Individual Variations in Nucleus Accumbens Responses Associated with Major Depressive Disorder Symptoms

Abnormal reward-related responses in the nucleus accumbens (NAcc) have been reported for major depressive disorder (MDD) patients. However, variability exists in the reported results, which could be due to heterogeneity in neuropathology of depression. To parse the heterogeneity of MDD we investigated variation of NAcc responses to gain and loss anticipations using fMRI. We found NAcc responses to monetary gain and loss were significantly variable across subjects in both MDD and healthy control (HC) groups. The variations were seen as a hyperactive response subtype that showed elevated activation to the anticipation of both gain and loss, an intermediate response with greater activation to gain than loss, and a suppressed-activity with reduced activation to both gain and loss compared to a non-monetary condition. While these response variability were seen in both MDD and HC subjects, specific symptoms were significantly associated with the right NAcc variation in MDD. Both the hyper- and suppressed-activity subtypes of MDD patients had severe suicidal ideation and anhedonia symptoms. The intermediate subjects had less severity in these symptoms. These results suggest that differing propensities in reward responsiveness in the NAcc may affect the development of specific symptoms in MDD

The association of kynurenine pathway metabolites with symptom severity and clinical features of bipolar disorder: An overview

Background. The balance between neurotoxic and neuroprotective effects of kynurenine pathway (KP) components has been recently proposed as a key element in the pathophysiology of bipolar disorder (BD) and related mood episodes. This comprehensive overview explored the link of KP with symptom severity and other clinical features of BD. Methods. We searched Medline, Embase, and PsycInfo electronic databases for studies assessing the association of peripheral and/or central concentrations of KP metabolites with putative clinical features, including symptom severity and other clinical domains in BD. Results. We included the findings of 13 observational studies investigating the possible variations of KP metabolites according to symptom severity, psychotic features, suicidal behaviors, and sleep disturbances in BD. Studies testing the relationship between KP metabolites and depression severity generated mixed and inconsistent findings. No statistically significant correlations with manic symptoms were found. Moreover, heterogeneous variations of the KP across different clinical domains were shown. Few available studies found (a) higher levels of cerebrospinal fluid kynurenic acid and lower of plasma quinolinic acid in BD with psychotic features, (b) lower central and peripheral picolinic acid levels in BD with suicide attempts, and (c) no significant correlations between KP metabolites and BD-related sleep disturbances. Conclusions. An imbalance of KP metabolism toward the neurotoxic branches is likely to occur in people with BD, though evidence on variations according to specific clinical features of BD is less clear. Additional research is needed to clarify the role of KP in the etiopathogenesis of BD and related clinical features

Effects of arterial cannulation stress on regional cerebral blood flow in major depressive disorder

Individuals with major depressive disorder (MDD) display abnormal neurophysiological responses to psychological stress but little is known about their neurophysiological responses to physiological stressors. Using [15O-H2O] positron emission tomography we assessed whether the regional cerebral blood flow (rCBF) response to arterial cannulation differed between patients with MDD and healthy controls (HCs). Fifty-one MDD patients and 62 HCs were scanned following arterial cannulation and 15 MDD patients and 17 HCs were scanned without arterial cannulation. A region-of-interest analysis showed that a significantly increased rCBF of the anterior cingulate cortex and right amygdala was associated with arterial cannulation in MDD. A whole brain analysis showed increased rCBF of the right post-central gyrus, left temporopolar cortex, and right amygdala during arterial cannulation in MDD patients. The rCBF in the right amygdala was significantly correlated with depression severity. Conceivably, the limbic response to invasive physical stress is greater in MDD subjects than in HCs

Identification and replication of RNA-Seq gene network modules associated with depression severity

Genomic variation underlying major depressive disorder (MDD) likely involves the interaction and regulation of multiple genes in a network. Data-driven co-expression network module inference has the potential to account for variation within regulatory networks, reduce the dimensionality of RNA-Seq data, and detect significant geneexpression modules associated with depression severity. We performed an RNA-Seq gene co-expression network analysis of mRNA data obtained from the peripheral blood mononuclear cells of unmedicated MDD (n = 78) and healthy control (n = 79) subjects. Across the combined MDD and HC groups, we assigned genes into modules using hierarchical clustering with a dynamic tree cut method and projected the expression data onto a lower-dimensional module space by computing the single-sample gene set enrichment score of each module. We tested the singlesample scores of each module for association with levels of depression severity measured by the Montgomery-Åsberg Depression Scale (MADRS). Independent of MDD status, we identified 23 gene modules from the co-expression network. Two modules were significantly associated with the MADRS score after multiple comparison adjustment (adjusted p = 0.009, 0.028 at 0.05 FDR threshold), and one of these modules replicated in a previous RNA-Seq study of MDD (p = 0.03). The two MADRS-associated modules contain genes previously implicated in mood disorders and show enrichment of apoptosis and B cell receptor signaling. The genes in these modules show a correlation between network centrality and univariate association with depression, suggesting that intramodular hub genes are more likely to be related to MDD compared to other genes in a module

