cycle of neurotransmitter synthesis release. In Drosophila, AA regulates feeding behaviour and addiction-like ethanol preference. In summary, this study proposes that bacteriophages in the gut microbiome contribute to regulating food addiction by modulating tryptophan and tyrosine metabolism.

Identifying dysfunctional cell types and circuits in animal models for psychiatric disorders with calcium imaging

A central goal of neuroscience is to understand how the brain transforms external stimuli and internal bodily signals into patterns of activity that underlie cognition, emotional states, and behavior. Understanding how these patterns of activity may be disrupted in mental illness is crucial for developing novel therapeutics. It is well appreciated that psychiatric disorders are complex, circuit-based disorders that arise from dysfunctional activity patterns generated in discrete cell types and their connections. Recent advances in large-scale, cell-type specific calcium imaging approaches have shed new light on the cellular, circuit, and network-level dysfunction in animal models for psychiatric disorders. Here, we highlight a series of recent findings over the last ~10 years from in vivo calcium imaging studies that show how aberrant patterns of activity in discrete cell types and circuits may underlie behavioral deficits in animal models for several psychiatric disorders, including depression, anxiety, autism spectrum disorders, and schizophrenia. These advances in calcium imaging in pre-clinical models demonstrate the power of cell-type-specific imaging tools in understanding the underlying dysfunction in cell types, activity patterns, and neural circuits that may contribute to disease and provide new blueprints for developing more targeted therapeutics and treatment strategies.

Efficacy comparison of five antidepressants in treating anxiety and depression in cancer and non-cancer patients

Introduction

Cancer patients have a heightened susceptibility to anxiety and depressive disorders, which significantly impact the effectiveness of cancer treatments and long-term quality of life. This study aimed to compare the efficacy of different antidepressants in cancer and non-cancer patients.

Methods

A total of 610 patients diagnosed with depressive episodes and/or anxiety disorders were retrospectively included and divided into a cancer group and a non-cancer control group. Antidepressants used included escitalopram, duloxetine, sertraline, venlafaxine, and vortioxetine, combined with trazodone or not. The Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder Questionnaire-7 (GAD-7) scores were used to evaluate the efficacy after 4 weeks and 8 weeks of systematic antidepressants treatment.

Results

Compared to the non-cancer group, the cancer group had higher proportions of females, older individuals, and patients with poor sleep quality, while reporting fewer somatic symptoms at baseline (all p < 0.05). PHQ-9 and GAD-7 scores in cancer patients treated with antidepressants were significantly lower than baseline at week 4 and week 8 (all p < 0.05). The sertraline group demonstrated significantly less improvement in GAD-7 scores at week 4 and in both GAD-7 and PHQ-9 scores at week 8 compared to the escitalopram group, while duloxetine, venlafaxine, and vortioxetine showed comparable efficacy to escitalopram. Antidepressants combined with trazodone showed significant improvement in PHQ-9 scores at week 4 compared to those without trazodone. The gynecological cancer group showed significantly more improvement in GAD-7 and PHQ-9 scores at week 4 and 8 compared to breast cancer patients.

Conclusion

Antidepressant treatment in cancer patients with anxiety and depression is as effective as in non-cancer patients. The efficacy of escitalopram is comparable to duloxetine, venlafaxine, and vortioxetine, all of which outperformed sertraline in cancer patients.

Frontostriatal salience network expansion in individuals in depression

Decades of neuroimaging studies have shown modest differences in brain structure and connectivity in depression, hindering mechanistic insights or the identification of risk factors for disease onset1. Furthermore, whereas depression is episodic, few longitudinal neuroimaging studies exist, limiting understanding of mechanisms that drive mood-state transitions. The emerging field of precision functional mapping has used densely sampled longitudinal neuroimaging data to show behaviourally meaningful differences in brain network topography and connectivity between and in healthy individuals2-4, but this approach has not been applied in depression. Here, using precision functional mapping and several samples of deeply sampled individuals, we found that the frontostriatal salience network is expanded nearly twofold in the cortex of most individuals with depression. This effect was replicable in several samples and caused primarily by network border shifts, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was stable over time, unaffected by mood state and detectable in children before the onset of depression later in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in frontostriatal circuits that tracked fluctuations in specific symptoms and predicted future anhedonia symptoms. Together, these findings identify a trait-like brain network topology that may confer risk for depression and mood-state-dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling

MAIN PROBLEM

Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia.

METHODS

A predictive model used connectome-based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre-treatment functional connectivity (FC) data from 59 patients with MDD. Node-based FC analysis was employed to compare differences in FC patterns between melancholic and non-melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients.

RESULTS

CPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non-melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_bonferroni = 0.0303). This distinct FC pattern effectively discriminated between melancholic and non-melancholic MDD patients, achieving a sensitivity of 93.54%, specificity of 67.86%, and an overall accuracy of 81.36% using the SVM method.

CONCLUSIONS

This study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non-melancholic MDD patients. These findings provide guidance for clinical treatment.

The brain's go-getter circuit: Anterior cingulate cortex to nucleus accumbens and its disruption by stress

In this issue of Neuron, Fetcho, Parekh, et al.1 show that neurons in the anterior cingulate cortex (ACC) projecting to the nucleus accumbens (NAc) are essential for integrating reward and effort evaluation in mice, and that this circuit is sensitive to exposure to stress hormones.