Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome

Machine learning-driven risk prediction and feature identification for major depressive disorder and its progression: an exploratory study based on five years of longitudinal data from the US national health survey

BACKGROUND

Major depressive disorder (MDD) presents significant public health challenges due to its increasing prevalence and complex risk factors. This study systematically analyzed data from 2019 to 2023 to explore trends in MDD incidence, symptom progression, and multidimensional influencing factors.

OBJECTIVE

The study aimed to identify key risk factors and mechanisms of MDD progression through multidimensional variable analysis and machine learning model evaluation, optimizing predictive models to provide a scientific basis for early screening, targeted interventions, and public health policy formulation.

METHODS

Data from the U.S. National Health Interview Survey (NHIS) were analyzed using LASSO regression for feature selection and various machine learning models (e.g., AdaBoost, Gradient Boosting, Gaussian NB, Extra Trees) for risk prediction. SHAP value analysis was employed to interpret the models and identify key predictors across psychological, social, and biological domains.

RESULTS

Depression risk was closely associated with psychological health (e.g., PHQ82, ANXLEVEL), social support (e.g., DISCRIM1, PCNTADLT), and biological health (e.g., COGMEMDFF, DEPEV). AdaBoost, Gradient Boosting, Gaussian NB, and Extra Trees demonstrated superior performance in different tasks, with SHAP analysis highlighting the critical role of psychological health in risk prediction and the importance of social support and cognitive deficits in symptom progression.

CONCLUSIONS

This study provides a scientific foundation for early screening of MDD, personalized intervention strategies, and public health policy optimization. Future research should expand data coverage, optimize models, and incorporate causal inference methods to enhance the precision and applicability of depression risk prediction.

Distinct homotopic functional connectivity patterns of the amygdalar sub-regions as biomarkers in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) affects multiple functional neural networks. Neuroimaging studies using resting-state functional connectivity (FC) have focused on the amygdala but did not assess changes in connectivity between the left and right amygdala. The current study aimed to examine the inter-hemispheric functional connectivity (homotopic FC, HoFC) between different amygdalar sub-regions in patients with MDD compared to healthy controls, and to examine whether amygdalar sub-regions' HoFC also predicts response to Serotonin Selective Reuptake Inhibitors (SSRIs).

METHOD

Sixty-seven patients with MDD and 64 matched healthy controls were recruited. An MRI scan focusing on resting state fMRI and clinical and cognitive evaluations were performed. An atlas seed-based approach was used to identify the lateral and medial sub-regions of the amygdala. HoFC of these sub-regions was compared between groups and correlated with severity of depression, and emotional processing performance. Baseline HoFC levels were used to predict response to SSRIs after 2 months of treatment.

RESULTS

Patients with MDD demonstrated decreased inter-hemispheric FC in the medial (F3,120 = 4.11, p = 0.008, η2 = 0.096) but not in the lateral (F3,119 = 0.29, p = 0.82, η2 = 0.008) amygdala compared with healthy controls. The inter-hemispheric FC of the medial sub-region correlated with symptoms severity (r = -0.33, p < 0.001) and emotional processing performance (r = 0.38, p < 0.001). Moreover, it predicted treatment response to SSRIs 65.4 % of the cases.

LIMITATIONS

The current study did not address FC changes in MDD biotypes. In addition, structural connectivity was not examined.

CONCLUSIONS

Using a unique perspective of the amygdalar distinct areas elucidated differential inter-hemispheric FC patterns in MDD patients, emphasizing the role of interhemispheric communication in depression.

Transcranial Doppler ultrasound in evaluating cerebral blood flow abnormalities in major depressive disorder

Previous research has shown that blood flow abnormalities affect major depressive disorder (MDD) from multiple perspectives. Therefore, this study aims to investigate the relationship between middle cerebral artery (MCA) blood flow velocity parameters and clinical symptom scores (Hamilton Depression Rating Scale [HAMD] and Montgomery-Åsberg Depression Rating Scale [MADRS]) in patients with MDD. We compared the MCA blood flow velocity parameters, including peak systolic velocity (MCA-PSV), end-diastolic velocity (MCA-EDV), and mean velocity (MCA-MV), between 50 MDD patients and 50 control subjects. Additionally, we analyzed the correlation between these parameters and HAMD and MADRS scores. Hemodynamic parameters such as pulsatility index and resistance index were also compared between the 2 groups. MCA-PSV, MCA-EDV, and MCA-MV were significantly lower in MDD patients compared to the control group, while pulsatility index and resistance index were significantly higher. Correlation analysis revealed that MCA-PSV, MCA-EDV, and MCA-MV were significantly negatively correlated with HAMD and MADRS scores in MDD patients, indicating that cerebral blood flow velocity decreases as depressive symptoms worsen. Furthermore, regression analysis confirmed the negative relationship between blood flow velocity parameters and clinical symptom scores. The results of this study suggest that the reduction in cerebral blood flow velocity in MDD patients may be associated with the severity of depressive symptoms. This finding provides new insights into the pathophysiological mechanisms of MDD and offers a potential theoretical basis for developing depression treatment strategies based on cerebral blood flow velocity parameters.

Regional brain structural network topology mediates the associations between white matter damage and disease severity in first-episode, Treatment-naïve pubertal children with major depressive disorder

Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.

Intranasal Delivery of circATF7IP siRNA via Lipid Nanoparticles Alleviates LPS-induced Depressive-Like Behaviors

Major depressive disorder (MDD) is a prevalent mental disorder that significantly impacts social and psychological function, but no effective medication is currently available. Circular RNAs (circRNAs) have been reported to participate in the pathogenesis of MDD which are envisioned as promising therapeutic targets. However, nonviral-based delivery strategies targeting circRNA against MDD are not thoroughly investigated. Here, it is identified that circATF7IP is significantly upregulated in plasma samples and positively correlated with 24-Hamilton Depression Scale (HAMD-24) scores of MDD patients. Synergistic amine lipid nanoparticles (SALNPs) are designed to deliver siRNA targeting circATF7IP (si-circATF7IP) into the hippocampus brain region by intranasal administration. Intranasal delivery of SALNP-si-circATF7IP successfully alleviated the depressive-like behaviors in the LPS-induced mouse depression model via decreasing CD11b+CD45dim microglia population and pro-inflammatory cytokine productions (TNF-α and IL-6). These results indicate that the level of circATF7IP positively correlates with MDD pathogenesis, and SALNP delivery of si-circATF7IP via intranasal administration is an effective strategy to ameliorate LPS-induced depressive-like behaviors.

Unraveling psilocybin's therapeutic potential: behavioral and neuroplasticity insights in Wistar-Kyoto and Wistar male rat models of treatment-resistant depression

RATIONALE

Our study aimed to unravel the unknown mechanisms behind the exceptional efficacy of Psilocybin (PSI) in treating treatment-resistant depression (TRD). Focusing on Wistar-Kyoto (WKY) rats with a TRD phenotype and Wistar (WIS) rats as a normative comparison, we investigated behavioral and neuroplasticity-related responses to PSI, striving to shed light on the distinctive features of its antidepressant effects.

OBJECTIVES

We set out to assess the behavioral impact of acute and prolonged PSI administration on WKY and WIS rats, employing Novel Object Recognition (NORT), Social Interaction (SI), and Forced Swimming Test (FST). Our secondary objectives involved exploring strain-specific alterations in neuroplasticity-related parameters, including brain-derived neurotrophic factor (BDNF) and activity-regulated cytoskeleton-associated protein (Arc).

METHODS

Conducting post-acute and extended assessments after a single PSI administration, we applied behavioral tests and biochemical analyses to measure serum BDNF levels and neuroplasticity-related parameters in the prefrontal cortex. Statistical analyses were deployed to discern significant differences between the rat strains and assess the impact of PSI on behavioral and biochemical outcomes.

RESULTS

Our findings uncovered significant behavioral disparities between WKY and WIS rats, indicating passive behavior and social withdrawal in the former. PSI demonstrated pronounced pro-social and antidepressant effects in both strains, each with its distinctive temporal trajectory. Notably, we identified strain-specific variations in BDNF-related signaling and observed the modulation of Arc expression in WKY rats.

CONCLUSIONS

Our study delineated mood-related behavioral nuances between WKY and WIS rat strains, underscoring the antidepressant and pro-social properties of PSI in both groups. The distinct temporal patterns of observed changes and the identified strain-specific neuroplasticity alterations provide valuable insights into the TRD phenotype and the mechanisms underpinning the efficacy of PSI.

Engaging Mood Brain Circuits with Psilocybin (EMBRACE): a study protocol for a randomized, placebo-controlled and delayed-start, neuroimaging trial in depression

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide across domains of health and cognition, affecting overall quality of life. Approximately one third of individuals with depression do not fully respond to treatments (e.g., conventional antidepressants, psychotherapy) and alternative strategies are needed. Recent early phase trials suggest psilocybin may be a safe and efficacious intervention with rapid-acting antidepressant properties. Psilocybin is thought to exert therapeutic benefits by altering brain network connectivity and inducing neuroplastic changes that endure for weeks post-treatment. Although early clinical results are encouraging, psilocybin's acute neurobiological effects on neuroplasticity have not been fully investigated. We aim to examine for the first time how psilocybin acutely (intraday) and subacutely (weeks) alters functional brain networks implicated in depression.

METHODS

Fifty participants diagnosed with MDD or persistent depressive disorder (PDD) will be recruited from a tertiary mood disorders clinic and undergo 1:1 randomization into either an experimental or control arm. Participants will be given either 25 mg psilocybin or 25 mg microcrystalline cellulose (MCC) placebo for the first treatment. Three weeks later, those in the control arm will transition to receiving 25 mg psilocybin. We will investigate whether treatments are associated with changes in arterial spin labelling and blood oxygenation level-dependent contrast neuroimaging assessments at acute and subacute timepoints. Primary outcomes include testing whether psilocybin demonstrates acute changes in (1) cerebral blood flow and (2) functional brain activity in networks associated with mood regulation and depression when compared to placebo, along with changes in MADRS score over time compared to placebo. Secondary outcomes include changes across complementary clinical psychiatric, cognitive, and functional scales from baseline to final follow-up. Serum peripheral neurotrophic and inflammatory biomarkers will be collected at baseline and follow-up to examine relationships with clinical response, and neuroimaging measures.

DISCUSSION

This study will investigate the acute and additive subacute neuroplastic effects of psilocybin on brain networks affected by depression using advanced serial neuroimaging methods. Results will improve our understanding of psilocybin's antidepressant mechanisms versus placebo response and whether biological measures of brain function can provide early predictors of treatment response.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT06072898. Registered on 6 October 2023.

Depressed patients with childhood maltreatment display altered intra- and inter-network resting state functional connectivity

BACKGROUND

Childhood maltreatment (CM) is a major risk factor for the development of major depressive disorder (MDD). To gain more knowledge on how adverse childhood experiences influence the development of brain architecture, we studied functional connectivity (FC) alterations of neural networks of depressed patients with, or without the history of CM.

METHODS

Depressed patients with severe childhood maltreatment (n = 18), MDD patients without maltreatment (n = 19), and matched healthy controls (n = 20) were examined with resting state functional MRI. History of maltreatment was assessed with the 28-item Childhood Trauma Questionnaire. Intra- and inter-network FC alterations were evaluated using FMRIB Software Library and CONN toolbox.

RESULTS

We found numerous intra- and inter-network FC alterations between the maltreated and the non-maltreated patients. Intra-network FC differences were found in the default mode, visual and auditory networks, and cerebellum. Network modelling revealed several inter-network FC alterations connecting the default mode network with the executive control, salience and cerebellar networks. Increased inter-network FC was found in maltreated patients between the sensory-motor and visual, cerebellar, default mode and salience networks.

LIMITATIONS

Relatively small sample size, cross-sectional design, and retrospective self-report questionnaire to assess adverse childhood experiences.

CONCLUSIONS

Our findings confirm that severely maltreated depressed patients display numerous alterations of intra- and inter-network FC strengths, not only in their fronto-limbic circuits, but also in sensory-motor, visual, auditory, and cerebellar networks. These functional alterations may explain that maltreated individuals typically display altered perception and are prone to develop functional neurological symptom disorder (conversion disorder) in adulthood.

Distinct effects of first-episode and recurrent major depressive disorder on brain changes related to suicidal ideation: Evidence from the REST-meta-MDD Project

BACKGROUND

Significant differences in clinical manifestations between first-episode and recurrent major depression disorder (FE-MDD/R-MDD) have been demonstrated in previous studies, including the degree of suicide attempt. However, the potential brain mechanism underlying the effect of depressive episode frequency on suicidal ideation (SI) remains unclear.

METHODS

In this study, 102 patients with FE-MDD (SI/non-SI: N = 70/32) and 71 matched normal controls (NCs), as well as 75 patients with R-MDD (SI/non-SI: N = 37/38) and 49 matched NCs were screened from the Chinese REST-meta-MDD consortium. T1-weighted and resting-state fMRI images were used to calculate gray matter volume (GMV) and fractional amplitude of low-frequency fluctuations (fALFF), respectively.

RESULTS

Group comparisons revealed that FE-MDD showed changes only in GMV, while R-MDD showed changes in both GMV and fALFF compared to NCs. SI-specific GMV decreases were observed in the right cerebellum, superior marginal gyrus, and left middle temporal gyrus in FE-MDD patients, while SI-specific fALFF decreases in bilateral superior frontal gyrus and increases in bilateral cerebellum and left parahippocampal gyrus were obserevd in R-MDD patients. Moreover, a negative correlation was found between GMV value in right cerebellum and HAMD score.

CONCLUSIONS

These findings suggest that first-episode and recurrent MDD show different effects on brain structure and function in patients with SI, providing a potential explanation for the distinct clinical manifestations of MDD patients from a brain mechanisms perspective.

D-arabinose acts as antidepressant by activating the ACSS2-PPARγ/TFEB axis and CRTC1 transcription

CREB-regulated transcription coactivator 1 (CRTC1), a pivotal synaptonuclear messenger, regulates synaptic plasticity and transmission to prevent depression. Despite exhaustive investigations into CRTC1 mRNA reductions in the depressed mice, the regulatory mechanisms governing its transcription remain elusive. Consequently, exploring rapid but non-toxic CRTC1 inducers at the transcriptional level is important for resisting depression. Here, we demonstrate the potential of D-arabinose, a unique monosaccharide prevalent in edible-medicinal plants, to rapidly enter the brain and induce CRTC1 expression, thereby eliciting rapid-acting and persistent antidepressant responses in chronic restrain stress (CRS)-induced depressed mice. Mechanistically, D-arabinose induces the expressions of peroxisome proliferator-activated receptor gamma (PPARγ) and transcription factor EB (TFEB), thereby activating CRTC1 transcription. Notably, we elucidate the pivotal role of the acetyl-CoA synthetase short-chain family member 2 (ACSS2) as an obligatory mediator for PPARγ and TFEB to potentiate CRTC1 transcription. Furthermore, D-arabinose augments ACSS2-dependent CRTC1 transcription by activating AMPK through lysosomal AXIN-LKB1 pathway. Correspondingly, the hippocampal down-regulations of ACSS2, PPARγ or TFEB alone failed to reverse CRTC1 reductions in CRS-exposure mice, ultimately abolishing the anti-depressant efficacy of D-arabinose. In summary, our study unveils a previously unexplored role of D-arabinose in activating the ACSS2-PPARγ/TFEB-CRTC1 axis, presenting it as a promising avenue for the prevention and treatment of depression.

Brain network hierarchy reorganization in subthreshold depression

BACKGROUND

Hierarchy is the organizing principle of human brain network. How network hierarchy changes in subthreshold depression (StD) is unclear. The aim of this study was to investigate the altered brain network hierarchy and its clinical significance in patients with StD.

METHODS

A total of 43 patients with StD and 43 healthy controls matched for age, gender and years of education participated in this study. Alterations in the hierarchy of StD brain networks were depicted by connectome gradient analysis. We assessed changes in network hierarchy by comparing gradient scores in each network in patients with StD and healthy controls. The study compared different brain subdivisions if there was a different network. Finally, we analysed the relationship between the altered gradient scores and clinical characteristics.

RESULTS

Patients with StD had contracted network hierarchy and suppressed cortical range gradients. In the principal gradient, the gradient scores of default mode network were significantly reduced in patients with StD compared to controls. In the default network, the subdivisions of reduced gradient scores were mainly located in the precuneus, superior temporal gyrus, and anterior and posterior cingulate gyrus. Reduced gradient scores in the default mode network, the anterior and posterior cingulate gyrus were correlated with severity of depression.

CONCLUSIONS

The network hierarchy of the StD changed and was significantly correlated with depressive symptoms and severity. These results provided new insights into further understanding of the neural mechanisms of StD.

Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes

BACKGROUND

Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied.

METHODS

Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes.

RESULTS

We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies.

CONCLUSIONS

Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.

An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates

The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain's structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain's logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced-under pertinent simplifications-via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations.

Transcutaneous Auricular Vagus Nerve Stimulation to Improve Emotional State

Emotional experiences are a part of our lives. The maladaptive functioning of an individual's emotional field can lead to emotional disturbances of various kinds, such as anxiety and depression. Currently, there is an increasing prevalence of emotional disorders that cause great human suffering and high socioeconomic costs. Emotional processing has a biological basis. The major neuroscientific theories of emotion are based on biological functioning, and all of them take into account the anatomy and function of the tenth cranial nerve: the vagus nerve. The vagus nerve connects the subdiaphragmatic and supradiaphragmatic areas and modulates emotional processing as the basis of interoceptive functioning. Auricular vagus nerve stimulation is a new and innovative neuromodulation technique based on the function of the vagus nerve. Several interventions have shown that this new neurostimulation technique is a very promising resource for treating emotional disorders. In this paper, we summarise three neuroscientific theories of emotion, explain what transcutaneous auricular nerve stimulation is, and present arguments for its use and continued research.

Immune mechanisms of depression in rheumatoid arthritis

Depression is a common and disabling comorbidity in rheumatoid arthritis that not only decreases the likelihood of remission and treatment adherence but also increases the risk of disability and mortality in patients with rheumatoid arthritis. Compelling data that link immune mechanisms to major depressive disorder indicate possible common mechanisms that drive the pathology of the two conditions. Preclinical evidence suggests that pro-inflammatory cytokines, which are prevalent in rheumatoid arthritis, have various effects on monoaminergic neurotransmission, neurotrophic factors and measures of synaptic plasticity. Neuroimaging studies provide insight into the consequences of inflammation on the brain (for example, on neural connectivity), and clinical trial data highlight the beneficial effects of immune modulation on comorbid depression. Major depressive disorder occurs more frequently in patients with rheumatoid arthritis than in the general population, and major depressive disorder also increases the risk of a future diagnosis of rheumatoid arthritis, further highlighting the link between rheumatoid arthritis and major depressive disorder. This Review focuses on interactions between peripheral and central immunobiological mechanisms in the context of both rheumatoid arthritis and major depressive disorder. Understanding these mechanisms will provide a basis for future therapeutic development, not least in depression.

Trait- and state-like co-activation pattern dynamics in current and remitted major depressive disorder

BACKGROUND

Distinguishing between trait- and state-like neural alternations in major depressive disorder (MDD) may advance our understanding of this recurring disorder. We aimed to investigate dynamic functional connectivity alternations in unmedicated individuals with current or past MDD using co-activation pattern analyses.

METHODS

Resting-state functional magnetic resonance imaging data were acquired from individuals with first-episode current MDD (cMDD, n = 50), remitted MDD (rMDD, n = 44), and healthy controls (HCs, n = 64). Using a data-driven consensus clustering technique, four whole-brain states of spatial co-activation were identified and associated metrics (dominance, entries, transition frequency) were analyzed with respect to clinical characteristics.

RESULTS

Relative to rMDD and HC, cMDD showed increased dominance and entries of state 1 (primarily involving default mode network (DMN)), and decreased dominance of state 4 (mostly involving frontal-parietal network (FPN)). Among cMDD, state 1 entries correlated positively with trait rumination. Conversely, relative to cMDD and HC, individuals with rMDD were characterized by increased state 4 entries. Relative to HC, both MDD groups showed increased state 4-to-1 (FPN to DMN) transition frequency but reduction in state 3 (spanning visual attention, somatosensory, limbic networks), with the former metric specifically related to trait rumination.

LIMITATIONS

Further confirmation with longitudinal studies are required.