Trends in Suicidology: Personality as an Endophenotype for Molecular Genetic Investigations

In studying the genetics of suicide, should personality be used as an endophenotype (an intermediate trait lying somewhere on the developmental pathway from genes to phenotype)

The Functional DRD3 Ser9Gly Polymorphism (rs6280) Is Pleiotropic, Affecting Reward as Well as Movement

Abnormalities of motivation and behavior in the context of reward are a fundamental component of addiction and mood disorders. Here we test the effect of a functional missense mutation in the dopamine 3 receptor (DRD3) gene (ser9gly, rs6280) on reward-associated dopamine (DA) release in the striatum. Twenty-six healthy controls (HCs) and 10 unmedicated subjects with major depressive disorder (MDD) completed two positron emission tomography (PET) scans with [11C]raclopride using the bolus plus constant infusion method. On one occasion subjects completed a sensorimotor task (control condition) and on another occasion subjects completed a gambling task (reward condition). A linear regression analysis controlling for age, sex, diagnosis, and self-reported anhedonia indicated that during receipt of unpredictable monetary reward the glycine allele was associated with a greater reduction in D2/3 receptor binding (i.e., increased reward-related DA release) in the middle (anterior) caudate (p<0.01) and the ventral striatum (p<0.05). The possible functional effect of the ser9gly polymorphism on DA release is consistent with previous work demonstrating that the glycine allele yields D3 autoreceptors that have a higher affinity for DA and display more robust intracellular signaling. Preclinical evidence indicates that chronic stress and aversive stimulation induce activation of the DA system, raising the possibility that the glycine allele, by virtue of its facilitatory effect on striatal DA release, increases susceptibility to hyperdopaminergic responses that have previously been associated with stress, addiction, and psychosis

EEG Microstates Temporal Dynamics Differentiate Individuals with Mood and Anxiety Disorders From Healthy Subjects

Electroencephalography (EEG) measures the brain’s electrophysiological spatio-temporal activities with high temporal resolution. Multichannel and broadband analysis of EEG signals is referred to as EEG microstates (EEG-ms) and can characterize such dynamic neuronal activity. EEG-ms have gained much attention due to the increasing evidence of their association with mental activities and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). Spatially independent EEG-ms are quasi-stationary topographies (e.g., stable, lasting a few dozen milliseconds) typically classified into four canonical classes (microstates A through D). They can be identified by clustering EEG signals around EEG global field power (GFP) maxima points. We examined the EEG-ms properties and the dynamics of cohorts of mood and anxiety (MA) disorders subjects (n = 61) and healthy controls (HCs; n = 52). In both groups, we found four distinct classes of EEG-ms (A through D), which did not differ among cohorts. This suggests a lack of significant structural cortical abnormalities among cohorts, which would otherwise affect the EEG-ms topographies. However, both cohorts’ brain network dynamics significantly varied, as reflected in EEG-ms properties. Compared to HC, the MA cohort features a lower transition probability between EEG-ms B and D and higher transition probability from A to D and from B to C, with a trend towards significance in the average duration of microstate C. Furthermore, we harnessed a recently introduced theoretical approach to analyze the temporal dependencies in EEG-ms. The results revealed that the transition matrices of MA group exhibit higher symmetrical and stationarity properties as compared to HC ones. In addition, we found an elevation in the temporal dependencies among microstates, especially in microstate B for the MA group. The determined alteration in EEG-ms temporal dependencies among the cohorts suggests that brain abnormalities in mood and anxiety disorders reflect aberrant neural dynamics and a temporal dwelling among ceratin brain states (i.e., mood and anxiety disorders subjects have a less dynamicity in switching between different brain states)

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE

Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the “Brain Age Gap Estimate” (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to “regression to the mean.” The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18–60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18–56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores