Biomarkers For Major Depressive Disorder Research Articles

Functional connectivity of the prefrontal cortex and amygdala is related to depression status in major depressive disorder

BackgroundWe used resting-state functional magnetic resonance imaging to examine possible amygdala-prefrontal cortex functional connectivity abnormalities and to clarify the correlation of the abnormal connectivity with response to antidepressant medications. MethodsWe recruited 40 drug-naïve patients with first-episode depression, had a 17-item Hamilton Rating Scale for Depression (HRSD17) score>17 for participation in a magnetic resonance imaging scan. Remission was defined as an HRSD17 score <7 following 8 weeks of fluoxetine antidepressant treatment. Gender- and age-matched healthy subjects (n = 26) also underwent MRI scanning. Finally, the association between the change in HRSD17 scores and a change in connectivity between the amygdala and prefrontal cortex from pre to post-treatment was evaluated in major depressive disorder (MDD). ResultsAfter controlling for age, gender and years of education, a statistically significant increase in functional connectivity to the right prefrontal cortex from the amygdala was observed in the MDD group compared with the healthy control group (p<0.05, corrected). After 8 weeks of antidepressant treatment and remission in the MDD group, a significant decrease in functional connectivity to the right prefrontal cortex and the left prefrontal cortex from the amygdala was observed, compared with the level of connectivity in the drug-naïve MDD group(p<0.05,corrected). There were no significant associations between the difference in HRSD17 scores rMDD and fMDD with the change in connectivity. LimitationsThe design of this study lack resistance to treatment for the depressed group. ConclusionsIncreased functional connectivity of PFC-AMY is a promise to be a biomarker of MDD, however weather it could be a biomarker of fluoxetine treatment needs future studying.

Machine Learning Analysis of Blood microRNA Data in Major Depression: A Case-Control Study for Biomarker Discovery.

BackgroundThere is a lack of reliable biomarkers for major depressive disorder (MDD) in clinical practice. However, several studies have shown an association between alterations in microRNA levels and MDD, albeit none of them has taken advantage of machine learning (ML).MethodSupervised and unsupervised ML were applied to blood microRNA expression profiles from a MDD case-control dataset (n = 168) to distinguish between (1) case vs control status, (2) MDD severity levels defined based on the Montgomery-Asberg Depression Rating Scale, and (3) antidepressant responders vs nonresponders.ResultsMDD cases were distinguishable from healthy controls with an area-under-the receiver-operating characteristic curve (AUC) of 0.97 on testing data. High- vs low-severity cases were distinguishable with an AUC of 0.63. Unsupervised clustering of patients, before supervised ML analysis of each cluster for MDD severity, improved the performance of the classifiers (AUC of 0.70 for cluster 1 and 0.76 for cluster 2). Antidepressant responders could not be successfully separated from nonresponders, even after patient stratification by unsupervised clustering. However, permutation testing of the top microRNA, identified by the ML model trained to distinguish responders vs nonresponders in each of the 2 clusters, showed an association with antidepressant response. Each of these microRNA markers was only significant when comparing responders vs nonresponders of the corresponding cluster, but not using the heterogeneous unclustered patient set.ConclusionsSupervised and unsupervised ML analysis of microRNA may lead to robust biomarkers for monitoring clinical evolution and for more timely assessment of treatment in MDD patients.

Major depression accompanied with inflammation and multiple cytokines alterations: Evidences from clinical patients to macaca fascicularis and LPS-induced depressive mice model