CONCLUSIONS

Regardless of symptoms, MDD was characterized by increased FPN-to-DMN transitions and reduced dominance of a hybrid network. State-related effect emerged in regions critically implicated in repetitive introspection and cognitive control. Asymptomatic individuals with past MDD were uniquely linked to increased FPN entries. Our findings identify trait-like brain network dynamics that might increase vulnerability to future MDD.

A resting-state electroencephalographic microstates study in depressed adolescents with non-suicidal self-injury

Neuroimaging studies have revealed abnormal brain activities in depressed teenagers who engage in non-suicidal self-injury (NSSI). We used resting-state electroencephalography (EEG) microstate analysis, which indicates the brief overlap of brain network activation for exploring the characteristics of large-scale cortical activities in depressed adolescents engaged with NSSI to clarify the underlying temporal mechanism. A modified k-means cluster algorithm was used to segment 64-channel resting-state EEG data into microstates. Data from 27 healthy adolescents, 37 adolescents with major depressive disorder (MDD), and 53 adolescents with both MDD and NSSI were examined in this study. The resting-state microstate parameters were compared among groups using the one-way ANOVA and Spearman correlation. Then the associations between significantly different microstate parameters and the depressive severity and self-harming data in the patient groups were further analyzed. The MDD group had higher contribution (p < 0.01), occurrence (p < 0.01) of microstate A, and higher microstate E→A transition (p < 0.05) than the HC and the NSSI group. The MDD group showed a distinctly longer duration (p < 0.05) of microstate A and microstate A→C transition than the HC. The transition probability from B to C was increased in the NSSI group compared to the HC. In the MDD group, the HAMD correlated with the duration of microstate A (Spearman's rho = 0.34, p = 0.044), as the PHQ-9 correlated with its occurrence (Spearman's rho = 0.37, p = 0.028). This research revealed that whereas depressive adolescents with NSSI and MDD displayed similar patterns with healthy controls in EEG microstate, the MDD group did not. Additionally, the non-random transition from microstate E→A may protect against recent self-harm in adolescents with MDD.

The connexin hemichannel inhibitor D4 produces rapid antidepressant-like effects in mice

Depression is a common mood disorder characterized by a range of clinical symptoms, including prolonged low mood and diminished interest. Although many clinical and animal studies have provided significant insights into the pathophysiology of depression, current treatment strategies are not sufficient to manage this disorder. It has been suggested that connexin (Cx)-based hemichannels are candidates for depression intervention by modifying the state of neuroinflammation. In this study, we investigated the antidepressant-like effect of a recently discovered selective Cx hemichannel inhibitor, a small organic molecule called D4. We first showed that D4 reduced hemichannel activity following systemic inflammation after LPS injections. Next, we found that D4 treatment prevented LPS-induced inflammatory response and depressive-like behaviors. These behavioral effects were accompanied by reduced astrocytic activation and hemichannel activity in depressive-like mice induced by repeated low-dose LPS challenges. D4 treatment also reverses depressive-like symptoms in mice subjected to chronic restraint stress (CRS). To test whether D4 broadly affected neural activity, we measured c-Fos expression in depression-related brain regions and found a reduction in c-Fos+ cells in different brain regions. D4 significantly normalized CRS-induced hypoactivation in several brain regions, including the hippocampus, entorhinal cortex, and lateral septum. Together, these results indicate that blocking Cx hemichannels using D4 can normalize neuronal activity and reduce depressive-like symptoms in mice by reducing neuroinflammation. Our work provides evidence of the antidepressant-like effect of D4 and supports glial Cx hemichannels as potential therapeutic targets for depression.

Neural plasticity and depression treatment

Depression is one of the most common mental disorders, which can lead to a variety of emotional problems and even suicide at its worst. As this neuropsychiatric disorder causes the patients to suffer a lot and function poorly in everyday life, it is imposing a heavy burden on the affected families and the whole society. Several hypotheses have been proposed to elucidate the pathogenesis of depression, such as the genetic mutations, the monoamine hypothesis, the hypothalamic-pituitary-adrenal (HPA) axis hyperactivation, the inflammation and the neural plasticity changes. Among these models, neural plasticity can occur at multiple levels from brain regions, cells to synapses structurally and functionally during development and in adulthood. In this review, we summarize the recent progresses (especially in the last five years) on the neural plasticity changes in depression under different organizational levels and elaborate different treatments for depression by changing the neural plasticity. We hope that this review would shed light on the etiological studies for depression and on the development of novel treatments.

Effects of 12-week escitalopram treatment on resting-state functional connectivity of large-scale brain networks in major depressive disorder

In this study, the effects of antidepressants on large-scale brain networks and the neural basis of individual differences in response were explored. A total of 41 patients with major depressive disorder (MDD) and 42 matched healthy controls (HCs) were scanned by resting-state functional magnetic resonance imaging separately at baseline and after a 12-week follow-up. The patients with MDD received escitalopram for 12 weeks. After treatment, patients were classified into those with MDD in remission [MDDr, endpoint 17-item Hamilton Depression Rating Scale (HAMD) total score ≤7] and those in nonremission (MDDnr). The human Brainnetome Atlas was used to define large-scale networks and compute within- and between-network resting-state functional connectivity (rsFC). Results showed the decreased subcortical network (SCN)-ventral attention network (VAN) connectivity at baseline increased in patients with MDD after 12-week treatment, and it was comparable with that of HCs. This change was only observed in patients with MDDr. However, the decreased within-network rsFC in SCN and default mode network (DMN) persisted in all patients with MDD, including those with MDDr and MDDnr, after treatment. The strength of SCN-VAN connectivity at baseline was significantly negatively correlated with the reduction rate of HAMD score in all patients with MDD. Thus, SCN-VAN connectivity may be an antidepressant target associated with depressive state changes and a predictor of treatment response to serotonin reuptake inhibitors. The within-network rsFC in SCN and DMN may reflect a trait-like abnormality in MDD. These findings provide further insights into the mechanism of antidepressants and their individual differences in response. The trial name is "Appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement" (URL: http://www.chictr.org.cn/showproj.aspx?proj=21377; registration number: ChiCTR-OOC-17012566).

Anticipatory cues in emotional processing shift the activation of a combined salience sensorimotor functional network in drug-naïve depressed patients

BACKGROUND

Major depressive disorder is characterized by a large-scale brain network dysfunction, contributing to impairments in cognitive and affective functioning. Core regions of default mode, limbic and salience networks are also impaired in emotional processing and anticipation. This study aimed to explore default mode, salience, and limbic networks modulation during the processing of emotional stimuli with and without anticipatory cues in depression, and further investigate how these networks were functionally coupled with the rest of the brain.

METHODS

Twenty-one drug-naïve depressed patients and 15 matched controls were included in the study. All participants completed a psychological assessment and the affective pictures paradigm during an fMRI acquisition. Group independent component analysis and psychophysiological interactions analyses were performed.

RESULTS

A significant interaction between Cue, Valence and Group was found for the salience/sensorimotor network. When processing uncued emotional stimuli, patients showed increased activation of this network for negative vs. neutral pictures, whereas when anticipatory cues were displayed previously to the picture presentation, they invert this pattern of activation (hyperactivating the salience/sensorimotor network for positive vs. neutral pictures). Patients showed increased functional connectivity between the salience/sensorimotor network and the left amygdala as well as the right inferior parietal lobule compared to controls when processing uncued negative pictures.

LIMITATIONS

The sample size was modest, and the salience/sensorimotor network included regions not typically identified as part of salience network. Thus, this study should be replicated to further interpret the results.

CONCLUSIONS

Anticipatory cues shift the pattern of activation of the salience/sensorimotor network in drug-naïve depressed patients.

Functional connectivity of the default mode network subsystems in patients with major depressive episodes with mixed features

Background

The neuroimaging mechanism of major depressive episodes with mixed features (MMF) is not clear.

Aims

This study aimed to investigate the functional connectivity of the default mode network (DMN) subsystems among patients with MMF and patients with major depressive disorder without mixed features (MDD_noMF).

