Structural brain networks in remitted psychotic depression

Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods

Posttraumatic stress disorder (PTSD) is associated with lower cortical thickness (CT) in prefrontal, cingulate, and insular cortices in diverse trauma-affected samples. However, some studies have failed to detect differences between PTSD patients and healthy controls or reported that PTSD is associated with greater CT. Using data-driven dimensionality reduction, we sought to conduct a well-powered study to identify vulnerable networks without regard to neuroanatomic boundaries. Moreover, this approach enabled us to avoid the excessive burden of multiple comparison correction that plagues vertex-wise methods. We derived structural covariance networks (SCNs) by applying non-negative matrix factorization (NMF) to CT data from 961 PTSD patients and 1124 trauma-exposed controls without PTSD. We used regression analyses to investigate associations between CT within SCNs and PTSD diagnosis (with and without accounting for the potential confounding effect of trauma type) and symptom severity in the full sample. We performed additional regression analyses in subsets of the data to examine associations between SCNs and comorbid depression, childhood trauma severity, and alcohol abuse. NMF identified 20 unbiased SCNs, which aligned closely with functionally defined brain networks. PTSD diagnosis was most strongly associated with diminished CT in SCNs that encompassed the bilateral superior frontal cortex, motor cortex, insular cortex, orbitofrontal cortex, medial occipital cortex, anterior cingulate cortex, and posterior cingulate cortex. CT in these networks was significantly negatively correlated with PTSD symptom severity. Collectively, these findings suggest that PTSD diagnosis is associated with widespread reductions in CT, particularly within prefrontal regulatory regions and broader emotion and sensory processing cortical regions.

Brain metabolite levels in remitted psychotic depression with consideration of effects of antipsychotic medication

BACKGROUND

The neurobiology of psychotic depression is not well understood and can be confounded by antipsychotics. Magnetic resonance spectroscopy (MRS) is an ideal tool to measure brain metabolites non-invasively. We cross-sectionally assessed brain metabolites in patients with remitted psychotic depression and controls. We also longitudinally assessed the effects of olanzapine versus placebo on brain metabolites.

METHODS

Following remission, patients with psychotic depression were randomized to continue sertraline + olanzapine (n = 15) or switched to sertraline + placebo (n = 18), at which point they completed an MRS scan. Patients completed a second scan either 36 weeks later, relapse, or discontinuation. Where water-scaled metabolite levels were obtained and a Point-RESolved Spectroscopy sequence was utilized, choline, myo-inositol, glutamate + glutamine (Glx), N-acetylaspartate, and creatine were measured in the left dorsolateral prefrontal cortex (L-DLPFC) and dorsal anterior cingulate cortex (dACC). An ANCOVA was used to compare metabolites between patients (n = 40) and controls (n = 46). A linear mixed-model was used to compare olanzapine versus placebo groups.

RESULTS

Cross-sectionally, patients (compared to controls) had higher myo-inositol (standardized mean difference [SMD] = 0.84; 95%CI = 0.25-1.44; p = 0.005) in the dACC but not different Glx, choline, N-acetylaspartate, and creatine. Longitudinally, patients randomized to placebo (compared to olanzapine) showed a significantly greater change with a reduction of creatine (SMD = 1.51; 95%CI = 0.71-2.31; p = 0.0002) in the dACC but not glutamate + glutamine, choline, myo-inositol, and N-acetylaspartate.

CONCLUSIONS

Patients with remitted psychotic depression have higher myo-inositol than controls. Olanzapine may maintain creatine levels. Future studies are needed to further disentangle the mechanisms of action of olanzapine.

Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome

22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.

Dynamic network characteristics of adolescents with major depressive disorder: Attention network mediates the association between anhedonia and attentional deficit

Attention deficit is a critical symptom that impairs social functioning in adolescents with major depressive disorder (MDD). In this study, we aimed to explore the dynamic neural network activity associated with attention deficits and its relationship with clinical outcomes in adolescents with MDD. We included 188 adolescents with MDD and 94 healthy controls. By combining psychophysics, resting-state electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) techniques, we aimed to identify dynamic network features through the investigation of EEG microstate characteristics and related temporal network features in adolescents with MDD. At baseline, microstate analysis revealed that the occurrence of Microstate C in the patient group was lower than that in healthy controls, whereas the duration and coverage of Microstate D increased in the MDD group. Mediation analysis revealed that the probability of transition from Microstate C to D mediated anhedonia and attention deficits in the MDD group. fMRI results showed that the temporal variability of the dorsal attention network (DAN) was significantly weaker in patients with MDD than in healthy controls. Importantly, the temporal variability of DAN mediated the relationship between anhedonia and attention deficits in the patient group. After acute-stage treatment, the response prediction group (RP) showed improvement in Microstates C and D compared to the nonresponse prediction group (NRP). For resting-state fMRI data, the temporal variability of DAN was significantly higher in the RP group than in the NRP group. Overall, this study enriches our understanding of the neural mechanisms underlying attention deficits in patients with MDD and provides novel clinical biomarkers.

Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes

Introduction

In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54).

Methods

We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups.

Results

As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups.

Discussion

Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.