BackgroundInflammation progress has been consistently implicated in the pathophysiology of major depressive disorder (MDD). However, the underlying mechanism of inflammation and depressive symptoms still far from being fully elucidated. In addition, studies on emotional disorders could also benefit from model of the non-human primates. To explore the difference of serum multi-cytokines levels among the MDD patients and depressed macaca fascicularis as well as LPS-treated mice, thus may find the reliable potential biomarkers for MDD. MethodsSerum multi-cytokines levels among MDD patients (n = 44) and depressed macaca fascicularis (n = 6) together with controls (n = 22 for human, n = 6 for macaques) were detected by the Bio-Plex cytokines panel. Then five of these serum cytokines in LPS-treated mice were measured via ELISA. Furthermore, these cytokines protein expressions were validated by western blotting in three depression-related regions of LPS-treated mice. ResultsHere, we found that MDD patients displayed increased concentration of 13 proinflammatory and anti-inflammatory cytokines accompanied with one decreased cytokine in peripheral serum. Meanwhile, the naturally occurring depression (NOD) macaca fascicularis merely exhibited elevated concentration of 4 peripheral cytokines (IL-6, IL-8, MCP-1, VEGF), which were in accordance with the outcomes of MDD patients. Importantly, the serum and brain tissues of LPS-treated mice also emerged similar cytokines alterations. ConclusionIn summary, our findings strengthen the evidence that cytokines were associated with the depression, and the IL-6 and VEGF may as predictive biomarkers for novel diagnostic as well as therapeutic of depression. The hypothalamus may as a key brain region involve in the inflammatory related depressive-like behaviors.

Circulating microRNA 134 sheds light on the diagnosis of major depressive disorder

Major depressive disorder (MDD) is a prevalent and debilitating psychiatric mood disorder that lacks objective laboratory-based tests to support its diagnosis. A class of microRNAs (miRNAs) has been found to be centrally involved in regulating many molecular processes fundamental to central nervous system function. Among these miRNAs, miRNA-134 (miR-134) has been reported to be related to neurogenesis and synaptic plasticity. In this study, the hypothesis that plasma miR-134 can be used to diagnose MDD was tested. Perturbation of peripheral and central miR-134 in a depressive-like rat model was also examined. By reverse-transcription quantitative PCR, miR-134 was comparatively measured in a small set of plasma samples from MDD and healthy control (HC) subjects. To determine its diagnostic efficacy, plasma miR-134 levels were assessed in 100 MDD, 50 bipolar disorder (BD), 50 schizophrenic (SCZ), and 100 HC subjects. A chronic unpredictable mild stress (CUMS) rat model was also developed to evaluate miR-134 expression in plasma, hippocampus (HIP), prefrontal cortex (PFC), and olfactory bulb. We found that plasma miR-134 was significantly downregulated in MDD subjects. Diagnostically, plasma miR-134 levels could effectively distinguish MDD from HC with 79% sensitivity and 84% specificity, while distinguishing MDD from HC, BD, and SCZ subjects with 79% sensitivity and 76.5% specificity. Congruent with these clinical findings, CUMS significantly reduced miR-134 levels in the rat plasma, HIP, and PFC. Although limited by the relatively small sample size, these results demonstrated that plasma miR-134 displays potential ability as a biomarker for MDD.

Survey of NMDA Receptor-related Biomarkers for Depression.

Major depressive disorder (MDD) is an important cause of disability in the world. Depression has negative influences on a person's mental and physical health, quality of life, and functioning. The pathophysiology of depression has not yet been confirmed. The traditional monoamine hypothesis of MDD could not explain the unsatisfactory treatment response of antidepressants. Thus, it is necessary to search other probable pathophysiology of MDD. In recent years, the role of glutamate neurotransmission in depression has drawn much attention. The N-methyl-D-aspartate receptor (NMDAR) is a subclass of glutamate receptors and is implicated in the pathogenesis of MDD and other mental disorders. Furthermore, NMDAR ligands, such as ketamine and Dcycloserine, have shown antidepressive effects in several studies. The diagnosis of MDD depends on physician's subjective evaluation which is often inconsistent. Therefore, reliable objective laboratory biomarkers are essential for more accurate and consistent diagnosis of MDD. In this review, we firstly described the structure and regulation of the NMDAR. We then searched different genes involved in the pathway of glutamatergic neurotransmission and NMDAR, including D-amino acids, glycine, and glutamate. Various related enzymes and transporters that play a role in the modulation of NMDAR neurotransmission were also surveyed. This review aims to investigate NMDAR related metabolism, which may serve as feasible indicators for MDD and may contribute to further exploration of reliable biomarkers for MDD and promote new treatment of depression.

