Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder

Fecal microbiota as a predictor of acupuncture responses in patients with postpartum depressive disorder

Background

There are several clinical and molecular predictors of responses to antidepressant therapy. However, these markers are either too subjective or complex for clinical use. The gut microbiota could provide an easily accessible set of biomarkers to predict therapeutic efficacy, but its value in predicting therapy responses to acupuncture in patients with depression is unknown. Here we analyzed the predictive value of the gut microbiota in patients with postpartum depressive disorder (PPD) treated with acupuncture.

Methods

Seventy-nine PPD patients were enrolled: 55 were treated with acupuncture and 24 did not received any treatment. The 17-item Hamilton depression rating scale (HAMD-17) was used to assess patients at baseline and after eight weeks. Patients receiving acupuncture treatment were divided into an acupuncture-responsive group or non-responsive group according to HAMD-17 scores changes. Baseline fecal samples were obtained from the patients receiving acupuncture and were analyzed by high-throughput 16S ribosomal RNA sequencing to characterize the gut microbiome.

Results

47.27% patients responded to acupuncture treatment and 12.5% patients with no treatment recovered after 8-week follow-up. There was no significant difference in α-diversity between responders and non-responders. The β-diversity of non-responders was significantly higher than responders. Paraprevotella and Desulfovibrio spp. were significantly enriched in acupuncture responders, and these organisms had an area under the curve of 0.76 and 0.66 for predicting responder patients, respectively.

Conclusions

Paraprevotella and Desulfovibrioare may be useful predictive biomarkers to predict PPD patients likely to respond to acupuncture. Larger studies and validation in independent cohorts are now needed to validate our findings.

An electroencephalography connectome predictive model of major depressive disorder severity

Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.

Common network effect-patterns after monoamine reuptake inhibition in dissociated hippocampus cultures

The pharmacological treatment of major depressive disorder with currently available antidepressant drugs is still unsatisfying as response to medication is delayed and in some patients even non-existent. To understand complex psychiatric diseases such as major depressive disorder and their treatment, research focus is shifting from investigating single neurons towards a view of the entire functional and effective neuronal network, because alterations on single synapses through antidepressant drugs may translate to alterations in the entire network. Here, we examined the effects of monoamine reuptake inhibitors on in vitro hippocampal network dynamics using calcium fluorescence imaging and analyzing the data with means of graph theoretical parameters. Hypothesizing that monoamine reuptake inhibitors operate through changes of effective connectivity on micro-scale neuronal networks, we measured the effects of the selective monoamine reuptake inhibitors GBR-12783, Sertraline, Venlafaxine, and Amitriptyline on neuronal networks. We identified a common pattern of effects of the different tested monoamine reuptake inhibitors. After treatment with GBR-12783, Sertraline, and Venlafaxine, the connectivity degree, measuring the number of existing connections in the network, was significantly decreased. All tested substances led to networks with more submodules and a reduced global efficiency. No monoamine reuptake inhibitor did affect network-wide firing rate, the characteristic path length, or the network strength. In our study, we found that monoamine reuptake inhibition in neuronal networks in vitro results in a sharpening of the network structure. These alterations could be the basis for the reorganization of a large-scale miswired network in major depressive disorder.

Functional connectivity of the hippocampus in predicting early antidepressant efficacy in patients with major depressive disorder

BAKGROUD

The hippocampus is involved in the pathophysiology of major depressive disorder (MDD), and its structure and function have been reported to be related to the antidepressant response. This study aimed to identify relationships between hippocampal functional connectivity (FC) and early improvement in patients with MDD and to further explore the ability of hippocampal FC to predict early efficacy.

METHODS

Thirty-six patients with nonpsychotic MDD were recruited and underwent resting-state functional magnetic resonance imaging scans at baseline. After two weeks of treatment with escitalopram, patients were divided into subgroups with early improved depression (EID, n= 19) and nonimproved depression (NID, n=17) . A voxelwise FC analysis was performed with the bilateral hippocampus as seeds, two-sample t-tests were used to compare hippocampal FC between groups. Receiver operating characteristic (ROC) curves were constructed to determine the best FC measures and optimal threshold for differentiating EID from END.

RESULTS

The EID group showed significantly higher FC between the left hippocampus and left inferior frontal gyrus and precuneus than the END group. And the left hippocampal FC of these two regions were positively correlated with the reduction ratio of the depressive symptom scores. The ROC curve analysis revealed that summed FC scores for these two regions exhibited the highest area under the curve, with a sensitivity of 0.947 and specificity of 0.882 at a summed score of 0.14.

LIMITATIONS

The sample used in this study was relatively small.

CONCLUSIONS

These findings demonstrated that FC of the left hippocampus can predict early efficacy of antidepressant.

A lateralized model of the pain-depression dyad

Chronic pain and depression are two frequently co-occurring and debilitating conditions. Even though the former is treated as a physical affliction, and the latter as a mental illness, both disorders closely share neural substrates. Here, we review the association of pain with depression, especially when symptoms are lateralized on either side of the body. We also explore the overlapping regions in the forebrain implicated in these conditions. Finally, we synthesize these findings into a model, which addresses gaps in our understanding of comorbid pain and depression. Our lateralized pain-depression dyad model suggests that individuals diagnosed with depression should be closely monitored for pain symptoms in the left hemibody. Conversely, for patients in pain, with the exception of acute pain with a known source, referrals in today's pain centers for psychological evaluation should be part of standard practice, within the framework of an interdisciplinary approach to pain treatment.

Topological network mechanisms of clinical response to antidepressant treatment in drug-naive major depressive disorder

AIM

There is rapidly increasing evidence that remission of MDD is associated with substantial changes in functional brain connectivity. These New data have provided a holistic view on the mechanism of antidepressants on multiple levels that goes beyond their conventional effects on neurotransmitters.

METHOD

The study was approved by the Local Ethics Committee of Istanbul Medipol University (10840098-604.01.01-E.65129) and followed the Helsinki Declaration principles. In our study, we have evaluated the effect of six weeks of treatment with antidepressants (escitalopram and duloxetine), and tested the underlying brain functional connectivity through a Graph analysis approach in a well-defined first-episode, drug-naive, and non-comorbid population with MDD.

RESULTS

Beyond indicating that there was a significant correlation between the antidepressant response and topological characteristics of the brain, our results suggested that global rather than regional network alterations may be implicated in the antidepressant effect.

CONCLUSION

Despite the small-sample size and non-controlled study design, our study provides important and relevant clinical data regarding the underlying mechanisms of the antidepressants on topological dynamics in the human brain.

Graph theoretical measures of the uncinate fasciculus subnetwork as predictors and correlates of treatment response in a transdiagnostic psychiatric cohort

The internalizing psychopathologies (IP) are a highly prevalent group of disorders for which little data exists to guide treatment selection. We examine whether graph theoretical metrics from white matter connectomes may serve as biomarkers of disease and predictors of treatment response. We focus on the uncinate fasciculus subnetwork, which has been previously implicated in these disorders. We compared baseline graph measures from a transdiagnostic IP cohort with controls. Patients were randomized to either SSRI or cognitive behavioral therapy and we determined if graph theory metrics change following treatment, and whether these changes correlated with treatment response. Lastly, we investigated whether baseline metrics correlated with treatment response. Several baseline nodal graph metrics differed at baseline. Of note, right amygdala betweenness centrality was increased in patients relative to controls. In addition, white matter integrity of the uncinate fasciculus was decreased at baseline in patients versus controls. The SSRI and CBT cohorts had increased left frontal superior orbital betweenness centrality and left frontal medial orbital clustering coefficient, respectively, suggesting the presence of treatment specific neural correlates of treatment response. This study provides insight on shared white matter network features of IPs and elucidates potential biomarkers of treatment response that may be modality-specific.

