Mitochondrial dysfunction has been implicated in the pathogenesis of major depressive disorder (MDD); however, the causal contributions of specific mitochondrial genes across regulatory layers remain unclear.

We integrated genome-wide association study summary statistics from the Psychiatric Genomics Consortium and FinnGen with quantitative-trait-locus (QTL) datasets for DNA methylation, gene expression (eQTL), and protein abundance. Mitochondrial genes were annotated using the MitoCarta3.0 database. Summary-based Mendelian randomization and Bayesian colocalization were applied to assess causal relationships, with colocalization determined by the posterior probability of a shared causal variant (PPH4), and the false discovery rate used for multiple-testing correction. Brain-specific effects were evaluated using Genotype-Tissue Expression eQTL data. Prioritized genes were ranked based on cross-omics consistency and replication evidence.

Five mitochondrial genes were prioritized. TDRKH showed consistent associations across methylation, transcription, and protein levels, with hypermethylation at cg24503712 linked to reduced expression and a lower risk of MDD (Tier 1). METAP1D (Tier 2) demonstrated protective effects at both the transcript and protein levels. LONP1, FIS1, and SCP2 (Tier 3) exhibited consistent but complex regulatory patterns. Several signals were replicated in brain tissues, including TDRKH in the caudate and METAP1D in the cortex.

This study provides multi-omics evidence for the causal involvement of mitochondrial genes in MDD. TDRKH and METAP1D emerged as key candidates, offering promising targets for future mechanistic research and therapeutic development.

Major depressive disorder (MDD) is a heterogeneous with underlying mechanisms that are insufficiently studied. We aimed to identify functional connectivity (FC)-based subtypes of MDD and investigate their biological mechanisms.

Consensus clustering of FC patterns was applied to a population of 829 MDD patients from the REST-Meta-MDD database, with validity assessed across multiple dimensions, including atlas replication, cross-validated classification, and drug-naïve subgroup analysis. Regression models were used to quantify FC alterations in each MDD subgroup compared with 770 healthy controls, and to analyze spatial associations between FC alterations and publicly available gene transcriptomic and neurotransmitter receptor/transporter density databases.

Two stable MDD subtypes emerged: hypoconnectivity (n = 527) and hyperconnectivity (n = 299), which had both shared and distinct regions with remarkable FC alterations (i.e. epicenters) in the default mode network.

There were several common enriched genes (e.g. axon/brain development, synaptic transmission/organization, etc.) related to FC alterations in both subtypes. However, glial cell and neuronal differentiation genes were specifically enriched in the hypoconnectivity and hyperconnectivity subtypes, respectively.

Both subtypes showed spatial associations between FC alterations and serotonin receptor/transporter density. In the hypoconnectivity subtype, FC alterations correlated with GABA and acetylcholine receptor densities, while norepinephrine transporter and glutamate receptor densities were linked to the hyperconnectivity subtype.

Our findings suggested the presence of two neuroimaging subtypes of MDD characterized by hypoconnectivity or hyperconnectivity, demonstrating robust reproducibility. The two subtypes had both shared and distinct genetic mechanisms and neurotransmitter receptor/transporter profiles, suggesting potential clinical implications for this heterogeneous disorder.

Major depressive disorder (MDD) patients exhibit a mood-congruent emotional processing bias within the amygdala toward negative facial stimuli at both unconscious and conscious levels. Therefore, our study aimed to investigate the temporal and spatial dynamics of amygdala along with its interactions with the whole brain during implicit and explicit conditions in MDD.

Thirty MDD patients and 26 healthy controls (HCs) underwent magnetoencephalography (MEG) recordings and performed implicit and explicit emotional face recognition tasks with happy, sad, and neutral facial expressions. Using the amygdala as a seed region, time frequency representations (TFR) and functional connectivity (FC) were calculated. Pearson correlation analyses measured the relationship between TFR and FC values with clinical symptoms.

During implicit processing, MDD patients exhibited left amygdala activation in the gamma power (60–70 Hz) before 250 ms in response to sad facial stimuli compared to HCs. In the implicit mode, there were increased FC between the right amygdala and several brain regions in the occipitoparietal lobes, as well as higher FC between the left amygdala and putamen in MDD patients. Additionally, the right amygdala was positively correlated with the severity of depression and anxiety during implicit processing.

MDD patients had lateralized amygdala activation in response to sad facial expressions during unconscious emotional recognition of facial stimuli. Our study provided valuable insights into the spatiotemporal dynamics of facial emotional recognition associated with depressive and anxiety-related cognitive bias during implicit and explicit processing.

Heightened reactivity in the amygdala measured by functional magnetic resonance imaging during emotional processing is considered a potential biomarker for clinical depression. Still, it is unknown whether this is also true for depressive symptoms in the general population, and – when in remission after recurrent depressive episodes – it is associated with future episodes.

Using the UK Biobank population study (n = 11,334), we investigated the association of amygdala reactivity during negative facial stimuli, focusing on lifetime depression (trait), depressive symptoms (state), and the modulating effect of antidepressant (AD) treatment thereof. We employed normative modeling (NM) to better incorporate population heterogeneity of the amygdala activity.

In line with a previous study, depressive symptoms (state) over the last 2 weeks were not associated with the amygdala reactivity signal. Rather, our results indicate a significant positive association (p = 0.03, ω2 = 0.001) between amygdala response and the recurrence of depressive episodes (trait). Longitudinal analysis revealed that the group that had experienced a single depressive episode before showed a significantly increased amygdala response after additional episodes (p = 0.03, ω2 = 0.017). ADs were not associated with amygdala response directly, but decreased associations within episode recurrence severity.

The amygdala response to negative stimuli was associated with an individual’s risk of recurrence of depressive episodes, and AD treatment reduced these associations. This study highlights the relevance of amygdala reactivity as a trait, but not a state biomarker for (recurrent) depression. Moreover, it demonstrates the benefit of applying NM in the context of population data.

Reward can influence cognitive control; however, dysfunctional interactions between reward and cognitive control in adolescents with major depressive disorder (MDD) remain unclear.

We recruited 35 adolescents with MDD and 29 healthy controls (HC) who completed the AX version of the Continuous Performance Test (AX-CPT) under reward and non-reward conditions, while undergoing functional Near-Infrared Spectroscopy (fNIRS).

Adolescents with MDD exhibited slower response times and higher error rates compared to healthy controls. Under reward conditions, they responded more quickly but made more errors. Hierarchical Drift Diffusion Modeling (HDDM) revealed that adolescents with MDD showed a reduced starting bias toward more rewarding responses and a broader decision threshold in reward contexts. Neuroimaging results indicated that the MDD group showed diminished activation differences in the left dorsolateral prefrontal cortex (DLPFC), left ventrolateral prefrontal cortex (VLPFC), and right VLPFC in response to cues requiring high versus low cognitive control. Additionally, they exhibited weaker functional connectivity between these regions during reward-related cognitive control. Correlation analyses further showed that greater anhedonia severity was associated with poorer behavioral performance and less flexible activation in the prefrontal cortex.

Cognitive control impairments in depressed adolescents may be related to dysfunction in the motivational system. Our findings provide behavioral, computational, and neural evidence for the Expected Value of Control (EVC) theory. Diminished reward sensitivity and inflexible cognitive control may jointly contribute to these deficits, highlighting the importance of considering motivational factors in the diagnosis and intervention of cognitive control impairments in adolescents with depression.

Psychomotor disturbance (PmD) is prevalent in major depressive disorder (MDD), with neural substrates implicated in disrupted motor circuits and the interaction to non-motor cortex. Our objective is to explore the functional connectivity pattern underlying PmD using functional magnetic resonance imaging (fMRI).

A total of 150 patients with MDD and 91 healthy controls (HCs) were included in this study. The patients were categorized into psychomotor (pMDD, n = 107) and non-psychomotor (npMDD, n = 43) groups based on the Hamilton Depression Rating Scale. Seed-based connectivity (SBC) analysis was conducted using predefined somatomotor and cerebellar network (SMN and CN) coordinates as seeds, to assess group differences and symptom correlations. Subsequently, we correlated the group-contrast SBC map with existing neurotransmitter maps to explore the neurochemical basis.

In pMDD patients compared to HC, we observed decreased connectivity, especially between the SMN and frontal cortex, within the bilateral SMN, and between the CN and right precentral cortex. Meanwhile, connectivity increased between the SMN and the middle cingulate cortex and between the CN and left precentral cortex in pMDD relative to npMDD and HC. Connectivity between the SMN and angular gyrus was positively correlated with the severity of PmD. Additionally, the aberrant SBC patterns in pMDD were linked to the distribution of dopamine D1 and D2 receptors.

This study provides insights into the aberrant connectivity within the motor circuits and its interactions with non-motor regions in PmD. It also suggests a potential role for dopaminergic dysregulation in the connectivity abnormalities associated with PmD.

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.