Salience-thalamic circuit uncouples in major depressive disorder, but not in bipolar depression

BackgroundBipolar depression (BDD) and major depressive disorder (MDD) are two diseases both characterized by depressed mood and diminished interest or pleasure. Recent neuroimaging studies have implicated the thalamo-cortical circuit in mood disorders, and the present study aimed to map thalamo-cortical connectivity to explore the dissociable and common abnormalities between bipolar and major depression in this circuit. MethodApplying resting-state functional magnetic resonance imaging (fMRI), we mapped the thalamo-cortical circuit using a fine-grained thalamic atlas with 8 sub-regions bilaterally in 38 BDD patients, 42 MDD patients and 39 healthy controls (HCs). Correlation analysis was then performed between thalamo-cortical connectivity and clinical variables. ResultThe findings showed that both patient groups exhibited prefronto-thalamo-cerebellar and sensorimotor-thalamic hypoconnectivity, while the abnormalities in MDD were more extensive. Particularly, MDD group showed decreased thalamic connectivity with the salience network including the insula, anterior cingulate cortex (ACC), and striatum. No correlations were found between the abnormal thalamo-cortical connectivity and clinical symptoms in either patient group. LimitationMost patients in our study were taking drugs at the time of scanning, which may confound our findings. ConclusionOur finding suggest that the thalamo-cortical hypofunction is a common neuro-substrate for BDD and MDD. Specifically, the hypoconnectivity between the thalamus and salience network including the insula, ACC and striatum may be a distinguished biomarker for MDD, which may help to differentiate these two emotional disorders.

Reduced plasma Fetuin-A is a promising biomarker of depression in the elderly.

Depression affects 7% of the elderly population, and it often remains misdiagnosed or untreated. Peripheral biomarkers might aid clinicians by allowing more accurate and well-timed recognition of the disease. We sought to determine if plasma protein levels predict the severity of depressive symptomatology or distinguish patients from healthy individuals. The severity of depressive symptoms and global cognitive functioning were assessed by the Geriatric Depression Scale (GDS) and Mini-Mental State Examination (MMSE) in 152 elderly subjects, 76 of which with major depressive disorder (MDD). Plasma levels of 24 proteins were measured by multiplexing and analyzed as continuous predictors or dichotomized using the median value. The association between individual plasma proteins and MDD risk or depressive symptoms severity was investigated using multiple logistic and linear regressions including relevant covariates. Sensitivity analyses were performed excluding cognitively impaired individuals or non-acute patients with MDD. After adjusting for possible confounders and false discovery rate (FDR) correction, we found lower Fetuin-A levels in MDD patients vs. controls (pFDR = 1.95 × 10-6). This result was confirmed by the sensitivity and dichotomized analyses. Lower prolactin (PRL) levels predicted more severe depressive symptoms in acute MDD patients (pFDR = 0.024). Fetuin-A is a promising biomarker of MDD in the elderly as this protein was negatively associated with the disorder in our sample, regardless of the global cognitive functioning. Lower PRL levels may be a peripheral signature of impaired neuroprotective processes and serotoninergic neurotransmission in more severely depressed patients.

Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression.

ObjectivesBiomarkers of major depressive disorder (MDD), its phases and forms have long been sought. Objectives were to examine whether the complexity of EEG activity, measured by Higuchi's fractal dimension (HFD) and sample entropy (SampEn), differs between healthy subjects, patients in remission, and in episode phase of the recurrent depression and whether the changes are differentially distributed between hemispheres and cortical regions.MethodsResting state EEG with eyes closed was recorded from 22 patients suffering from recurrent depression (11 in remission, 11 in the episode), and 20 age and sex‐matched healthy control subjects. Artifact‐free EEG epochs were analyzed by in‐house developed programs running HFD and SampEn algorithms.ResultsDepressed patients had higher HFD and SampEn complexity compared to healthy subjects. The complexity was higher in patients who were in remission than in those in the acute episode. Altered complexity was present in the frontal and centro‐parietal regions when compared to control group. The complexity in frontal and parietal regions differed between the two phases of depressive disorder.ConclusionsComplexity measures of EEG distinguish between the healthy controls, patients in remission and episode. Further studies are needed to establish whether these measures carry a potential to aid clinically relevant decisions about depression.

Prefrontal Asymmetry during Cognitive Tasks and its Relationship with Suicide Ideation in Major Depressive Disorder: An fNIRS Study.

Reduced oxygenation changes in the prefrontal cortex during cognitive tasks have been reported in major depressive disorder (MDD). However, prefrontal asymmetry during cognitive tasks and its relation to suicide ideations have been less frequently examined in patients with MDD. This study investigated prefrontal asymmetry and its moderating effect on the relationship between depression severity and suicidal ideation in MDD patients during cognitive tasks. Forty-two patients with MDD and 64 healthy controls (HCs) were assessed for changes in oxygenated and deoxygenated hemoglobin (Hb) in the prefrontal cortex using functional near-infrared spectroscopy (fNIRS) during the verbal fluency task (VFT), Stroop task, and two-back task. Depression, anxiety, and suicide ideation were measured through self-report questionnaires. Relatively smaller left oxy-Hb changes during VFT, but not during the Stroop or two-back tasks, were found in MDD patients compared with HCs. Furthermore, prefrontal asymmetry during VFT moderated the effect of depression severity on suicide ideation, and was significantly and positively correlated with suicide ideation in patients with MDD. Specifically, relatively greater left oxy-Hb changes were associated with greater suicide ideation. These findings suggest fNIRS-measured prefrontal asymmetry as a potential biomarker for MDD and for the assessment of suicidal risk in patients with MDD.

Promising Neuroimaging Biomarkers in Depression.

The neuroimaging has been applied in the study of pathophysiology in major depressive disorder (MDD). In this review article, several kinds of methodologies of neuroimaging would be discussed to summarize the promising biomarkers in MDD. For the magnetic resonance imaging (MRI) and magnetoencephalography field, the literature review showed the potentially promising roles of frontal lobes, such as anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). In addition, the limbic regions, such as hippocampus and amygdala, might be the potentially promising biomarkers for MDD. The structures and functions of ACC, DLPFC, OFC, amygdala and hippocampus might be confirmed as the biomarkers for the prediction of antidepressant treatment responses and for the pathophysiology of MDD. The functions of cognitive control and emotion regulation of these regions might be crucial for the establishment of biomarkers. The near-infrared spectroscopy studies demonstrated that blood flow in the frontal lobe, such as the DLPFC and OFC, might be the biomarkers for the field of near-infrared spectroscopy. The electroencephalography also supported the promising role of frontal regions, such as the ACC, DLPFC and OFC in the biomarker exploration, especially for the sleep electroencephalogram to detect biomarkers in MDD. The positron emission tomography (PET) and single-photon emission computed tomography (SPECT) in MDD demonstrated the promising biomarkers for the frontal and limbic regions, such as ACC, DLPFC and amygdala. However, additional findings in brainstem and midbrain were also found in PET and SPECT. The promising neuroimaging biomarkers of MDD seemed focused in the fronto-limbic regions.

A methylation study of long-term depression risk.

Recurrent and chronic Major Depressive Disorder (MDD) accounts for a substantial part of the disease burden because this course is most prevalent and typically requires long-term treatment. We associated blood DNA methylation profiles from 581 MDD patients at baseline with MDD status 6 years later. A resampling approach showed a highly significant association between methylation profiles in blood at baseline and future disease status (P=2.0×10−16). Top MWAS results were enriched specific pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and inflammation, and co-localized with eQTLS and (genic enhancers of) of transcription sites in brain and blood. Many of these findings remained significant after correction for multiple testing. The major themes emerging were cellular responses to stress and signaling mechanisms linked to immune cell migration and inflammation. This suggests that an immune signature of treatment-resistant depression is already present at baseline. We also created a methylation risk score (MRS) to predict MDD status 6 years later. The AUC of our MRS was 0.724 and higher than risk scores created using a set of five putative MDD biomarkers, genome-wide SNP data, and 27 clinical, demographic and lifestyle variables. Although further studies are needed to examine the generalizability to different patient populations, these results suggest that methylation profiles in blood may present a promising avenue to support clinical decision making by providing empirical information about the likelihood MDD is chronic or will recur in the future.

Major depressive disorder is associated with changes in a cluster of serum and urine biomarkers

Major Depressive Disorder (MDD) is a heterogeneous disorder with a considerable symptomatic overlap with other psychiatric and somatic disorders. This study aims at providing evidence for association of a set of serum and urine biomarkers with MDD. We analyzed urine and serum samples of 40 MDD patients and 47 age- and sex-matched controls using 40 potential MDD biomarkers (21 serum biomarkers and 19 urine biomarkers). All participants were of Caucasian origin. We developed an algorithm to combine the heterogeneity at biomarker level. This method enabled the identification of correlating biomarkers based on differences in variation and distribution between groups, combined the outcome of the selected biomarkers, and calculated depression probability scores (the “bio depression score”). Phenotype permutation analysis showed a significant discrimination between MDD and euthymic (control) subjects for biomarkers in urine (P < .001), in serum (P = .02) and in the combined serum plus urine result (P < .001). Based on this algorithm, a combination of 8 urine biomarkers and 9 serum biomarkers were identified to correlate with MDD, enabling an area under the curve (AUC) of 0.955 in a Receiver Operating Characteristic (ROC) analysis. Selection of either urine biomarkers or serum biomarkers resulted in AUC values of 0.907 and 0.853, respectively. Internal cross-validation (5-fold) confirmed the association of this set of biomarkers with MDD.

Co-Expression Network Analysis Revealed That the ATP5G1 Gene Is Associated With Major Depressive Disorder.

Major depressive disorder (MDD) is a leading cause of disability worldwide, although its etiology and mechanism remain unknown. The aim of our study was to identify hub genes associated with MDD and to illustrate the underlying mechanisms. A weighted gene co-expression network analysis (WGCNA) was performed to identify significant gene modules and hub genes associated with MDD in peripheral blood mononuclear cells (PBMCs) (n = 45). In the blue module (R 2 = 0.95), five common hub genes in both co-expression network and protein–protein interaction (PPI) network were regarded as “real” hub genes. In another independent dataset, GSE52790, four genes were still significantly down-regulated in PBMCs from MDD patients compared with the controls. Furthermore, these four genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in PBMCs from 33 MDD patients and 41 healthy controls. The qRT-PCR analysis showed that ATP synthase membrane subunit c locus 1 (ATP5G1) was significantly down-regulated in samples from MDD patients than in control samples (t = −2.89, p-value = 0.005). Moreover, this gene was significantly differentially expressed between patients and controls in the prefrontal cortex (z = −2.83, p-value = 0.005). Highly significant differentially methylated positions were identified in the Brodmann area 25 (BA25), with probes in the ATP5G1 gene being significantly associated with MDD: cg25495775 (t = 2.82, p-value = 0.008), cg25856120 (t = −2.23, p-value = 0.033), and cg23708347 (t = −2.24, p-value = 0.032). These findings indicate that the ATP5G1 gene is associated with the pathogenesis of MDD and that it could serve as a peripheral biomarker for MDD.

Altered expression of long noncoding RNAs in patients with major depressive disorder

Although major depressive disorder (MDD) is a leading cause of disability worldwide, its pathophysiology is poorly understood. Increasing evidence suggests that aberrant regulation of transcription plays a key role in the pathophysiology of MDD. Recently, long noncoding RNAs (lncRNAs) have been recognized for their important functions in chromatin structure, gene expression, and the subsequent manifestation of various biological processes in the central nervous system. However, it is unclear whether the aberrant expression and function of lncRNAs are associated with the pathophysiology of MDD. In this study, we sought to evaluate the expression of lncRNAs in peripheral blood leukocytes as potential biomarkers for MDD. We measured the expression levels of 83 lncRNAs in the peripheral blood leukocytes of 29 MDD patients and 29 age- and gender-matched healthy controls using quantitative reverse transcription PCR (RT-qPCR) analysis. We found that MDD patients exhibited distinct expression signatures. Specifically, the expression level of one lncRNA (RMRP) was lower while the levels of four (Y5, MER11C, PCAT1, and PCAT29) were higher in MDD patients compared to healthy controls. The expression level of RMRP was correlated with depression severity as measured by the Hamilton Depression Rating Scale (HAM-D). Moreover, RMRP expression was lower in a mouse model of depression, corroborating the observation from MDD patients. Taken together, our data suggest that lower RMRP levels may serve as a potential biomarker for MDD.

Methylation analysis for postpartum depression: a case control study

BackgroundPostpartum depression (PPD) is a major depressive disorder that occurs after childbirth. Objective diagnostic and predictive methods for PPD are important for early detection and appropriate intervention. DNA methylation has been recognized as a potential biomarker for major depressive disorder. In this study, we used methylation analysis and peripheral blood to search for biomarkers that could to lead to the development a predictive method for PPD.MethodsStudy participants included 36 pregnant women (18 cases and 18 controls determined after childbirth). Genome-wide DNA methylation profiles were obtained by analysis with an Infinium Human Methylation 450BeadChip. The association of DNA methylation status at each DNA methylation site with PPD was assessed using linear regression analysis. We also conducted functional enrichment analysis of PPD using The Database for Annotation, Visualization and Integrated Discovery 6.8 to explore enriched functional-related gene groups for PPD.ResultsIn the analysis with postpartum depressed state as an independent variable, the difference in methylation frequency between the postpartum non-depressed group and the postpartum depressed group was small, and sites with genome-wide significant differences were not confirmed. After analysis by The Database for Annotation, Visualization and Integrated Discovery 6.8, we revealed four gene ontology terms, including axon guidance, related to postpartum depression.ConclusionsThese findings may help with the development of an objective predictive method for PPD.

Lysophosphatidic acid levels in cerebrospinal fluid and plasma samples in patients with major depressive disorder

Major depressive disorder (MDD) is the most common psychiatric disorders. However, a biochemical marker has yet to be established for clinical purposes. It is proposed that lysophosphatidic acid (LPA, 1-acyl-2-sn-glycerol-3-phosphoate) plays some important roles in emotional regulation of experimental animals. Therefore, in this study, we measured LPA levels using enzyme-linked immunosorbent assays of cerebrospinal fluid (CSF) and plasma samples from patients with MDD. The participants were 52 patients and 49 normal healthy controls for CSF study, and 47 patients and 44 controls for plasma study. We used the Japanese version of the GRID Hamilton Depression Rating Scale (17-item version) for the assessment of depressive symptoms. We found no associations between LPA levels (CSF or plasma) and either diagnosis or severity of MDD, or with psychotropic medication. In conclusion, our data suggest that LPA levels likely would not serve as a practical biomarker of MDD.

Alpha-Synuclein RNA Expression is Increased in Major Depression.

Alpha-synuclein (SNCA) is a small membrane protein that plays an important role in neuro-psychiatric diseases. It is best known for its abnormal subcellular aggregation in Lewy bodies that serves as a hallmark of Parkinson’s disease (PD). Due to the high comorbidity of PD with depression, we investigated the role of SNCA in patients suffering from major depressive disorder (MDD). SNCA mRNA expression levels were analyzed in peripheral blood cells of MDD patients and a healthy control group. SNCA mRNA expression was positively correlated with severity of depression as indicated by psychometric assessment. We found a significant increase in SNCA mRNA expression levels in severely depressed patients compared with controls. Thus, SNCA analysis could be a helpful target in the search for biomarkers of MDD.