Structural brain networks in schizophrenia based on nonnegative matrix factorization

Schizophrenia is a severe mental disease with significant morphometric reductions in gray matter volume and cortical thickness in a variety of brain regions. However, most studies only focused on the voxel level alterations in specific cerebral regions and ignored the spatial relationship between voxels. In the present study, we used a novel, data-driven technique-nonnegative matrix factorization (NMF) to group voxels with similar information into a network, and studied the structural covariance at the network level in schizophrenia. Our sample included 36 patients with schizophrenia and 21 healthy controls. Compared with healthy controls, patients with schizophrenia showed significant gray matter volume reductions in six structural covariance networks (dorsal striatum, thalamus, hippocampus-parahippocampus, supplementary motor area-fusiform, middle/inferior temporal network, frontal-parietal-occipital network). Our findings confirmed the assumption of a disturbance in the cortical-subcortical circuit in schizophrenia and suggested that NMF is a useful multivariate method to identify brain networks, which provides a new perspective to study the neural mechanism in schizophrenia.

Resolving heterogeneity in depression using individualized structural covariance network analysis

BACKGROUND

Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis.

METHODS

T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges.

RESULTS

As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms.

CONCLUSIONS

In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

Altered topology of individual brain structural covariance networks in major depressive disorder

BACKGROUND

The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks.

METHODS

We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics.

RESULTS

Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex.

CONCLUSIONS

The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.

Scalable Orthonormal Projective NMF via Diversified Stochastic Optimization

The increasing availability of large-scale neuroimaging initiatives opens exciting opportunities for discovery science of human brain structure and function. Data-driven techniques, such as Orthonormal Projective Non-negative Matrix Factorization (opNMF), are well positioned to explore multivariate relationships in big data towards uncovering brain organization. opNMF enjoys advantageous interpretability and reproducibility compared to commonly used matrix factorization methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), which led to its wide adoption in clinical computational neuroscience. However, applying opNMF in large-scale cohort studies is hindered by its limited scalability caused by its accompanying computational complexity. In this work, we address the computational challenges of opNMF using a stochastic optimization approach that learns over mini-batches of the data. Additionally, we diversify the stochastic batches via repulsive point processes, which reduce redundancy in the mini-batches and in turn lead to lower variance in the updates. We validated our framework on gray matter tissue density maps estimated from 1000 subjects part of the Open Access Series of Imaging (OASIS) dataset. We demonstrated that operations over mini-batches of data yield significant reduction in computational cost. Importantly, we showed that our novel optimization does not compromise the accuracy or interpretability of factors when compared to standard opNMF. The proposed model enables new investigations of brain structure using big neuroimaging data that could improve our understanding of brain structure in health and disease.

Exploring the influence of functional architecture on cortical thickness networks in early psychosis - a longitudinal study

Cortical thickness reductions differ between individuals with psychotic disorders and comparison subjects even in early stages of illness. Whether these reductions covary as expected by functional network membership or simply by spatial proximity has not been fully elucidated. Through orthonormal projective non-negative matrix factorization, cortical thickness measurements in functionally-annotated regions from MRI scans of early-stage psychosis and matched healthy controls were reduced in dimensionality into features capturing positive covariance. Rather than matching the functional networks, the covarying regions in each feature displayed a more localized spatial organization. With Bayesian belief networks, the covarying regions per feature were arranged into a network topology to visualize the dependency structure and identify key driving regions. The features demonstrated diagnosis-specific differences in cortical thickness distributions per feature, identifying reduction-vulnerable spatial regions. Differences in key cortical thickness features between psychosis and control groups were delineated, as well as those between affective and non-affective psychosis. Clustering of the participants, stratified by diagnosis and clinical variables, characterized the clinical traits that define the cortical thickness patterns. Longitudinal follow-up revealed that in select clusters with low baseline cortical thickness, clinical traits improved over time. Our study represents a novel effort to characterize brain structure in relation to functional networks in healthy and clinical populations and to map patterns of cortical thickness alterations among ESP patients onto clinical variables for a better understanding of brain pathophysiology.

Scalable NMF via linearly optimized data compression

Orthonormal projective non-negative matrix factorization (opNMF) has been widely used in neuroimaging and clinical neuroscience applications to derive representations of the brain in health and disease. The non-negativity and orthonormality constraints of opNMF result in intuitive and well-localized factors. However, the advantages of opNMF come at a steep computational cost that prohibits its use in large-scale data. In this work, we propose novel and scalable optimization schemes for orthonormal projective non-negative matrix factorization that enable the use of the method in large-scale neuroimaging settings. We replace the high-dimensional data matrix with its corresponding singular value decomposition (SVD) and QR decompositions and combine the decompositions with opNMF multiplicative update algorithm. Empirical validation of the proposed methods demonstrated significant speed-up in computation time while keeping memory consumption low without compromising the accuracy of the solution.