The influence of ketamine's repeated treatment on brain topology does not suggest an antidepressant efficacy

As ketamine is increasingly used as an effective antidepressant with rapid action, sustaining its short-lived efficacy over a longer period of time using a schedule of repeated injections appears as an option. An open question is whether repeated and single administrations would affect convergent neurocircuits. We used a combination of one of the most robust animal models of depression with high-field neuroimaging to perform a whole-brain delineation of functional mechanisms underlying ketamine's effects. Rats from two genetic strains, depressive-like and resilient, received seven treatments of 10 mg/kg S-ketamine (N = 14 depressive-like, N = 11 resilient) or placebo (N = 12 depressive-like, N = 10 resilient) and underwent resting-state functional magnetic resonance imaging. Using graph theoretical models of brain networks, we compared effects of repeated ketamine with those of single administration from a separate dataset of our previous study. Compared to single treatment, repeated ketamine evoked strain-specific brain network randomization, resembling characteristics of the depressive-like strain and patients. Several affected regions belonged to the auditory, visual, and motor circuitry, hinting at possible cumulative side effects. Finally, when compared to saline, repeated ketamine affected only a few local topological properties and had no effects on global properties. In combination with the lack of clear differences compared to placebo, our findings point toward an inefficacy of ketamine's long-term administration on brain topology, making questionable the postulated effect of repeated administration and being consistent with the recently reported absence of repeated ketamine's antidepressant efficacy in several placebo-controlled studies.

Brain structural changes associated with aberrant functional responses to the Valsalva maneuver in heart failure

Heart failure (HF) patients show inability to regulate autonomic functions in response to autonomic challenges. The autonomic deficits may stem from brain tissue injury in central autonomic regulatory areas, resulting from ischemic and hypoxic processes accompanying the condition. However, the direct evaluation of correlations between brain structural injury and functional timing and magnitude of neural signal patterns within affected areas, which may lead to impaired autonomic outflow, is unclear. In this study, we evaluate neural responses to the Valsalva maneuver with blood oxygen level-dependent functional magnetic resonance imaging in 29 HF patients and 35 control subjects and brain structural changes using diffusion tensor imaging-based mean diffusivity in a subsample of 19 HF and 24 control subjects. HF showed decreased neural activation in multiple autonomic and motor control areas, including cerebellum cortices, vermis, left insular, left putamen, and bilateral postcentral gyrus. Structural brain changes emerged in similar autonomic, as well as cognitive and mood regulation areas. Functional MRI responses in cerebellum and insula in HF subjects are delayed or decreased in magnitude to the challenge. The impaired functional responses of insular and cerebellar sites are correlated with the severity of tissue changes. These results indicate that the functions of insular and cerebellar regions, sites that are involved in autonomic regulation, are compromised, and that autonomic deficits in these areas have brain structural basis for impaired functions. Our study enhanced our understanding of brain structural and functional alterations underlying impaired autonomic regulations in HF subjects.

Differences between ketamine's short-term and long-term effects on brain circuitry in depression

Ketamine acts as a rapid clinical antidepressant at 25 min after injection with effects sustained for 7 days. As dissociative effects emerging acutely after injection are not entirely discernible from therapeutic action, we aimed to dissect the differences between short-term and long-term response to ketamine to elucidate potential imaging biomarkers of ketamine's antidepressant effect. We used a genetical model of depression, in which we bred depressed negative cognitive state (NC) and non-depressed positive cognitive state (PC) rat strains. Four parallel rat groups underwent stress-escape testing and a week later received either S-ketamine (12 NC, 13 PC) or saline (12 NC, 12 PC). We acquired resting-state functional magnetic resonance imaging time series before injection and at 30 min and 48 h after injection. Graph analysis was used to calculate brain network properties. We identified ketamine's distinct action over time in a qualitative manner. The rapid response entailed robust and strain-independent topological modifications in cognitive, sensory, emotion, and reward-related circuitry, including regions that exhibited correlation of connectivity metrics with depressive behavior, and which could explain ketamine's dissociative and antidepressant properties. At 48 h ketamine had mainly strain-specific action normalizing habenula, midline thalamus, and hippocampal connectivity measures in depressed rats. As these nodes mediate cognitive flexibility impaired in depression, action within this circuitry presumably reflects ketamine's procognitive effects induced only in depressed patients. This finding is especially valid, as our model represents cognitive aspects of depression. These empirically defined circuits explain ketamine's distinct action over time and might serve as translational imaging correlates of antidepressant response in preclinical testing.

Variance of the global signal as a pretreatment predictor of antidepressant treatment response in drug-naïve major depressive disorder

Several behavioral and neuroimaging markers could be used to predict eventual antidepressant medication (ADM) outcomes in patients with major depressive disorder (MDD). However, these predictors are either subjective or complex, which has limited their clinical use. Thus, we aimed to identify an objective and easy-to-get marker to predict early therapeutic efficacy. Forty-seven drug-naïve patients with MDD and 47 age-, gender- and education-matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) scans. We calculated the variable coefficient (VC) of the global signal for each subject. Baseline Hamilton Rating Scale for Depression (HRSD) score and that after 2 weeks of ADM were assessed for patients. Although there was no difference in VC between patients with MDD and healthy controls, we found a significant positive correlation between the VC and the decline rate of HRSD scores in the patients. Compared with the non-responding depression (NRD) group, the treatment-responsive depression (TRD) group had a higher VC. Receiver operator characteristic curve analysis revealed that the VC exhibited a good ability to differentiate TRD from NRD. In addition, the linear and logistic regression analyses showed that the VC was a significant predictor of the decline rate of HRSD scores and the antidepressant treatment response. These findings suggest that variance of the global signal may serve as a useful marker to help clinicians find an appropriate drug for individuals with MDD at the earliest opportunity and then further to facilitate personalized therapy.

Increased temporal variability of striatum region facilitating the early antidepressant response in patients with major depressive disorder

The aim of this study is to identify the difference of temporal variability among major depressive disorder (MDD) patients (with different early antidepressant responses) and healthy controls (HC), and further explore the relationship between pre-treatment temporal variability and early antidepressant response. At baseline, 77 treatment-naïve inpatients with MDD and 42 matched HC received clinical assessments and 3.0 Tesla resting-state functional magnetic resonance imaging scans. After 2 weeks' antidepressant treatment, the patients were subgrouped into responsive depression (RD, n = 40) and non-responding depression (NRD, n = 37) based on the reduction of Hamilton depression rating scale (HAMD). The temporal variability of 90 brain nodes was calculated for further analysis. Compared with the HC group, both the RD and NRD subjects showed greater baseline temporal variability (i.e., greater dynamic) in the left inferior occipital gyrus. Significantly greater temporal variability in the left pallidum was found in the RD group than the NRD and the HC groups, and the higher variability of left pallidum correlated positively with the HAMD reduction. Moreover, the pooled MDD (i.e., RD and NRD) group showed greater baseline temporal variability in the right inferior frontal gyrus, the left inferior occipital gyrus, the bilateral fusiform gyri and the left Heschl gyrus than the HC group. The distinctive pattern of dynamically reorganized networks may provide a crucial scaffold to facilitate early antidepressant response, and the temporal variability may serve as a promising indicator for the personalized therapy of MDD.

Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study

Background

Electroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a useful alternative. We analyzed EEG-based source-level network in major depressive disorder (MDD).

Method

Resting-state EEG was recorded in 87 MDD and 58 healthy controls, and cortical source signals were estimated. Network measures were calculated: global indices (strength, clustering coefficient (CC), path length (PL), and efficiency) and nodal indices (eigenvector centrality and nodal CC) in six frequency. Correlation analyses were performed between network indices and symptom scales.

Results

At the global level, MDD showed decreased strength, CC in theta and alpha bands, and efficiency in alpha band, while enhanced PL in alpha band. At nodal level, eigenvector centrality of alpha band showed region dependent changes in MDD. Nodal CCs of alpha band were reduced in MDD and were negatively correlated with depression and anxiety scales.

Conclusion

Disturbances in EEG-based brain network indices might reflect altered emotional processing in MDD. These source-level network indices might provide useful biomarkers to understand regional brain pathology in MDD.

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We investigated the altered function of electroencephalogram-based cortical network for major depressive disorder (MDD).

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MDD showed altered cortical brain network at both global and nodal level network in alpha frequency band.

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Abnormal network indices in MDD were significantly correlated with depression and anxiety symptom scale scores.

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Disrupted cortical networks band might reflect altered neural pathway during emotional processing in patients with MDD.

Antagonism at the NR2B subunit of NMDA receptors induces increased connectivity of the prefrontal and subcortical regions regulating reward behavior

RATIONALE

Evidence indicates that ketamine's rapid antidepressant efficacy likely results from its antagonism of NR2B-subunit-containing NMDA receptors (NMDAR). Since ketamine equally blocks NR2A- and NR2B-containing NMDAR, and has affinity to other receptors, NR2B-selective drugs might have improved therapeutic efficiency and side effect profile.

OBJECTIVES

We aimed to compare the effects of (S)-ketamine and two different types of NR2B-selective antagonists on functional brain networks in rats, in order to find common circuits, where their effects intersect, and that might explain their antidepressant action.

METHODS

The experimental design comprised four parallel groups of rats (N = 37), each receiving (S)-Ketamine, CP-101,606, Ro 25-6981 or saline. After compound injection, we acquired resting-state functional magnetic resonance imaging time series. We used graph theoretical approach to calculate brain network properties.

RESULTS

Ketamine and CP-101,606 diminished the global clustering coefficient and small-worldness index. At the nodal level, all compounds induced increased connectivity of the regions mediating reward and cognitive aspects of emotional processing, such as ventromedial prefrontal cortex, septal nuclei, and nucleus accumbens. The dorsal hippocampus and regions involved in sensory processing and aversion, such as superior and inferior colliculi, exhibited an opposite effect.

CONCLUSIONS

The effects common to ketamine and NR2B-selective compounds were localized to the same brain regions as those reported in depression, but in the opposite direction. The upregulation of the reward circuitry might partially underlie the antidepressant and anti-anhedonic effects of the antagonists and could potentially serve as a translational imaging phenotype for testing putative antidepressants, especially those targeting the NR2B receptor subtype.

Association between change in brain gray matter volume, cognition, and depression severity: Pre- and post- antidepressant pharmacotherapy for late-life depression

Late-life depression (LLD) is associated with cognitive impairments and reduced gray matter volume (GMV); however the mechanisms underlying this association are not well understood. The goal of this study was to characterize changes in depression severity, cognitive function, and brain structure associated with pharmacologic antidepressant treatment for LLD. We administered a detailed neurocognitive battery and conducted structural magnetic resonance imaging (MRI) on 26 individuals with LLD, pre-/post-a 12-week treatment trial with venlafaxine. After calculating changes in cognitive performance, GMV, and depression severity, we calculated Pearson's correlations, performed permutation testing, and false discovery rate correction. We found that loss of GMV over 12 weeks in the superior orbital frontal gyrus was associated with less improvement in depression severity and that increased GMV in the same was associated with greater improvement in depression severity. We detected no associations between changes in cognitive performance and improvements in either depressive symptoms or changes in GMV.

Alternations of White Matter Structural Networks in First Episode Untreated Major Depressive Disorder with Short Duration

It is crucial to explore the pathogenesis of major depressive disorder (MDD) at the early stage for the better diagnostic and treatment strategies. It was suggested that MDD might be involving in functional or structural alternations at the brain network level. However, at the onset of MDD, whether the whole brain white matter (WM) alterations at network level are already evident still remains unclear. In the present study, diffusion MRI scanning was adopt to depict the unique WM structural network topology across the entire brain at the early stage of MDD. Twenty-one first episode, short duration (<1 year) and drug-naïve depression patients, and 25 healthy control (HC) subjects were recruited. To construct the WM structural network, atlas-based brain regions were used for nodes, and the value of multiplying fiber number by the mean fractional anisotropy along the fiber bundles connected a pair of brain regions were used for edges. The structural network was analyzed by graph theoretic and network-based statistic methods. Pearson partial correlation analysis was also performed to evaluate their correlation with the clinical variables. Compared with HCs, the MDD patients had a significant decrease in the small-worldness (σ). Meanwhile, the MDD patients presented a significantly decreased subnetwork, which mainly involved in the frontal-subcortical and limbic regions. Our results suggested that the abnormal structural network of the orbitofrontal cortex and thalamus, involving the imbalance with the limbic system, might be a key pathology in early stage drug-naive depression. And the structural network analysis might be potential in early detection and diagnosis of MDD.