The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

Childhood maltreatment (CM) is a risk factor for substance use disorders (SUD) in adulthood. Understanding the mechanisms by which people are susceptible or resilient to developing SUD after exposure to CM is important for improving intervention. This case-control study investigated the impact of prospectively assessed CM on biomarkers of endocannabinoid function and emotion regulation in relation to the susceptibility or resilience to developing SUD. Four groups were defined across the dimensions of CM and lifetime SUD (N = 101 in total). After screening, participants completed two experimental sessions on separate days, aimed at assessing the behavioral, physiological, and neural mechanisms involved in emotion regulation. In the first session, participants engaged in tasks assessing biochemical (i.e., cortisol, endocannabinoids), behavioral, and psychophysiological indices of stress and affective reactivity. During the second session, the behavioral and brain mechanisms associated with emotion regulation and negative affect were investigated using magnetic resonance imaging. CM-exposed adults who did not develop SUD, operationally defined as resilient to developing SUD, had higher peripheral levels of the endocannabinoid anandamide at baseline and during stress exposure, compared to controls. Similarly, this group had increased activity in salience and emotion regulation regions in task-based measures of emotion regulation compared to controls, and CM-exposed adults with lifetime SUD. At rest, the resilient group also showed significantly greater negative connectivity between ventromedial prefrontal cortex and anterior insula compared to controls and CM-exposed adults with lifetime SUD. Collectively, these peripheral and central findings point to mechanisms of potential resilience to developing SUD after documented CM exposure.

Biological assay and imaging techniques have made visible a great deal of the machinery of mental illness. Over fifty years of investigation of mood disorders using these technologies has identified several biological regularities in these disorders. Here we present a narrative connecting genetic, cytokine, neurotransmitter, and neural-systems-level findings in major depressive disorder (MDD). Specifically, we connect recent genome-wide findings in MDD to metabolic and immunological disturbance in this disorder and then detail links between immunological abnormalities and dopaminergic signaling within cortico-striatal circuitry. Following this, we discuss implications of reduced dopaminergic tone for cortico-striatal signal conduction in MDD. Finally, we specify some of the flaws in the current model and propose ways forward for advancing multilevel formulations of MDD most efficiently.

Considerable data relate major depressive disorder (MDD) with aberrant immune system functioning. Pro inflammatory cytokines facilitate metabolism of tryptophan along the kynurenine pathway (KP) putatively resulting in reduced neuroprotective and increased neurotoxic KP metabolites in MDD, in addition to modulating metabolic and immune function. This central nervous system hypothesis has, however, only been tested in the periphery. Here, we measured KP-metabolite levels in both plasma and cerebrospinal fluid (CSF) of depressed patients (n = 63/36 respectively) and healthy controls (n = 48/33). Further, we assessed the relation between KP abnormalities and brain-structure volumes, as well as body mass index (BMI), an index of metabolic disturbance associated with atypical depression. Plasma levels of picolinic acid (PIC), the kynurenic/quinolinic acid ratio (KYNA/QUIN), and PIC/QUIN were lower in MDD, but QUIN levels were increased. In the CSF, we found lower PIC in MDD. Confirming previous work, MDD patients had lower hippocampal, and amygdalar volumes. Hippocampal and amygdalar volumes were correlated positively with plasma KYNA/QUIN ratio in MDD patients. BMI was increased in the MDD group relative to the control group. Moreover, BMI was inversely correlated with plasma and CSF PIC and PIC/QUIN, and positively correlated with plasma QUIN levels in MDD. Our results partially confirm previous peripheral KP findings and extend them to the CSF in MDD. We present the novel finding that abnormalities in KP metabolites are related to metabolic disturbances in depression, but the relation between KP metabolites and depression-associated brain atrophy might not be as direct as previously hypothesized.

This systematic review and meta-analysis assesses levels of anhedonia in healthy individuals and patients with mental illness according to the Snaith-Hamilton Pleasure Scale. Question Does anhedonia severity differ among patients with different types of mental illness typically associated with this symptom, and what is considered healthy hedonic functioning? Findings In this systematic review and meta-analysis of 168 studies including more than 16000 participants, anhedonia as measured by the Snaith-Hamilton Pleasure Scale was significantly elevated in patients with major depressive disorder, schizophrenia, substance use disorders, Parkinson disease, and chronic pain. Compared with ongoing major depressive disorder, all other patient groups displayed significantly lower anhedonia. Meaning The findings of this meta-analysis provide a possible set of reference values for anhedonia severity across healthy populations and those with mental illness; these results may have utility for researchers and clinicians evaluating new and existing treatments for anhedonia. Importance Anhedonia, a reduced capacity for pleasure, is described for many psychiatric and neurologic conditions. However, a decade after the Research Domain Criteria launch, whether anhedonia severity differs between diagnoses is still unclear. Reference values for hedonic capacity in healthy humans are also needed. Objective To generate and compare reference values for anhedonia levels in adults with and without mental illness. Data Sources Web of Science, Scopus, PubMed, and Google Scholar were used to list all articles from January 1, 1995 to July 2, 2019, citing the scale development report of a widely used anhedonia questionnaire, the Snaith-Hamilton Pleasure Scale (SHAPS). Searches were conducted from April 5 to 11, 2018, and on July 2, 2019. Study Selection Studies including healthy patients and those with a verified diagnosis, assessed at baseline or in a no-treatment condition with the complete 14-item SHAPS, were included in this preregistered meta-analysis. Data Extraction and Synthesis Random-effects models were used to calculate mean SHAPS scores and 95% CIs separately for healthy participants and patients with current major depressive disorder (MDD), past/remitted MDD, bipolar disorder, schizophrenia, substance use disorders, Parkinson disease, and chronic pain. SHAPS scores were compared between groups using meta-regression, and traditional effect size meta-analyses were conducted to estimate differences in SHAPS scores between healthy and patient samples. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Main Outcomes and Measures Self-reported anhedonia as measured by 2 different formats of the SHAPS (possible ranges, 0-14 and 14-56 points), with higher values on both scales indicating greater anhedonia symptoms. Results In the available literature (168 articles; 16494 participants; 8058 [49%] female participants; aged 13-72 years), patients with current MDD, schizophrenia, substance use disorder, Parkinson disease, and chronic pain scored higher on the SHAPS than healthy participants. Within the patient groups, those with current MDD scored considerably higher than all other groups. Patients with remitted MDD scored within the healthy range (g = 0.1). This pattern replicated across SHAPS scoring methods and was consistent across point estimate and effect size analyses. Conclusions and Relevance The findings of this meta-analysis indicate that the severity of anhedonia may differ across disorders associated with anhedonia. Whereas anhedonia in MDD affects multiple pleasure domains, patients with other conditions may experience decreased enjoyment of only a minority of lifes many rewards. These findings have implications for psychiatric taxonomy development, where dimensional approaches are gaining attention. Moreover, the SHAPS reference values presented herein may be useful for researchers and clinicians assessing the efficacy of anhedonia treatments.

Background

Depersonalization/derealization disorder is a dissociative disorder characterized by feelings of unreality and detachment from the self and surroundings. Depersonalization/derealization disorder is classified as a primary disorder, but depersonalization symptoms are frequently observed in mood and anxiety disorders. In the context of major depressive disorder (MDD), depersonalization symptoms are associated with greater depressive severity as indexed by treatment resistance, inpatient visits, and duration of depressive episodes. In the current investigation, we tested four network-based, neural-functional hypotheses of depersonalization in MDD. These hypotheses were framed in terms of functional relationships between 1) extrastriate body area and default mode network (DMN); 2) hippocampus and DMN; 3) medial prefrontal cortex and ventral striatum; and 4) posterior and anterior insular cortex.

Methods

We conducted functional magnetic resonance imaging during resting state on 28 female patients with MDD and 27 control subjects with no history of a psychiatric disorder. Functional connectivity between seed and target regions as specified by our network-level hypotheses was computed and correlated with scores on the Cambridge Depersonalization Scale. We used a conservative, unbiased bootstrapping procedure to test the significance of neural-behavioral correlations observed under each of the four models tested.

Results

Of the four neural-functional models of depersonalization symptoms tested, only the model proposing that reduced connectivity between the extrastriate body area and DMN predicts higher levels of depersonalization symptoms in MDD was confirmed.

Conclusions

Our results indicate that depersonalization/derealization disorder symptoms in patients with depression are related to reduced functional connectivity between brain regions that are proposed to support processing of body-related (extrastriate body area) and autobiographical (DMN) information.

Rationale

Methamphetamine (MA) use is steadily increasing and thus constitutes a major public health concern. Women seem to be particularly vulnerable to developing MA use disorder, as they initiate use at a younger age and transition more quickly to problematic use. Initial drug responses may predict subsequent use, but little information exists on potential gender differences in the acute effects of MA prior to dependence.

Objective

We examined gender differences in the acute effects of MA on subjective mood and reward-related behavior in healthy, non-dependent humans.

Methods

Men (n = 44) and women (n = 29) completed 4 sessions in which they received placebo or MA under double-blind conditions twice each. During peak drug effect, participants completed the monetary incentive delay task to assess reaction times to cues signaling potential monetary losses or gains, in an effort to determine if MA would potentiate reward-motivated behavior. Cardiovascular and subjective drug effects were assessed throughout sessions.

Results

Overall, participants responded more quickly to cues predicting incentivized trials, particularly large-magnitude incentives, than to cues predicting no incentive. MA produced faster reaction times in women, but not in men. MA produced typical stimulant-like subjective and cardiovascular effects in all participants, but subjective ratings of vigor and (reduced) sedation were greater in women than in men.

Conclusions

Women appear to be more sensitive to the psychomotor-related behavioral and subjective effects of MA. These findings provide initial insight into gender differences in acute effects of MA that may contribute to gender differences in problematic MA use.

Major depressive disorder (MDD) is one of the most significant contributors to the global burden of illness. Diffusion tensor imaging (DTI) is a procedure that has been used in several studies to characterize abnormalities in white matter (WM) microstructural integrity in MDD. These studies, however, have provided divergent findings, potentially due to the large variety of methodological alternatives available in conducting DTI research. In order to determine the importance of different approaches to coregistration of DTI-derived metrics to a standard space, we compared results from two different skeletonized voxel-wise analysis approaches: the standard TBBS pipeline and the Advanced Normalization Tools (ANTs) approach incorporating a symmetric image normalization (SyN) algorithm and a group-wise template (ANTs TBSS). We also assessed effects of applying twelve different fitting procedures for the diffusion tensor. For our dataset, lower fractional anisotropy (FA) and axial diffusivity (AD) in depressed subjects compared with healthy controls were found for both methods and for all fitting procedures. No group differences were found for radial and mean diffusivity indices. Importantly, for the AD metric, the normalization methods and fitting procedures showed reliable differences, both in the volume and in the number of significant between-groups difference clusters detected. Additionally, a significant voxel-based correlation, in the left inferior fronto-occipital fasciculus, between AD and self-reported stress was found only for one of the normalization procedure (ANTs TBSS). In conclusion, the sensitivity to detect group-level effects on DTI metrics might depend on the DTI normalization and/or tensor fitting procedures used.