Methods

This study recruited 47 patients with MDD_noMF and 27 patients with MMF from Beijing Anding Hospital, Capital Medical University, between April 2021 and June 2022. Forty-five healthy controls (HCs) were recruited. All subjects underwent resting-state functional magnetic resonance imaging scanning and clinical assessments. Intranetwork and internetwork functional connectivity were computed in the DMN core subsystem, dorsal medial prefrontal cortex (dMPFC) subsystem and medial temporal lobe (MTL) subsystem. Analysis of covariance method was performed to compare the intranetwork and internetwork functional connectivity in the DMN subsystems among the MDD_noMF, MMF and HC groups.

Results

The functional connectivity within the DMN core (F=6.32, p_FDR=0.008) and MTL subsystems (F=4.45, p_FDR=0.021) showed significant differences among the MDD_noMF, MMF and HC groups. Compared with the HC group, the patients with MDD_noMF and MMF had increased functional connectivity within the DMN MTL subsystem, and the patients with MMF also showed increased functional connectivity within the DMN core subsystem. Meanwhile, compared with the MDD_noMF, the patients with MMF had increased functional connectivity within the DMN core subsystem (mean difference (MDD_noMF-MMF)=-0.08, SE=0.04, p=0.048). However, no significant differences were found within the DMN dMPFC subsystem and all the internetwork functional connectivity.

Conclusions

Our results indicated abnormal functional connectivity patterns of DMN subsystems in patients with MMF, findings potentially beneficial to deepen our understanding of MMF's neural basis.

Cerebral Venous-Associated Brain Damage May Lead to Anxiety and Depression

Background and purpose: Anxiety and depression are common in patients with Cerebral venous outflow disturbance (CVOD). Here, we aimed to explore possible mechanisms underlying this phenomenon. Methods: We enrolled patients diagnosed with imaging-confirmed CVOD, including internal jugular venous stenosis (IJVS) and cerebral venous sinus stenosis (CVSS) between 2017 and 2020. All of them had MRI/PWI scans. The Hamilton Anxiety Scale (HAMA) and 24-item Hamilton Depression Scale (HAMD) were used to evaluate the degree of anxiety and depression at the baseline and three months post-stenting. In addition, the relationships between the HAMA and HAMD scores, white matter lesions, and cerebral perfusion were analyzed using multiple logistic regressions. Results: A total of 61 CVOD patients (mean age 47.95 ± 15.26 years, 59.0% females) were enrolled in this study. Over 70% of them reported symptoms of anxiety and/or depression. Severe CVOD-related anxiety correlated with older age (p = 0.046) and comorbid hyperlipidemia (p = 0.005). Additionally, head noise, sleep disturbances, and white matter lesions (WMLs) were common risk factors for anxiety and depression (p < 0.05). WMLs were considered an independent risk factor for anxiety based on multiple regression analysis (p = 0.029). Self-contrast displayed that CVOD-related anxiety (p = 0.027) and depression (p = 0.017) scores could be corrected by stenting, as the hypoperfusion scores in the limbic lobes of patients with anxiety and depression were significantly higher than those in patients without. Conclusions: CVOD-induced hypoperfusion-mediated changes in the white matter microstructure may represent an underlying mechanism of anxiety and depression in patients with chronic CVOD.

Combined HTR1A/1B methylation and human functional connectome to recognize patients with MDD

OBJECTIVES

This study aimed to use a machine-learning method to identify HTR1A/1B methylation and resting-state functional connectivity (rsFC) related to the diagnosis of MDD, then try to build classification models for MDD diagnosis based on the identified features.

METHODS

Peripheral blood samples were collected from all recruited participants, and part of the participants underwent the resting-state fMRI scan. Features including HTR1A/1B methylation and rsFC were calculated. Then, the initial feature sets of epigenetics and neuroimaging were separately input into an all-relevant feature selection to generate significant discriminative power for MDD diagnosis. Random forest classifiers were constructed and evaluated based on identified features. In addition, the SHapley Additive exPlanations (SHAP) method was adapted to interpret the diagnostic model.

RESULTS

A combination of selected HTR1A/1B methylation and rsFC feature sets achieved better performance than using either one alone - a distinction between MDD and healthy control groups was achieved at 81.78% classification accuracy and 0.8948 AUC.

CONCLUSION

A high classification accuracy can be achieved by combining multidimensional information from epigenetics and cerebral radiomic features in MDD. Our approach can be helpful for accurate clinical diagnosis of MDD and further exploring the pathogenesis of MDD.

Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression

Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression.