This study investigates structural abnormalities in hippocampal subfield volumes and shapes, and their association with plasma CC chemokines in individuals with major depressive disorder (MDD).

A total of 61 patients with MDD and 65 healthy controls (HC) were recruited. All participants underwent high-resolution T1-weighted imaging and provided blood samples for the detection of CC chemokines (CCL2, CCL7, and CCL11). Comparisons of hippocampal subregion volumes, surface shapes, and plasma CC chemokine concentrations were conducted between the MDD and HC groups. Furthermore, partial correlation analysis was performed to assess the relationship between structural abnormalities (hippocampal subfield volume and shape) and plasma CC chemokine levels.

The MDD group exhibited a significant reduction in the volume of the left hippocampal tail compared to the HC group (F = 9.750, Bonferroni-corrected p = 0.026). No significant outward or inward deformation of the hippocampus was detected in MDD patients relative to the HC group (all FWE-corrected p > 0.05). Additionally, plasma CCL11 levels were elevated in the MDD group compared to the HC group (F = 9.982, p = 0.002), with these levels showing a positive correlation with the duration of the illness (r = 0.279, p = 0.029). Partial correlation analysis further revealed a negative correlation between the smaller left hippocampal tail volume and plasma CCL11 levels in MDD patients (r = −0.416, p = 0.001).

Abnormally elevated plasma CCL11 in MDD patients may mediate damage to specific hippocampal substructures.

Identifying key areas of brain dysfunction in mental illness is critical for developing precision diagnosis and treatment. This study aimed to develop region-specific brain aging trajectory prediction models using multimodal magnetic resonance imaging (MRI) to identify similarities and differences in abnormal aging between bipolar disorder (BD) and major depressive disorder (MDD) and pinpoint key brain regions of structural and functional change specific to each disorder.

Neuroimaging data from 340 healthy controls, 110 BD participants, and 68 MDD participants were included from the Taiwan Aging and Mental Illness cohort. We constructed 228 models using T1-weighted MRI, resting-state functional MRI, and diffusion tensor imaging data. Gaussian process regression was used to train models for estimating brain aging trajectories using structural and functional maps across various brain regions.

Our models demonstrated robust performance, revealing accelerated aging in 66 gray matter regions in BD and 67 in MDD, with 13 regions common to both disorders. The BD group showed accelerated aging in 17 regions on functional maps, whereas no such regions were found in MDD. Fractional anisotropy analysis identified 43 aging white matter tracts in BD and 39 in MDD, with 16 tracts common to both disorders. Importantly, there were also unique brain regions with accelerated aging specific to each disorder.

These findings highlight the potential of brain aging trajectories as biomarkers for BD and MDD, offering insights into distinct and overlapping neuroanatomical changes. Incorporating region-specific changes in brain structure and function over time could enhance the understanding and treatment of mental illness.

Autoimmune thyroid disease (AITD) and major depressive disorder (MDD) are common genetic diseases. The comorbidity of AITD and MDD has been widely demonstrated by large amounts of epidemiological studies. However, the genetic architectures of the comorbidity remain unknown.

We use large-scale GWAS summary data and novel genetic statistical methods to assess the genetic correlation and potential causality between AITD and MDD disorders. We perform cross-trait GWAS meta-analyses to identify genetic risk variants not previously associated with the individual traits. And we use summary-data-based mendelian randomisation to identify putative functional genes shared between diseases.

Both global and local genetic correlation study confirmed the genetic correlation of AITD and MDD. Through multi-trait analysis of GWAS (MTAG), we identified 112 SNPs associated with the conjoint phenotype, but not with individual traits. Mendelian randomisation confirmed the causal relationship between MDD (exposure) and AITD (outcome). The summary-based mendelian randomisation study found two plausible functional genes for AITD and MDD comorbidity.

AITD and MDD are genetically correlated in global and local chromosomal regions. MR analyses support a putative casual effect of MDD on AITD risk, though residual pleiotropy or confounding cannot be fully excluded. These findings highlight the need for triangulation with experimental and longitudinal studies to confirm causality.

Deep brain stimulation (DBS) is being investigated as a treatment for patients with refractory major depressive disorder (MDD). However, little is known about how DBS exerts its antidepressive effects. Here, we investigated whether ventral anterior limb of the internal capsule stimulation modulates a limbic network centered around the amygdala in patients with treatment-resistant MDD.

Nine patients underwent resting state functional magnetic resonance scans before DBS surgery and after 1 year of treatment. In addition, they were scanned twice within 2 weeks during the subsequent double-blind cross-over phase with active and sham treatment. Twelve matched controls underwent scans at the same time intervals to account for test–retest effects. The imaging data were investigated with functional connectivity (FC) analysis and dynamic causal modelling.

Results showed that 1 year of DBS treatment was associated with increased FC of the left amygdala with precentral cortex and left insula, along with decreased bilateral connectivity between nucleus accumbens and ventromedial prefrontal cortex. No changes in FC were observed during the cross-over phase. Effective connectivity analyses using dynamic causal models revealed widespread amygdala-centric changes between presurgery and 1 year follow-up, while the cross-over phase was associated with insula-centric changes between active and sham stimulation.

These results suggest that ventral anterior limb of the internal capsule DBS results in complex rebalancing of the limbic network involved in emotion, reward, and interoceptive processing.

Major depressive disorder (MDD) and psychostimulant use disorder (PUD) are common, disabling psychopathologies that pose a major public health burden. They share a common behavioral phenotype: deficits in inhibitory control (IC). However, whether this is underpinned by shared neurobiology remains unclear. In this meta-analytic study, we aimed to define and compare brain functional alterations during IC tasks in MDD and PUD.

We conducted a systematic literature search on IC task-based functional magnetic resonance imaging studies in MDD and PUD (cocaine or methamphetamine use disorder) in PubMed, Web of Science, and Scopus. We performed a quantitative meta-analysis using seed-based d mapping to define common and distinct neurofunctional abnormalities.

We identified 14 studies comparing IC-related brain activation in a total of 340 MDD patients with 303 healthy controls (HCs), and 11 studies comparing 258 PUD patients with 273 HCs. MDD showed disorder-differentiating hypoactivation during IC tasks in the median cingulate/paracingulate gyri relative to PUD and HC, whereas PUD showed disorder-differentiating hypoactivation relative to MDD and HC in the bilateral inferior parietal lobule. In conjunction analysis, hypoactivation in the right inferior/middle frontal gyrus was common to both MDD and PUD.

The transdiagnostic neurofunctional alterations in prefrontal cognitive control regions may underlie IC deficits shared by MDD and PUD, whereas disorder-differentiating activation abnormalities in midcingulate and parietal regions may account for their distinct features associated with disturbed goal-directed behavior.

Anhedonia, a transdiagnostic feature common to both Major Depressive Disorder (MDD) and Schizophrenia (SCZ), is characterized by abnormalities in hedonic experience. Previous studies have used machine learning (ML) algorithms without focusing on disorder-specific characteristics to independently classify SCZ and MDD. This study aimed to classify MDD and SCZ using ML models that integrate components of hedonic processing.

We recruited 99 patients with MDD, 100 patients with SCZ, and 113 healthy controls (HC) from four sites. The patient groups were allocated to distinct training and testing datasets. All participants completed a modified Monetary Incentive Delay (MID) task, which yielded features categorized into five hedonic components, two reward consequences, and three reward magnitudes. We employed a stacking ensemble model with SHapley Additive exPlanations (SHAP) values to identify key features distinguishing MDD, SCZ, and HC across binary and multi-class classifications.

The stacking model demonstrated high classification accuracy, with Area Under the Curve (AUC) values of 96.08% (MDD versus HC) and 91.77% (SCZ versus HC) in the main dataset. However, the MDD versus SCZ classification had an AUC of 57.75%. The motivation reward component, loss reward consequence, and high reward magnitude were the most influential features within respective categories for distinguishing both MDD and SCZ from HC (p < 0.001). A refined model using only the top eight features maintained robust performance, achieving AUCs of 96.06% (MDD versus HC) and 95.18% (SCZ versus HC).

The stacking model effectively classified SCZ and MDD from HC, contributing to understanding transdiagnostic mechanisms of anhedonia.

Major depressive disorder (MDD) is a disabling psychiatric condition in which physical activity provides clinical benefits. While exercise effectively alleviates depressive symptoms, its biological mechanisms remain unclear.

This systematic review investigated the neurobiological effects of physical exercise on biomarkers in adults with MDD through randomized controlled trials, including studies assessing exercise interventions and reporting data on their biological effects.

A total of 30 studies, including 2194 participants, were included, examining the effects of physical exercise on various biological systems in patients with MDD. Exercise interventions had mixed effects on inflammatory markers, including interleukins, C-reactive protein, and tumor necrosis factor-α, suggesting a potential but inconsistent anti-inflammatory role. Neurotrophic factors, such as brain-derived neurotrophic factor showed promise as biomarkers of treatment response, but their role in clinical improvements remained inconclusive. Findings for the stress-response system, including cortisol and monoaminergic systems, primarily involving serotonin and dopamine, were limited and variable. Exercise demonstrated potential benefits in reducing oxidative stress and enhancing β-endorphin levels, although these effects were not consistently observed.

This systematic review adopted a broader perspective than prior studies, exploring less-studied biological systems and identifying several limitations in the included studies, including small sample sizes, varying methodologies, and a predominant focus on biochemical markers. Future research should prioritize larger, standardized trials and particularly employ omics approaches to better understand the biological mechanisms underlying the effects of exercise in MDD. The findings highlight the complexity of exercise’s biological effects and emphasize the need for further research to clarify its mechanisms.

3,4-methylenedioxymethamphetamine (MDMA)-assisted therapy (MDMA-AT) has shown promising safety and efficacy in phase 3 studies of post-traumatic stress disorder, but has not been investigated for a primary diagnosis of major depressive disorder (MDD).

We aimed to explore the proof of principle and safety as a first study with MDMA-AT for MDD, and to provide preliminary efficacy data.

Twelve participants (7 women, 5 men) with moderate to severe MDD received MDMA in 2 open-label sessions 1 month apart, along with psychotherapy before, during and after the MDMA sessions, between January 2023 and May 2024. The primary outcome measure was mean change in Montgomery–Asberg Depression Rating Scale (MADRS), and the secondary outcome measure was mean change in functional impairment as measured with the Sheehan Disability Scale (SDS), both from baseline to 8 weeks following the second MDMA session. We used descriptive statistics and the two-tailed Wilcoxon signed-rank test to compare baseline and outcome scores. Repeated measures were analysed by a mixed-effects model.

Baseline MADRS was 29.6 (s.d. 4.9). Feasibility was demonstrated with sufficient recruitment and retention. MADRS scores were significantly reduced post treatment compared with baseline (mean difference –19.3, s.e. 2.4, CI –14.8 to –23.8, P < 0.001). SDS scores significantly decreased from baseline (mean difference –11.7, s.e. 2.2, CI –7.5 to –15.9, P = 0.001). There were no adverse events of special interest, and no unexpected or serious adverse events.

The study met the primary objectives of safety and feasibility, and provided indications of efficacy for MDMA-AT for MDD. Further studies with a randomised design are required to confirm these findings.

EudraCT no. 2021-000805-26.

Compelling evidence claims that gut microbial dysbiosis may be causally associated with major depressive disorder (MDD), with a particular focus on Alistipes. However, little is known about the potential microbiota–gut–brain axis mechanisms by which Alistipes exerts its pathogenic effects in MDD.

We collected data from 16S rDNA amplicon sequencing, untargeted metabolomics, and multimodal brain magnetic resonance imaging from 111 MDD patients and 102 healthy controls. We used multistage linked analyses, including group comparisons, correlation analyses, and mediation analyses, to explore the relationships between the gut microbiome (Alistipes), fecal metabolome, brain imaging, and behaviors in MDD.

Gut microbiome analysis demonstrated that MDD patients had a higher abundance of Alistipes relative to controls. Partial least squares regression revealed that the increased Alistipes was significantly associated with fecal metabolome in MDD, involving a range of metabolites mainly enriched for amino acid, vitamin B, and bile acid metabolism pathways. Correlation analyses showed that the Alistipes-related metabolites were associated with a wide array of brain imaging measures involving gray matter morphology, spontaneous brain function, and white matter integrity, among which the brain functional measures were, in turn, associated with affective symptoms (anxiety and anhedonia) and cognition (sustained attention) in MDD. Of more importance, further mediation analyses identified multiple significant mediation pathways where the brain functional measures in the visual cortex mediated the associations of metabolites with behavioral deficits.

Our findings provide a proof of concept that Alistipes and its related metabolites play a critical role in the pathophysiology of MDD through the microbiota–gut–brain axis.

Major depressive disorder (MDD) is a leading cause of disability worldwide. Investigating early-stage alterations in cerebral intrinsic activity among drug-naive patients may enhance our understanding of MDD’s neurobiological mechanisms and contribute to early diagnosis and intervention.

To examine alterations in the amplitude of low-frequency fluctuation (ALFF) in first-episode, drug-naive MDD individuals and explore associations between ALFF changes and clinical parameters, including depression severity and illness duration.

A total of 30 first-episode, drug-naive MDD individuals (mean illness duration 14 weeks) and 52 healthy controls were included in this study. Resting-state functional magnetic resonance imaging was used to obtain whole-brain ALFF measurements. Voxel-based ALFF maps were compared between MDD and healthy control groups using a two-sample t-test. Simple regression analysis was performed to assess associations between ALFF and clinical measures, including Hamilton Rating Scale for Depression (HAMD) scores and illness duration.

MDD individuals exhibited significantly increased ALFF in the dorsal anterior cingulate cortex and vermal subregion V3 of the cerebellum. Additionally, ALFF in the right dorsolateral prefrontal cortex was negatively correlated with HAMD scores (r = –0.591, P < 0.001). However, no significant association was found between ALFF and illness duration.

This study demonstrates early-stage ALFF alterations in drug-naive MDD patients, particularly in brain regions implicated in cognitive and emotional regulation. These findings suggest potential neuroimaging biomarkers for the early diagnosis and intervention of MDD.

Major depressive disorder (MDD) is a complex and heterogeneous disorder, and this heterogeneity poses a significant challenge for advancing precision medicine in patients with MDD. MRI-based subtyping analysis has been widely employed to address the heterogeneity of MDD patients. In this study, we investigated the subtypes of first-episode and drug-naive (FEDN) MDD patients based on the individualized structural covariance network (IDSCN).

In this study, we used T1-weighted anatomical images of 164 FEDN MDD patients and 164 healthy controls from the REST-meta-MDD consortium. The IDSCN of participants was obtained using the network template perturbation method. Subtypes of FEDN MDD were identified using k-means clustering analysis, and differences in neuroimaging findings and clinical symptoms between the identified subtypes were compared using two-sample t-tests.

This study identified two subtypes of FEDN MDD: subtype 1 (n = 117) and subtype 2 (n = 47) by characterizing 10 edges that were significantly altered in at least 5% of patients (i.e., 8 patients) in the IDSCN. Compared with subtype 2, subtype 1 had significantly higher anxiety symptom scores, stronger structural covariance edges in 9 edges within the thalamus, and a significantly reduced gray matter volume (GMV) in the frontal and parietal regions, and in the thalamus.

Our results suggest that patients with FEDN MDD can be classified into two different subtypes based on their IDSCN, providing an important reference for personalized treatment and precision medicine for patients with FEDN MDD.

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), but initial outcomes can be modest.

To compare SSRI dose optimisation with four alternative second-line strategies in MDD patients unresponsive to an SSRI.

Of 257 participants, 51 were randomised to SSRI dose optimisation (SSRI-Opt), 46 to lithium augmentation (SSRI+Li), 48 to nortriptyline combination (SSRI+NTP), 55 to switch to venlafaxine (VEN) and 57 to problem-solving therapy (SSRI+PST). Primary outcomes were week-6 response/remission rates, assessed by blinded evaluators using the 17-item Hamilton Depression Rating Scale (HDRS-17). Changes in HDRS-17 scores, global improvement and safety outcomes were also explored. EudraCT No. 2007-002130-11.

Alternative second-line strategies led to higher response (28.2% v. 14.3%, odds ratio = 2.36 [95% CI 1.0–5.6], p = 0.05) and remission (16.9% v. 12.2%, odds ratio = 1.46, [95% CI 0.57–3.71], p = 0.27) rates, with greater HDRS-17 score reductions (−2.6 [95% CI −4.9 to −0.4], p = 0.021]) than SSRI-Opt. Significant/marginally significant effects were only observed in both response rates and HDRS-17 decreases for VEN (odds ratio = 2.53 [95% CI 0.94–6.80], p = 0.067; HDRS-17 difference: −2.7 [95% CI −5.5 to 0.0], p = 0.054) and for SSRI+PST (odds ratio = 2.46 [95% CI 0.92 to 6.62], p = 0.074; HDRS-17 difference: −3.1 [95% CI −5.8 to −0.3], p = 0.032). The SSRI+PST group reported the fewest adverse effects, while SSRI+NTP experienced the most (28.1% v. 75%; p < 0.01), largely mild.

Patients with MDD and insufficient response to SSRIs would benefit from any other second-line strategy aside from dose optimisation. With limited statistical power, switching to venlafaxine and adding psychotherapy yielded the most consistent results in the DEPRE'5 study.

Difficult-to-treat depression (DTD) is a common clinical challenge for major depressive disorder and bipolar disorders. Electro convulsive therapy (ECT) has proven to be one of the most effective treatments for this condition. Although several studies have investigated individually the clinical factors associated with the DTD response, the role of their interplay in the clinical response to ECT remains unknown. In the present study, we aimed to characterize the network of symptoms in DTD, evaluate the effects of ECT on the interrelationship of depressive symptoms, and identify the network characteristics that could predict the clinical response.

A network analysis of clinical and demographic data from 154 patients with DTD was performed to compare longitudinally the patterns of relationships among depressive symptoms after ECT treatment. Furthermore, we estimated the network structure at baseline associated with a greater clinical improvement (≥80% reduction at Montgomery–Åsberg Depression Rating Scale total score).

ECT modulated the network of depressive symptoms, with increased strength of the global network (p = 0.03, Cohen’s d = −0.98, 95% confidence interval = [−1.07, −0.88]). The strength of the edges between somatic symptoms (appetite and sleep) and cognitive-emotional symptoms (tension, lassitude, and pessimistic thoughts) was also increased. A stronger negative relationship between insomnia and pessimistic thoughts was associated with a greater improvement after ECT. Concentration difficulties and apparent sadness showed the greatest centrality.

In conclusion, ECT treatment may affect not only the severity of the symptoms but also their relationship; this may contribute to the response in DTD.