Similarly in depression, nuances of gut microbiota: Evidences from a shotgun metagenomics sequencing study on major depressive disorder versus bipolar disorder with current major depressive episode patients

BackgroundTo probe the differences of gut microbiota among major depressive disorder (MDD), bipolar disorder with current major depressive episode (BPD) and health participants. MethodsThirty one MDD patients, thirty BPD patients, and thirty healthy controls (HCs) were recruited. All the faecal samples were analyzed by shotgun metagenomics sequencing. Except for routine analyses of alpha diversity, we specially designed a new indicator, the Gm coefficient, to evaluate the inequality of relative abundances of microbiota for each participant. ResultsThe Gm coefficients are significant decreased in both MDD and BPD groups. The relative abundances of increased phyla Firmicutes and Actinobacteria and decreased Bacteroidetes were significantly in the MDD and BPD groups. At genus level, four of top five enriched genera (Bacteroides, Clostridium, Bifidobacterium, Oscillibacter and Streptococcus) were found increased significantly in the MDD and BPD groups compared with HCs. The genera Escherichia and Klebsiella showed significant changes in abundances only between the BPD and HC groups. At the species level, compared with BPD patients, MDD patients had a higher abundance of Prevotellaceae including Prevotella denticola F0289, Prevotella intermedia 17, Prevotella ruminicola, and Prevotella intermedia. Furthermore, the abundance of Fusobacteriaceae, Escherichia blattae DSM 4481 and Klebsiella oxytoca were significantly increased, whereas the Bifidobacterium longum subsp. infantis ATCC 15697 = JCM 1222 was significantly reduced in BPD group compared with MDD group. ConclusionsOur study suggested that gut microbiota may be involved in the pathogenesis of both MDD and BPD patients, and the nuances of bacteria may have the potentiality of being the biomarkers of MDD and BPD.

The Pursuit of Pleasure

Many component processes of reward require appropriate serotonin (5HT) and dopamine (DA) neurotransmission within key limbic brain regions. Evidence suggests that dysregulation of 5HT and DA transmission can precipitate reward dysfunction and major depressive disorder (MDD) symptoms in genetically predisposed individuals. Various neurobiological indicators (biomarkers) of MDD have been proposed, including changes in signal transduction pathways, protein phosphorylation, and gene expression in subcortical, reward-related structures. However, these insights have yielded limited clinically relevant benefits for diagnosis, treatment, or prognosis. In addition, clinical application of identified biomarkers is often hindered by multiple factors including disease heterogeneity and symptom variability between patients. Innovative approaches including big data analytics, methodical collaboration between research programs, and reverse-translational strategies are now required to understand if particular biomarkers can be used to predict disease onset and treatment response, to stratify treatments for patient subgroups, and develop novel pharmacotherapies. This review briefly summarizes the predictive value of big data analytics in parsing the neurobiological underpinnings of MDD, with a focus on potential clinically-viable biomarkers for predictive therapies.

Hippocampal subfield-specific connectivity findings in major depressive disorder: A 7 Tesla diffusion MRI study

ObjectiveDiffusion magnetic resonance imaging (dMRI) enables non-invasive characterization of white matter (WM) structures in vivo. Prior studies suggest that certain WM tracts may be affected in major depressive disorder (MDD), however, hippocampal subfield-specific dMRI measures have not yet been explored in MDD. We use 7 Tesla dMRI to investigate differences in hippocampal subfield connectivity of MDD patients. MethodsEighteen MDD patients and eighteen matched healthy volunteers underwent 7 Tesla MRI. The hippocampal formations were segmented by subfields and tractography was performed to determine streamline count (SC), fractional anisotropy (FA), and mean diffusivity (MD) in patients and controls. Significant subfield connectivity measures were also correlated with age at depression onset. ResultsCompared with controls, MDD patients exhibited reduced SC in the molecular layer of the left dentate gyrus (p < 0.001). SC count in the left dentate gyrus was shown to positively correlate with age at disease onset (p < 0.05). Increased MD was observed in streamlines emanating from both the left (p = 0.0001) and right (p < 0.001) fimbriae in MDD patients. ConclusionsIncreased MD of tracts in the fimbriae suggests compromised neuronal membranes in the major hippocampal output gate. Reduced SC of the dentate gyri indexes a disruption of normal cellular processes such as neurogenesis. These findings may have significant implications for identifying biomarkers of MDD and elucidating the neurobiological underpinnings of depression.