ROLE OF GUT MICROBIOTA IN DEPRESSION: UNDERSTANDING MOLECULAR PATHWAYS, RECENT RESEARCH, AND FUTURE DIRECTION

Gut microbiota, also known as the "second brain" in humans because of the regulatory role it has on the central nervous system via neuronal, chemical and immune pathways. It has been proven that there exists a bidirectional communication between the gut and the brain. Increasing evidence supports that this crosstalk is linked to the etiology and treatment of depression. Reports suggest that the gut microbiota control the host epigenetic machinery in depression and gut dysbiosis causes negative epigenetic modifications via mechanisms like histone acetylation, DNA methylation and non-coding RNA mediated gene inhibition. The gut microbiome can be a promising approach for the management of depression. The diet and dietary metabolites like kynurenine, tryptophan, and propionic acid also greatly influence the microbiome composition and thereby, the physiological activities. This review gives a bird-eye view on the pathological updates and currently used treatment approaches targeting the gut microbiota in depression.

Aberrant Topological Patterns of Structural Covariance Networks in Cognitively Normal Elderly Adults With Mild Behavioral Impairment

Mild behavioral impairment (MBI), characterized by the late-life onset of sustained and meaningful neuropsychiatric symptoms, is increasingly recognized as a prodromal stage of dementia. However, the underlying neural mechanisms of MBI remain unclear. Here, we examined alterations in the topological organization of the structural covariance networks of patients with MBI (N = 32) compared with normal controls (N = 38). We found that the gray matter structural covariance networks of both the patients with MBI and controls exhibited a small-world topology evidenced by sigma value larger than one. The patients with MBI had significantly decreased clustering coefficients at several network densities and local efficiency at densities ranging from 0.05 to 0.26, indicating decreased local segregation. No significant differences in the characteristic path length, gamma value, sigma value, or global efficiency were detected. Locally, the patients with MBI showed significantly decreased nodal betweenness centrality in the left middle frontal gyrus, right inferior frontal gyrus (opercular part), and left Heschl gyrus and increased betweenness centrality in the left gyrus rectus, right insula, bilateral precuneus, and left thalamus. Moreover, the difference in the bilateral precuneus survived after correcting for multiple comparisons. In addition, a different number and distribution of hubs was identified in patients with MBI, showing more paralimbic hubs than observed in the normal controls. In conclusion, we revealed abnormal topological patterns of the structural covariance networks in patients with MBI and offer new insights into the network dysfunctional mechanisms of MBI.

Neural Substrates of Psychotic Depression: Findings From the Global ECT-MRI Research Collaboration

Psychotic major depression (PMD) is hypothesized to be a distinct clinical entity from nonpsychotic major depression (NPMD). However, neurobiological evidence supporting this notion is scarce. The aim of this study is to identify gray matter volume (GMV) differences between PMD and NPMD and their longitudinal change following electroconvulsive therapy (ECT). Structural magnetic resonance imaging (MRI) data from 8 independent sites in the Global ECT-MRI Research Collaboration (GEMRIC) database (n = 108; 56 PMD and 52 NPMD; mean age 71.7 in PMD and 70.2 in NPMD) were analyzed. All participants underwent MRI before and after ECT. First, cross-sectional whole-brain voxel-wise GMV comparisons between PMD and NPMD were conducted at both time points. Second, in a flexible factorial model, a main effect of time and a group-by-time interaction were examined to identify longitudinal effects of ECT on GMV and longitudinal differential effects of ECT between PMD and NPMD, respectively. Compared with NPMD, PMD showed lower GMV in the prefrontal, temporal and parietal cortex before ECT; PMD showed lower GMV in the medial prefrontal cortex (MPFC) after ECT. Although there was a significant main effect of time on GMV in several brain regions in both PMD and NPMD, there was no significant group-by-time interaction. Lower GMV in the MPFC was consistently identified in PMD, suggesting this may be a trait-like neural substrate of PMD. Longitudinal effect of ECT on GMV may not explain superior ECT response in PMD, and further investigation is needed.

Abnormalities in Electroencephalographic Microstates Among Adolescents With First Episode Major Depressive Disorder

Background: Recent studies have reported changes in the electroencephalograms (EEG) of patients with major depressive disorder (MDD). However, little research has explored EEG differences between adolescents with MDD and healthy controls, particularly EEG microstates differences. The aim of the current study was to characterize EEG microstate activity in adolescents with MDD and healthy controls (HCs). Methods: A total of 35 adolescents with MDD and 35 HCs were recruited in this study. The depressive symptoms were assessed by Hamilton Depression Scale (HAMD) and Children's Depression Inventory (CDI), and the anxiety symptoms were assessed by Chinese version of DSM-5 Level 2-Anxiety-Child scale. A 64-channel EEG was recorded for 5 min (eye closed, resting-state) and analyzed using microstate analysis. Microstate properties were compared between groups and correlated with patients' depression scores. Results: We found increased occurrence and contribution of microstate B in MDD patients compared to HCs, and decreased occurrence and contribution of microstate D in MDD patients compared to HCs. While no significant correlation between depression severity (HAMD score) and the microstate metrics (occurrence and contribution of microstate B and D) differing between MDD adolescents and HCs was found. Conclusions: Adolescents with MDD showed microstate B and microstate D changes. The obtained results may deepen our understanding of dynamic EEG changes among adolescents with MDD and provide some evidence of changes in brain development in adolescents with MDD.