Major Depressive Disorder Brain Research Articles

Integrating network pharmacology and bioinformatics to explore the mechanism of Xiaojian Zhongtang in treating major depressive disorder: An observational study.

Major depressive disorder (MDD) is a common mental illness. The traditional Chinese medicine compound Xiaojian Zhongtang (XJZT) has a good therapeutic effect on MDD, but the specific mechanism is not clear. The aim of this study is to explore the molecular mechanism of XJZT in the treatment of MDD through network pharmacology and bioinformatics. The traditional Chinese medicine system pharmacology database was used to screen the chemical components and targets of XJZT, while the online Mendelian inheritance in man, DisGeNET, Genecards, and therapeutic target database databases were used to collect MDD targets and identify the intersection targets of XJZT and MDD. A "drugs-components-targets" network was constructed using the Cytoscape platform, and the STRING was used for protein-protein interaction analysis of intersecting targets. Gene Ontology and Kyoto encyclopedia of genes and genomes analysis of intersecting targets was performed using the DAVID database. Obtain serum and brain transcriptome datasets of MDD from the gene expression omnibus database, and perform differentially expressed genes, weighted gene co-expression network analysis, gene set enrichment analysis, and receiver operating characteristic analysis. A total of 127 chemical components and 767 targets were obtained from XJZT, among which quercetin, kaempferol, and maltose are the core chemical components, and 1728 MDD targets were screened out, with 77 intersecting targets between XJZT and MDD. These targets mainly involve AGE-RAGE signaling pathway in diabetic complexes, epidermal growth factor receptor tyrosine kinase inhibitor resistance, and HIF-1 signaling pathway, and these core targets have strong binding activity with core components. In addition, 1166 differentially expressed genes were identified in the MDD serum transcriptome dataset, and weighted gene co-expression network analysis identified the most relevant gene modules (1269 genes), among which RAC-alpha serine/threonine-protein kinase (AKT1), D(4) dopamine receptor (DRD4), and kynurenine 3-monooxygenase (KMO) were target genes for the treatment of MDD with XJZT, these 3 genes are mainly related to the ubiquitin-mediated proteolysis, arachidonic acid (AA) metabolism, and Huntington disease pathways, and the expression of AKT1, DRD4, and KMO was also found in the MDD brain transcriptome dataset, which is significantly correlated with the occurrence of MDD. We have identified 3 key targets for XJZT treatment of MDD, including AKT1, KMO, and DRD4, and they can be regulated by the key components of XJZT, including quercetin, maltose, and kaempferol. This provides valuable insights for the early clinical diagnosis and development of therapeutic drugs for MDD.

Diagnosis support of major depressive disorder using event-related potentials during affective priming tasks

Major Depressive Disorder (MDD) is a global problem. Currently, the most common diagnosis is based on criteria susceptible to the subjectivity of the patient and the clinician. A possible solution to this problem is to look for diagnostic biomarkers that can accurately and early detect this mental condition. Some researchers have focused on electroencephalogram (EEG) analysis to identify biomarkers. In this study we used a dataset composed of EEG recordings from 24 subjects with MDD and 29 healthy controls (HC), during the execution of affective priming tasks with three different emotional stimuli (images): fear, sadness, and happiness. We investigated abnormalities in depressed patients using a novel technique, by directly comparing Event-Related Potential (ERP) waveforms to find statistically significant differences between the MMD and HC groups. Compared to the control group (healthy subjects), we found out that for the emotions fear and happiness there is a decrease in cortical activity at temporal regions in MDD patients. Just the opposite, for the emotion sadness, an increase in MDD brain activity occurs in frontal and occipital regions. Our findings suggest that emotions regulate the attentional control of cognitive processing and are promising for clinical application in diagnosing patients with MDD more objectively.

Electroconvulsive Therapy Regulates Brain Connectome Dynamics in Patients With Major Depressive Disorder

BackgroundElectroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but its underlying neural mechanisms remain largely unknown. The aim of this study was to identify changes in brain connectome dynamics after ECT in MDD and to explore their associations with treatment outcome. MethodsWe collected longitudinal resting-state functional magnetic resonance imaging data from 80 patients with MDD (50 with suicidal ideation [MDD-SI] and 30 without [MDD-NSI]) before and after ECT and 37 age- and sex-matched healthy control participants. A multilayer network model was used to assess modular switching over time in functional connectomes. Support vector regression was used to assess whether pre-ECT network dynamics could predict treatment response in terms of symptom severity. ResultsAt baseline, patients with MDD had lower global modularity and higher modular variability in functional connectomes than control participants. Network modularity increased and network variability decreased after ECT in patients with MDD, predominantly in the default mode and somatomotor networks. Moreover, ECT was associated with decreased modular variability in the left dorsal anterior cingulate cortex of MDD-SI but not MDD-NSI patients, and pre-ECT modular variability significantly predicted symptom improvement in the MDD-SI group but not in the MDD-NSI group. ConclusionsWe highlight ECT-induced changes in MDD brain network dynamics and their predictive value for treatment outcome, particularly in patients with SI. This study advances our understanding of the neural mechanisms of ECT from a dynamic brain network perspective and suggests potential prognostic biomarkers for predicting ECT efficacy in patients with MDD.

Abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fMRI: a machine learning approach.

To explore the interhemispheric information synergy ability of the brain in major depressive disorder (MDD) patients by applying the voxel-mirrored homotopic connectivity (VMHC) method and further explore the potential clinical diagnostic value of VMHC metric by a machine learning approach. 52 healthy controls and 48 first-episode MDD patients were recruited in the study. We performed neuropsychological tests and resting-state fMRI scanning on all subjects. The VMHC values of the symmetrical interhemispheric voxels in the whole brain were calculated. The VMHC alterations were compared between two groups, and the relationship between VMHC values and clinical variables was analyzed. Then, abnormal brain regions were selected as features to conduct the classification model by using the support vector machine (SVM) approach. Compared to the healthy controls, MDD patients exhibited decreased VMHC values in the bilateral middle frontal gyrus, fusiform gyrus, medial superior frontal gyrus and precentral gyrus. Furthermore, the VMHC value of the bilateral fusiform gyrus was positively correlated with the total Hamilton Depression Scale (HAMD). Moreover, SVM analysis displayed that a combination of all clusters demonstrated the highest area under the curve (AUC) of 0.87 with accuracy, sensitivity, and specificity values of 86.17%, 76.74%, and 94.12%, respectively. MDD patients had reduced functional connectivity in the bilateral middle frontal gyrus, fusiform gyrus, medial superior frontal gyrus and precentral gyrus, which may be related to depressive symptoms. The abnormality in these brain regions could represent potential imaging markers to distinguish MDD patients from healthy controls.

Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study.

Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption. Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD. We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS. Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.

Recent advances in understanding depressive disorder: Possible relevance to brain stimulation therapies.

Recent research has provided novel insights into the major depressive disorder (MDD) and identified certain biomarkers of this disease. There are four main mechanisms playing a key role in the related pathophysiology, namely (1) monoamine systems dysfunction, (2) stress response, (3) neuroinflammation, and (4) neurotrophic factors alteration. Robust evidence on the decreased homovanillic acid in the cerebrospinal fluid (CSF) of patients with MDD supports a rationale for therapeutic stimulation of the medial forebrain bundle activating the dopamine reward system. Both activation and suppression of the hypothalamic-pituitary-adrenal (HPA) axis in MDD and related conditions indicate usefulness of its evaluation for the disease subtyping. Elevated proinflammatory cytokines (specifically, interleukin-6) in CSF imply the role of neuroinflammation resulting in activation of the tryptophan-kynurenine pathway. Finally, neuroplasticity and trophic effects of the brain-derived neurotrophic factor (BDNF) may be related to both structural abnormalities of the brain in MDD and the underlying mechanisms of various therapies. In addition, the gut-brain interaction is pivotal, since lack of beneficial microbes confer the risk of MDD through negative effects on the dopamine system, HPA axis, and vagal nerve. All these factors may be highly relevant to treatment of MDD with contemporary brain stimulation therapies.

Facial Expression Recognition and ReHo Analysis in Major Depressive Disorder.

Objective: To explore the characteristics of expression recognition and spontaneous activity of the resting state brain in major depressive disorder (MDD) patients to find the neural basis of expression recognition and emotional processing.Methods: In this study, two of the six facial expressions (happiness, sadness, anger, fear, aversion, and surprise) were presented in quick succession using a short expression recognition test. The differences in facial expression recognition between MDD patients and healthy people were compared. Further, the differences in ReHo values between the two groups were compared using a resting-state functional magnetic resonance imaging (fMRI) scan to investigate the characteristics of spontaneous brain activity in the resting state and its relationship with clinical symptoms and the accuracy of facial expression recognition in patients with MDD.Results: (1) The accuracy of facial expression recognition in patients with MDD was lower than that of the HC group. There were differences in facial expression recognition between the two groups in sadness-anger (p = 0.026), surprise-aversion (p = 0.038), surprise-happiness (p = 0.014), surprise-sadness (p = 0.019), fear-happiness (p = 0.027), and fear-anger (p = 0.009). The reaction time for facial expression recognition in the patient group was significantly longer than that of the HC group. (2) Compared with the HC group, the ReHo values decreased in the left parahippocampal gyrus, left thalamus, right putamen, left putamen, and right angular gyrus, and increased in the left superior frontal gyrus, left middle temporal gyrus, left medial superior frontal gyrus, and right medial superior frontal gyrus in the patient group. (3) Spearman correlation analysis showed no statistical correlation between ReHo and HAMD-17 scores in MDD patients (p > 0.05). The ReHo value of the left putamen was negatively correlated with the recognition of fear-surprise (r = −0.429, p = 0.016), the ReHo value of the right angular gyrus was positively correlated with the recognition of sadness-anger (r = 0.367, p = 0.042), and the ReHo value of the right medial superior frontal gyrus was negatively correlated with the recognition of fear-anger (r = −0.377, p = 0.037).Conclusion: In view of the different performance of patients with MDD in facial expression tasks, facial expression recognition may have some suggestive effect on the diagnosis of depression and has clinical guiding significance. Many brain regions, including the frontal lobe, temporal lobe, striatum, hippocampus, and thalamus, in patients with MDD show extensive ReHo abnormalities in the resting state. These brain regions with abnormal spontaneous neural activity are important components of LCSPT and LTC circuits, and their dysfunctional functions cause disorder of emotion regulation. The changes in spontaneous activity in the left putamen, right angular gyrus, and right medial superior frontal gyrus may represent the abnormal pattern of spontaneous brain activity in the neural circuits related to emotion perception and may be the neural basis of facial expression recognition.

Analysis of Differentially Expressed Genes in the Dentate Gyrus and Anterior Cingulate Cortex in a Mouse Model of Depression.

Major depressive disorder (MDD) is a prevalent, chronic, and relapse-prone psychiatric disease. However, the intermediate molecules resulting from stress and neurological impairment in different brain regions are still unclear. To clarify the pathological changes in the dentate gyrus (DG) and anterior cingulate cortex (ACC) regions of the MDD brain, which are the most closely related to the disease, we investigated the published microarray profile dataset GSE84183 to identify unpredictable chronic mild stress- (UCMS-) induced differentially expressed genes (DEGs) in the DG and ACC regions. Based on the DEG data, functional annotation, protein-protein interaction, and transcription factor (TF) analyses were performed. In this study, 1071 DEGs (679 upregulated and 392 downregulated) and 410 DEGs (222 upregulated and 188 downregulated) were identified in DG and ACC, respectively. The pathways and GO terms enriched by the DEGs in the DG, such as cell adhesion, proteolysis, ion transport, transmembrane transport, chemical synaptic transmission, immune system processes, response to lipopolysaccharide, and nervous system development, may reveal the molecular mechanism of MDD. However, the DEGs in the ACC involved metabolic processes, proteolysis, visual learning, DNA methylation, innate immune responses, cell migration, and circadian rhythm. Sixteen hub genes in the DG (Fn1, Col1a1, Anxa1, Penk, Ptgs2, Cdh1, Timp1, Vim, Rpl30, Rps21, Dntt, Ptk2b, Jun, Avp, Slit1, and Sema5a) were identified. Eight hub genes in the ACC (Prkcg, Grin1, Syngap1, Rrp9, Grwd1, Pik3r1, Hnrnpc, and Prpf40a) were identified. In addition, eleven TFs (Chd2, Zmiz1, Myb, Etv4, Rela, Tcf4, Tcf12, Chd1, Mef2a, Ubtf, and Mxi1) were predicted to regulate more than two of these hub genes. The expression levels of ten randomly selected hub genes that were specifically differentially expressed in the MDD-like animal model were verified in the corresponding regions in the human brain. These hub genes and TFs may be regarded as potential targets for future MDD treatment strategies, thus aiding in the development of new therapeutic approaches to MDD.

Accelerated brain aging predicts impulsivity and symptom severity in depression.

Multiple structural and functional neuroimaging measures vary over the course of the lifespan and can be used to predict chronological age. Accelerated brain aging, as quantified by deviations in the MRI-based predicted age with respect to chronological age, is associated with risk for neurodegenerative conditions, bipolar disorder, and mortality. Whether age-related changes in resting-state functional connectivity are accelerated in major depressive disorder (MDD) is unknown, and, if so, it is unclear if these changes contribute to specific cognitive weaknesses that often occur in MDD. Here, we delineated age-related functional connectivity changes in a large sample of normal control subjects and tested whether brain aging is accelerated in MDD. Furthermore, we tested whether accelerated brain aging predicts individual differences in cognitive function. We trained a support vector regression model predicting age using resting-state functional connectivity in 710 healthy adults aged 18-89. We applied this model trained on normal aging subjects to a sample of actively depressed MDD participants (n = 109). The difference between predicted brain age and chronological age was 2.11 years greater (p = 0.015) in MDD patients compared to control participants. An older MDD brain age was significantly associated with increased impulsivity and, in males, increased depressive severity. Unexpectedly, accelerated brain aging was also associated with increased placebo response in a sham-controlled trial of high-frequency repetitive transcranial magnetic stimulation targeting the dorsomedial prefrontal cortex. Our results indicate that MDD is associated with accelerated brain aging, and that accelerated aging is selectively associated with greater impulsivity and depression severity.

Molecular pathology associated with altered synaptic transcriptome in the dorsolateral prefrontal cortex of depressed subjects

Disrupted synaptic plasticity is the hallmark of major depressive disorder (MDD), with accompanying changes at the molecular and cellular levels. Often, the maladaptive molecular changes at the synapse are the result of global transcriptional reprogramming dictated by activity-dependent synaptic modulation. Thus far, no study has directly studied the transcriptome-wide expression changes locally at the synapse in MDD brain. Here, we have examined altered synaptic transcriptomics and their functional relevance in MDD with a focus on the dorsolateral prefrontal cortex (dlPFC). RNA was isolated from total fraction and purified synaptosomes of dlPFC from well-matched 15 non-psychiatric controls and 15 MDD subjects. Transcriptomic changes in synaptic and total fractions were detected by next-generation RNA-sequencing (NGS) and analyzed independently. The ratio of synaptic/total fraction was estimated to evaluate a shift in gene expression ratio in MDD subjects. Bioinformatics and network analyses were used to determine the biological relevance of transcriptomic changes in both total and synaptic fractions based on gene–gene network, gene ontology (GO), and pathway prediction algorithms. A total of 14,005 genes were detected in total fraction. A total of 104 genes were differentially regulated (73 upregulated and 31 downregulated) in MDD group based on 1.3-fold change threshold and p < 0.05 criteria. In synaptosomes, out of 13,236 detectable genes, 234 were upregulated and 60 were downregulated (>1.3-fold, p < 0.05). Several of these altered genes were validated independently by a quantitative polymerase chain reaction (qPCR). GO revealed an association with immune system processes and cell death. Moreover, a cluster of genes belonged to the nervous system development, and psychological disorders were discovered using gene–gene network analysis. The ratio of synaptic/total fraction showed a shift in expression of 119 genes in MDD subjects, which were primarily associated with neuroinflammation, interleukin signaling, and cell death. Our results suggest not only large-scale gene expression changes in synaptosomes, but also a shift in the expression of genes from total to synaptic fractions of dlPFC of MDD subjects with their potential role in immunomodulation and cell death. Our findings provide new insights into the understanding of transcriptomic regulation at the synapse and their possible role in MDD pathogenesis.

Task MRI-Based Functional Brain Network of Major Depression.

This chapter will focus on task magnetic resonance imaging (MRI) to understand the biological mechanisms and pathophysiology of brain in major depressive disorder (MDD), which would have minor alterations in the brain function. Therefore, the functional study, such as task MRI functional connectivity, would play a crucial role to explore the brain function in MDD. Different kinds of tasks would determine the alterations in functional connectivity in task MRI studies of MDD. The emotion-related tasks are linked with alterations in anterior cingulate cortex, insula, and default mode network. The emotional memory task is linked with amygdala-hippocampus alterations. The reward-related task would be related to the reward circuit alterations, such as fronto-straital. The cognitive-related tasks would be associated with frontal-related functional connectivity alterations, such as the dorsolateral prefrontal cortex, anterior cingulate cortex, and other frontal regions. The visuo-sensory characteristics of tasks might be associated with the parieto-occipital alterations. The frontolimbic regions might be major components of task MRI-based functional connectivity in MDD. However, different scenarios and tasks would influence the representations of results.

Whole-brain structural covariance network abnormality in first-episode and drug-naïve major depressive disorder

There has been a growing interest in the abnormality of networks across the brain in major depressive disorder (MDD). We aimed to investigate the structural covariance networks in patients with first-episode and drug-naïve MDD using structural imaging. A total of 77 patients with first-episode and drug-naïve MDD and 79 healthy subjects (HS) were recruited, from whom high-resolution T1-weighted images were analysed. Incident component analysis was used to calculate the brain networks based on grey matter volume covariance. There were significant differences in salience network, medial temporal lobe network, default mode network and central executive network between MDD and HS (p < 0.05). Further, the disturbance of medial temporal lobe network was significantly correlated with the severity of depressive symptoms (p < 0.05). In conclusion, we found a novel abnormality in the brain network in the medial temporal lobe primarily involving the hippocampus and parahippocampal gyrus in patients with first-episode and treatment-naïve MDD.

Graph theory analysis of directed functional brain networks in major depressive disorder based on EEG signal

Objective. Analysis of functional and structural brain networks has suggested that major depressive disorder (MDD) is associated with a disruption in brain networks. This paper aims to investigate the abnormalities of brain networks in MDD. Approach. To this aim, we constructed weighted directed functional networks based on electroencephalography (EEG) signals of 26 MDD patients and 23 normal (N) subjects. The nodes of networks were 19 EEG electrodes, and the edges were phase transfer entropy (PTE) between each pair of electrodes. PTE is a model-free, phase-based effective connectivity measure that is relatively robust to noise and linear mixing. Since the correct instantaneous phase of a signal is computed for narrow frequency bands, the networks were analyzed in eight frequency sub-bands including delta, theta, alpha1, alpha2, beta1, beta2, beta3, and beta4. To assess the alteration in the topology of brain networks in MDD patients, graph theory metrics consisting of global efficiency, local efficiency, node betweenness centrality, node degree, and node strength were analyzed by statistical tests and classification. Furthermore, directed differential connectivity graphs (dDCGs) for the MDD and N groups were studied. Main results. These analyses revealed a higher node degree and strength in the dDCGs of the MDD group than the normal group. It was also found that MDD brain networks have a more randomized structure than the N group. Moreover, our results indicated that the out-degree of networks classified MDD and N subjects with an accuracy of 92%; thus, our method can be considered as a powerful tool for depression detection. Significance. Our analysis may provide new insights into developing biomarkers for depression detection based on brain networks.

Elevated expression of unfolded protein response genes in the prefrontal cortex of depressed subjects: Effect of suicide

Background: Major Depressive Disorder (MDD) is a leading cause of mental disability worldwide. Despite many studies, the pathophysiology associated with MDD brain is not very clear. It is reported that cellular stress is related to depressive symptoms. Under stressful conditions, intracellular homeostasis processes can be disrupted, which can induce a process of unfolded protein response (UPR) in the subcellular lumen of endoplasmic reticulum (ER). The purpose of this study is to elucidate whether UPR is active in the depressed brain. Methods: The dorsolateral prefrontal cortex (dlPFC) was used from 23 non-psychiatric controls and 43 MDD subjects. The expression levels of UPR associated genes (GRP78, GRP94, XBP-1, CHOP, ATF4C, and ATF6C) were measured by qRT-PCR. Results: The level of mRNA expression in MDD subjects was significantly higher for GRP78 (p = 0.008), GRP94 (p = 0.018), and ATF4C (p = 0.03) compared to non-psychiatric controls. Further analysis suggested that changes in the expression of these genes were specifically higher only in those MDD subjects who died by suicide but not in those who died by causes other than suicide when compared with non-psychiatric controls (GRP78, p = 0.007; GRP94, p = 0.041; ATF4C, p = 0.037). Limitations: This study was performed only in MDD subjects who had died by suicide. Suicide subjects with other psychiatric illnesses need to be included. Conclusions: Given that UPR is involved in many physiological processes in the brain, including inflammatory response as well as apoptosis, increased expression of UPR genes indicates that ER stress and mediated UPR may be critical factors in suicidality among depressed patients.

