Major depressive disorder (MDD) is a polygenic disorder associated with brain alterations but until recently, there have been no brain-based metrics to quantify individual-level variation in brain morphology. Here, we evaluated and compared the performance of a new brain-based ‘Regional Vulnerability Index’ (RVI) with polygenic risk scores (PRS), in the context of MDD. We assessed associations with syndromal MDD in an adult sample (N = 702, age = 59 ± 10) and with subclinical depressive symptoms in a longitudinal adolescent sample (baseline N = 3,825, age = 10 ± 1; 2-year follow-up N = 2,081, age = 12 ± 1).

MDD-RVIs quantify the correlation of the individual’s corresponding brain metric with the expected pattern for MDD derived in an independent sample. Using the same methodology across samples, subject-specific MDD-PRS and six MDD-RVIs based on different brain modalities (subcortical volume, cortical thickness, cortical surface area, mean diffusivity, fractional anisotropy, and multimodal) were computed.

In adults, MDD-RVIs (based on white matter and multimodal measures) were more strongly associated with MDD (β = 0.099–0.281, PFDR = 0.001–0.043) than MDD-PRS (β = 0.056–0.152, PFDR = 0.140–0.140). In adolescents, depressive symptoms were associated with MDD-PRS at baseline and follow-up (β = 0.084–0.086, p = 1.38 × 10−4−4.77 × 10−4) but not with any MDD-RVIs (β < 0.05, p > 0.05).

Our results potentially indicate the ability of brain-based risk scores to capture a broader range of risk exposures than genetic risk scores in adults and are also useful in helping us to understand the temporal origins of depression-related brain features. Longitudinal data, specific to the developmental period and on white matter measures, will be useful in informing risk for subsequent psychiatric illness.

There is significant heterogeneity in cognitive function in patients with bipolar I disorder (BDI); however, there is a dearth of research into biological mechanisms that might underlie cognitive heterogeneity, especially at disease onset. To this end, this study investigated the association between accelerated or delayed age-related brain structural changes and cognition in early-stage BDI.

First episode patients with BDI (n = 80) underwent cognitive assessment to yield demographically normed composite global and domain-specific scores in verbal memory, non-verbal memory, working memory, processing speed, attention, and executive functioning. Structural magnetic resonance imaging data were also collected from all participants and subjected to machine learning to compute the brain-predicted age difference (brainPAD), calculated by subtracting chronological age from age predicted by neuroimaging data (positive brainPAD values indicating age-related acceleration in brain structural changes and negative values indicating delay). Patients were divided into tertiles based on brainPAD values, and cognitive performance compared amongst tertiles with ANCOVA.

Patients in the lowest (delayed) tertile of brainPAD values (brainPAD range −17.9 to −6.5 years) had significantly lower global cognitive scores (p = 0.025) compared to patients in the age-congruent tertile (brainPAD range −5.3 to 2.4 yrs), and significantly lower verbal memory scores (p = 0.001) compared to the age-congruent and accelerated (brainPAD range 2.8 to 16.1 yrs) tertiles.

These results provide evidence linking cognitive dysfunction in the early stage of BDI to apparent delay in typical age-related brain changes. Further studies are required to assess how age-related brain changes and cognitive functioning evolve over time.

The COVID-19 pandemic has been associated with increased levels of depression and anxiety with implications for the use of antidepressant medications.

The incident rate of antidepressant fills before and during the COVID-19 pandemic were compared using interrupted time-series analysis followed by comprehensive sensitivity analyses on data derived from electronic medical records from a large health management organization providing nationwide services to 14% of the Israeli population. The dataset covered the period from 1 January 2013 to 1 February 2021, with 1 March 2020 onwards defined as the period of the COVID-19 pandemic. Forecasting analysis was implemented to test the effect of the vaccine roll-out and easing of social restrictions on antidepressant use.

The sample consisted of 852 233 persons with a total antidepressant incident fill count of 139 535.4 (total cumulative rate per 100 000 = 16 372.91, 95% CI 16 287.19–16 459.01). We calculated the proportion of antidepressant prescription fills for the COVID-19 period, and the counterfactual proportion for the same period, assuming COVID-19 had not occurred. The difference in these proportions was significant [Cohen's h = 10−3 (0.16), 95% CI 10−3 ( − 0.71 to 1.03)]. The pandemic was associated with a significant increase in the slope of the incident rate of antidepressant fills (slope change = 0.01, 95% CI 0.00–0.03; p = 0.04) and a monthly increase of 2% compared to the counterfactual (the estimated rate assuming no pandemic occurred). The increased rate was more pronounced in women, and was not modified by lockdown on/off periods, socioeconomic or SARS-CoV-2 status. The rate of observed antidepressant fills was similar to that forecasted under the assumption of ongoing COVID-19 distress.

These findings underscore the toll of the pandemic on mental health and inform mental health policy and service delivery during and after implementing COVID-19 attenuation strategies.

Metabolic dysregulation is currently considered a major risk factor for hippocampal pathology. The aim of the present study was to characterize the influence of key metabolic drivers on functional connectivity of the hippocampus in healthy adults.

Insulin resistance was directly quantified by measuring steady-state plasma glucose (SSPG) concentration during the insulin suppression test and fasting levels of insulin, glucose, leptin, and cortisol, and measurements of body mass index and waist circumference were obtained in a sample of healthy cognitively intact adults (n = 104). Resting-state neuroimaging data were also acquired for the quantification of hippocampal functional cohesiveness and integration with the major resting-state networks (RSNs). Data-driven analysis using unsupervised machine learning (k-means clustering) was then employed to identify clusters of individuals based on their metabolic and functional connectivity profiles.

K-means clustering identified two clusters of increasing metabolic deviance evidenced by cluster differences in the plasma levels of leptin (40.36 (29.97) vs. 27.59 (25.58) μg/L) and the degree of insulin resistance (SSPG concentration: 161.63 (65.27) vs. 125.72 (66.81) mg/dL). Individuals in the cluster with higher metabolic deviance showed lower functional cohesiveness within each hippocampus and lower integration of posterior and anterior components of the left and right hippocampus with the major RSNs. The two clusters did not differ in general intellectual ability or episodic memory.

We identified two clusters of individuals differentiated by abnormalities in insulin resistance, leptin levels, and hippocampal connectivity, with one of the clusters showing greater deviance. These findings support the link between metabolic dysregulation and hippocampal function even in nonclinical samples.

One of the challenges in human neuroscience is to uncover associations between brain organization and psychopathology in order to better understand the biological underpinnings of mental disorders. Here, we aimed to characterize the neural correlates of psychopathology dimensions obtained using two conceptually different data-driven approaches.

Dimensions of psychopathology that were either maximally dissociable or correlated were respectively extracted by independent component analysis (ICA) and exploratory factor analysis (EFA) applied to the Childhood Behavior Checklist items from 9- to 10-year-olds (n = 9983; 47.8% female, 50.8% white) participating in the Adolescent Brain Cognitive Development study. The patterns of brain morphometry, white matter integrity and resting-state connectivity associated with each dimension were identified using kernel-based regularized least squares and compared between dimensions using Spearman’s correlation coefficient.

ICA identified three psychopathology dimensions, representing opposition–disinhibition, cognitive dyscontrol, and negative affect, with distinct brain correlates. Opposition–disinhibition was negatively associated with cortical surface area, cognitive dyscontrol was negatively associated with anatomical and functional dysconnectivity while negative affect did not show discernable associations with any neuroimaging measure. EFA identified three dimensions representing broad externalizing, neurodevelopmental, and broad Internalizing problems with partially overlapping brain correlates. All EFA-derived dimensions were negatively associated with cortical surface area, whereas measures of functional and structural connectivity were associated only with the neurodevelopmental dimension.

This study highlights the importance of cortical surface area and global connectivity for psychopathology in preadolescents and provides evidence for dissociable psychopathology dimensions with distinct brain correlates.

Studies of COVID-19 pandemic biopsychosocial exposure and schizophrenia risk showed contradictory results, were undertaken early in the pandemic, and did not consider lockdowns or COVID-19 infection. Hence, we examined the association between COVID-19 biopsychosocial exposure and incident schizophrenia.

An interrupted time-series study design was implemented based on Israeli electronic health records from 2013 to 2021 with national coverage. The period coinciding with the COVID-19 pandemic biopsychosocial exposures from March 2020 to February 2021 was classified as exposed, otherwise unexposed. The effect of the COVID-19 pandemic on incident schizophrenia was quantified by fitting a Poisson regression and modeling the relative risk (RR) and corresponding 95% confidence intervals (CI). Three scenarios were projected from the third lockdown to 10 months to forecast incident schizophrenia rates and their associated 95% prediction intervals (PI).

The total population (N = 736,356) yielded 4,310 cases of incident schizophrenia over time. The primary analysis showed that the period exposed to the COVID-19 pandemic was associated with a reduced RR (RR = 0.81, 95% CI = 0.73, 0.91, p < 0.001). This conclusion was supported in 12 sensitivity analyses, including scrutinizing lockdowns and COVID-19 infection status. Two of three forecast scenarios projected an incident increase (6.74, 95% PI = 5.80, 7.84; 7.40, 95% PI = 6.36, 8.60).

The reduced risk of schizophrenia during the pandemic suggests no immediate triggering of new onsets either by the virus or the pandemic-induced psychosocial adversities. Once restrictions are lifted, the increased projected presentations have implications for clinicians and healthcare policy.

To characterize the association between the protracted biopsychosocial coronavirus disease 2019 (COVID-19) pandemic exposures and incident suicide attempt rates.

Data were from a nationally representative cohort based on electronic health records from January 2013 to February 2021 (N = 852 233), with an interrupted time series study design. For the primary analysis, the effect of COVID-19 pandemic on incident suicide attempts warranting in-patient hospital treatment was quantified by fitting a Poisson regression and modeling the relative risk (RR) and the corresponding 95% confidence intervals (CIs). Scenarios were forecast to predict attempted suicide rates at 10 months after social mitigation strategies. Fourteen sensitivity analyses were performed to test the robustness of the results.

Despite the increasing trend in the unexposed interval, the interval exposed to the COVID-19 pandemic was statistically significant (p < 0.001) associated with a reduced RR of incident attempted suicide (RR = 0.63, 95% CI 0.52–0.78). Consistent with the primary analysis, sensitivity analysis of sociodemographic groups and methodological factors were statistically significant (p < 0.05). No effect modification was identified for COVID-19 lockdown intervals or COVID-19 illness status. All three forecast scenarios at 10 months projected a suicide attempt rate increase from 12.49 (7.42–21.01) to 21.38 (12.71–35.99).

The interval exposed to the protracted mass social trauma of the COVID-19 pandemic was associated with a lower suicide attempt rate compared to the unexposed interval. However, this trend is likely to reverse 10 months after lifting social mitigation policies, underscoring the need for enhanced implementation of public health policy for suicide prevention.

Aberrant emotion regulation has been posited as a putative endophenotype of bipolar disorder (BD). We therefore aimed to compare the neural responses during voluntary down-regulation of negative emotions in a large functional magnetic resonance imaging study of BD, patients' unaffected first-degree relatives (URs), and healthy controls (HCs).

We compared neural activity and fronto-limbic functional connectivity during emotion regulation in response to aversive v. neutral pictures in patients recently diagnosed with BD (n = 78) in full/partial remission, their URs (n = 35), and HCs (n = 56).

Patients showed hypo-activity in the left dorsomedial, dorsolateral, and ventrolateral prefrontal cortex (DMPFC and DLPFC) during emotion regulation while viewing aversive pictures compared to HCs, with URs displaying intermediate neural activity in these regions. There were no significant differences between patients with BD and HCs in functional connectivity from the amygdala during emotion regulation. However, exploratory analysis indicated that URs displayed more negative amygdala–DMPFC coupling compared with HCs and more negative amygdala-cingulate DLPFC coupling compared to patients with BD. At a behavioral level, patients and their URs were less able to dampen negative emotions in response aversive pictures.

The findings point to deficient recruitment of prefrontal resources and more negative fronto-amygdala coupling as neural markers of impaired emotion regulation in recently diagnosed remitted patients with BD and their URs, respectively.

The pandemic caused by coronavirus disease 2019 (COVID-19) has forced governments to implement strict social mitigation strategies to reduce the morbidity and mortality from acute infections. These strategies, however, carry a significant risk for mental health, which can lead to increased short-term and long-term mortality and is currently not included in modeling the impact of the pandemic.

We used years of life lost (YLL) as the main outcome measure, applied to Switzerland as an example. We focused on suicide, depression, alcohol use disorder, childhood trauma due to domestic violence, changes in marital status, and social isolation, as these are known to increase YLL in the context of imposed restriction in social contact and freedom of movement. We stipulated a minimum duration of mitigation of 3 months based on current public health plans.

The study projects that the average person would suffer 0.205 YLL due to psychosocial consequence of COVID-19 mitigation measures. However, this loss would be entirely borne by 2.1% of the population, who will suffer an average of 9.79 YLL.

The results presented here are likely to underestimate the true impact of the mitigation strategies on YLL. However, they highlight the need for public health models to expand their scope in order to provide better estimates of the risks and benefits of mitigation.

The default mode network (DMN) dysfunction has emerged as a consistent biological correlate of multiple psychiatric disorders. Specifically, there is evidence of alterations in DMN cohesiveness in schizophrenia, mood and anxiety disorders. The aim of this study was to synthesize at a fine spatial resolution the intra-network functional connectivity of the DMN in adults diagnosed with schizophrenia, mood and anxiety disorders, capitalizing on powerful meta-analytic tools provided by activation likelihood estimation.

Results from 70 whole-brain resting-state functional magnetic resonance imaging articles published during the last 15 years were included comprising observations from 2,789 patients and 3,002 healthy controls.

Specific regional changes in DMN cohesiveness located in the anteromedial and posteromedial cortex emerged as shared and trans-diagnostic brain phenotypes. Disease-specific dysconnectivity was also identified. Unmedicated patients showed more DMN functional alterations, highlighting the importance of interventions targeting the functional integration of the DMN.

This study highlights functional alteration in the major hubs of the DMN, suggesting common abnormalities in self-referential mental activity across psychiatric disorders.

A growing body of evidence suggests that the glial cell line-derived neurotrophic factor (GDNF) is involved in the aetiopathology of mood disorders. GDNF is a neurotrophic factor from the transforming growth factor-β-family, playing a role in cell development and function in the limbic system. This is the first study to examine GDNF concentration in different brain regions of patients with depressive disorder (DD).

We used sandwich-ELISA-technique to ascertain GDNF concentration and Lowry assay for overall protein levels in post-mortem brain tissue of 7 patients with recurrent depressive disorder and 14 individuals without any neurological or psychiatric diagnoses. We included cortical regions as well as limbic area's (hippocampus, entorhinal cortex) basal ganglia (putamen, caudate nucleus), thalamus and cingulated gyrus.

We found a significant increase in GDNF concentration in the parietal cortex of patients with DD compared to the control group. In other regions the trend of an increased GDNF concentration did not reach statistical difference.

This proof of concept study supports previous findings of an alteration of the GDNF in patients with depressive disorder. However, for the first time a significant increase of GDNF in a cortical brain area was found in DD.

Transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS) have shown promise in the treatment of schizophrenia.

To quantify the efficacy of double-blind randomized controlled trials (RCT) of tDCS and rTMS for the positive and negative symptoms of schizophrenia and identify significant moderators relating to patient-related features and stimulation parameters.

Systemic review and meta-analyses of the relevant literature published until February 1st, 2017 to assess treatment efficacy and quantify the contribution of potential moderator variables.

We identified 7 RCTs on tDCS (involving 105 participants) and 30 RCTs on rTMS (involving 768 participants). Compared to sham, tDCS improved all symptom dimensions but the effect reached significance for negative symptoms (Hedge’s g = −0.63, p = 0.02). Efficacy for positive but not negative symptoms was linearly associated with cumulative tDCS stimulation. Compared to sham, rTMS improved hallucinations (Hedge’s g = −0.51, p < 0.001) and negative symptoms (Hedge’s g = −0.49, p = 0.01) but was associated with modest, non-significant worsening of positive symptoms (Hedge’s g = 0.28, p = 0.13). Higher pulse frequency (>10 Hz), motor threshold intensity of 110%, left prefrontal cortical treatment site and trial duration over 3 weeks were associated with improvement in negative symptoms and worsening in positive symptoms (all p < 0.03).

The symptom dimensions in schizophrenia may respond differently to brain stimulation interventions in a way that may reflect the interaction between disease- and treatment-related mechanisms. Our findings underscore the need for further research into patient selection prior to treatment assignment and greater refinement of stimulation protocols.