Multimodal imaging reveals a complex pattern of dysfunction in corticolimbic pathways in major depressive disorder.

Major depressive disorder (MDD) is highly prevalent and associated with considerable morbidity, yet its pathophysiology remains only partially understood. While numerous studies have investigated the neurobiological correlates of MDD, most have used only a single neuroimaging modality. In particular, diffusion tensor imaging (DTI) studies have failed to yield uniform results. In this context, examining key tracts and using information from multiple neuroimaging modalities may better characterize potential abnormalities in the MDD brain. This study analyzed data from 30 participants with MDD and 26 healthy participants who underwent DTI, magnetic resonance spectroscopy (MRS), resting-state functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG). Tracts connecting the subgenual anterior cingulate cortex (sgACC) and the left and right amygdala, as well as connections to the left and right hippocampus and thalamus, were examined as target areas. Reduced fractional anisotropy (FA) was observed in the studied tracts. Significant differences in the correlation between medial prefrontal glutamate concentrations and FA were also observed between MDD and healthy participants along tracts connecting the sgACC and right amygdala; healthy participants exhibited a strong correlation but MDD participants showed no such relationship. In the same tract, a correlation was observed between FA and subsequent antidepressant response to ketamine infusion in MDD participants. Exploratory models also suggested group differences in the relationship between DTI, fMRI, and MEG measures. This study is the first to combine MRS, DTI, fMRI, and MEG data to obtain multimodal indices of MDD and antidepressant response and may lay the foundation for similar future analyses.

Intrinsic gray-matter connectivity of the brain in major depressive disorder

BackgroundMajor depressive disorder (MDD) has been assumed to be associated with aberrant brain connectivity. However, research suggests that brain connectivity abnormalities should not be restricted to extrinsic white matter connectivity, but may also impact on intrinsic gray matter connectivity. Therefore, our study aimed to investigate the intrinsic gray-matter connectivity in MDD. MethodsThe participants were 16 first-episode, drug-naïve patients with MDD and 16 healthy controls matched on age and gender. All participants were scanned by 3.0T structural magnetic resonance imaging. Global and local intrinsic gray-matter connectivity were measured based on surface-based geodesic distances, including mean coritical separation distances (MSDs), perimeter function, and radius function. ResultsMDD patients had significantly lower MSDs in the left postcentral gyrus and higher MSDs in the left superior parietal cortex. Marginally significant correlation was observed between MSDs in the left postcentral gyrus and symptoms of depression. Compared with healthy controls, depressed subjects had abnormal local intrinsic gray-matter connectivity in the left postcentral gyrus, the left transverse temporal gyrus, the right lingual gyrus, the right lateral occipital cortex, and the right superior frontal gyrus. Furthermore, local intrinsic gray matter connections of these brain areas were associated with some symptoms of depression. LimitationsThe small sample size limited the interpretability of our potential conclusions. ConclusionAberrant intrinsic gray-matter connectivity was observed in depressed subjects, indicating abnormal intrinsic wiring cost of brain architecture. This might help explain the aberrant topological properties of brain functional connectivity and provide insights into the vulnerability of MDD.

Splice-Break: exploiting an RNA-seq splice junction algorithm to discover mitochondrial DNA deletion breakpoints and analyses of psychiatric disorders.

Deletions in the 16.6 kb mitochondrial genome have been implicated in numerous disorders that often display muscular and/or neurological symptoms due to the high-energy demands of these tissues. We describe a catalogue of 4489 putative mitochondrial DNA (mtDNA) deletions, including their frequency and relative read rate, using a combinatorial approach of mitochondria-targeted PCR, next-generation sequencing, bioinformatics, post-hoc filtering, annotation, and validation steps. Our bioinformatics pipeline uses MapSplice, an RNA-seq splice junction detection algorithm, to detect and quantify mtDNA deletion breakpoints rather than mRNA splices. Analyses of 93 samples from postmortem brain and blood found (i) the 4977 bp ‘common deletion’ was neither the most frequent deletion nor the most abundant; (ii) brain contained significantly more deletions than blood; (iii) many high frequency deletions were previously reported in MitoBreak, suggesting they are present at low levels in metabolically active tissues and are not exclusive to individuals with diagnosed mitochondrial pathologies; (iv) many individual deletions (and cumulative metrics) had significant and positive correlations with age and (v) the highest deletion burdens were observed in major depressive disorder brain, at levels greater than Kearns–Sayre Syndrome muscle. Collectively, these data suggest the Splice-Break pipeline can detect and quantify mtDNA deletions at a high level of resolution.

Epigenetic age analysis of brain in major depressive disorder

A large body of evidence indicates major depressive disorder (MDD) associated with biological aging acceleration. In this study, we aim to assess epigenetic aging in different prefrontal sub-regions and cell types from postmortem tissues in MDD by using two public methylation datasets (subjects are 40 and 58 respectively). Results showed that brain epigenetic age has a strong correlation with chronological age in both datasets. However, there are no significant differences in epigenetic aging acceleration between MDD and controls in all prefrontal sub-regions and cell types. In conclusion, this study provides evidence against epigenetic aging acceleration in the brain in MDD.

In vivo relationship between serotonin 1A receptor binding and gray matter volume in the healthy brain and in major depressive disorder.

Serotonin 1A (5-HT1A) receptors mediate serotonin trophic role in brain neurogenesis. Gray matter volume (GMV) loss and 5-HT1A receptor binding alterations have been identified in major depressive disorder (MDD). Here we investigated the relationship between 5-HT1A receptor binding and GMV in 40 healthy controls (HCs) and, for the first time, 47 antidepressant-free MDD patients using Voxel-Based Morphometry and [11C]WAY100635 Positron Emission Tomography. Values of GMV and 5-HT1A binding (expressed as BPF, one of the types of binding potentials that refer to displaceable or specific binding that can be quantified in vivo with PET) were obtained in 13 regions of interest, including raphe, and at the voxel level. We used regression analysis within each group to predict GMV from BPF, while covarying for age, sex, total gray matter volume and medication status. In the HCs group, we found overall a positive correlation between terminal field 5-HT1A receptor binding and GMV, which reached statistical significance in regions such as hippocampus, insula, orbital prefrontal cortex, and parietal lobe. We observed a trend towards inverse correlation between raphe 5-HT1A autoreceptor binding and anterior cingulate GMV in both groups, and a statistically significant positive correlation between raphe 5-HT1A binding and temporal GMV in MDD. Analysis of covariance at the voxel-level revealed a trend towards interaction between diagnosis and raphe 5-HT1A binding in predicting GMV in cerebellum and supramarginal gyrus (higher correlation in HCs compared with MDD). Our results replicated previous findings in the normative brain, but did not extend them to the brain in MDD, and indicated a trend towards dissociation between MDD and HCs in the relationship of raphe 5-HT1A binding with postsynaptic GMV. These results suggest that 5-HT1A receptors contribute to altered neuroplasticity in MDD, possibly via effects predating depression onset.

Understanding the Neuroepigenetic Constituents of Suicide Brain.

Stressful life incidents often cause a predisposition for developing mental disorders such as major depressive disorder (MDD). Impaired neurocognitive and neuro-vegetative functions of the central nervous system are the hallmarks of this mental illness. Blunted responses from emotionally salient regions of the brain including cortex, hippocampus, and amygdala have been associated with MDD-related behavioral changes. Moreover, improper signal processing and neuronal atrophy were held responsible for the overall dysfunctionality of these vulnerable regions in the MDD brain. The prevalence of genetic susceptibility along with adverse environmental stimuli often makes the situation worse for MDD patients, leading to an increased risk of suicidal behavior and eventually death by suicide. Despite considerable efforts to understand the complex neurobiology associated with MDD and suicidal behavior, their pathological determinants remain mostly elusive. Recent research, however, has shown that epigenetic perturbations have a formidable impact on the etiopathogenesis of MDD. Understanding the neuroepigenetic nature of this mental disorder may provide opportunities to devise more effective treatment strategies. Moreover, this can potentially lead to identifying predictive biomarkers associated with suicide risk. The present chapter critically reviews studies pertaining to epigenetic signatures of MDD and suicide brain.