Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium

Gray matter volume and corresponding covariance connectivity are biomarkers for major depressive disorder

The major depressive disorder (MDD) is a common and severe mental disorder. To identify a reliable biomarker for MDD is important for early diagnosis and prevention. Given easy access and high reproducibility, the structural magnetic resonance imaging (sMRI) is an ideal method to identify the biomarker for depression. In this study, sMRI data of first episode, treatment-naïve 66 MDD patients and 54 sex-, age-, and education-matched healthy controls (HC) were used to identify the differences in gray matter volume (GMV), group-level, individual-level covariance connections. Finally, the abnormal GMV and individual covariance connections were applied to classify MDD from HC. MDD patients showed higher GMV in middle occipital gyrus (MOG) and precuneus (PCun), and higher structural covariance connections between MOG and PCun. In addition, the Allen Human Brain Atlas (AHBA) was applied and revealed the genetic basis for the changes of gray matter volume. Importantly, we reported that GMV in MOG, PCun and structural covariance connectivity between MOG and PCun are able to discriminate MDD from HC. Our results revealed structural underpinnings for MDD, which may contribute towards early discriminating for depression.

Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders

Dysfunction of brain resting-state functional networks has been widely reported in psychiatric disorders. However, the causal relationships between brain resting-state functional networks and psychiatric disorders remain largely unclear. Here we perform bidirectional two-sample Mendelian randomization (MR) analyses to investigate the causalities between 191 resting-state functional magnetic resonance imaging (rsfMRI) phenotypes (n = 34,691 individuals) and 12 psychiatric disorders (n = 14,307 to 698,672 individuals). Forward MR identified 8 rsfMRI phenotypes causally associated with the risk of psychiatric disorders. For example, the increase in the connectivity of motor, subcortical-cerebellum and limbic network was associated with lower risk of autism spectrum disorder. In adddition, increased connectivity in the default mode and central executive network was associated with lower risk of post-traumatic stress disorder and depression. Reverse MR analysis revealed significant associations between 4 psychiatric disorders and 6 rsfMRI phenotypes. For instance, the risk of attention-deficit/hyperactivity disorder increases the connectivity of the attention, salience, motor and subcortical-cerebellum network. The risk of schizophrenia mainly increases the connectivity of the default mode and central executive network and decreases the connectivity of the attention network. In summary, our findings reveal causal relationships between brain functional networks and psychiatric disorders, providing important interventional and therapeutic targets for psychiatric disorders at the brain functional network level.

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease

OBJECTIVE

Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI).

METHODS

We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population.

RESULTS

The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier.

INTERPRETATION

Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.

Neural correlates of aggression outcome expectation and their association with aggression: A voxel-based morphometry study

BACKGROUND

Aggression outcome expectation is an important cognitive factor of aggression. Discovering the neural mechanism of aggression outcome expectation is conducive to developing aggression research. However, the neural correlates underlying aggression outcome expectation and its effect remain elusive.

METHODS

We utilized voxel-based morphometry (VBM) to unravel the neural architecture of aggression outcome expectation measured by the Social Emotional Information Processing Assessment for Adults and its relationship with aggression measured by the Buss Perry Aggression Questionnaire in a sample of 185 university students (114 female; mean age = 19.94 ± 1.62 years; age range: 17-32 years).

RESULTS

We found a significantly positive correlation between aggression outcome expectation and the regional gray matter volume (GMV) in the right middle temporal gyrus (MTG) (x = 55.5, y = -58.5, z = 1.5; t = 3.35; cluster sizes = 352, p < 0.05, GRF corrected). Moreover, aggression outcome expectation acted as a mediator underlying the association between the right MTG volume and aggression.

CONCLUSIONS

These results revealed the neural correlates of aggression outcome expectation and its effect on aggression for the first time, which may contribute to our understanding of the cognitive neural mechanism of aggression and potentially identifying neurobiological markers for aggression.

Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing

Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using structured electronic medical record data, brain morphometry, and natural language processing. In total, 247 patients with a new depressive episode were included. TRD-predictive models were developed based on the combination of following parameters: selected tabular dataset features, independent components-map weightings from brain T1-weighted magnetic resonance imaging (MRI), and topic probabilities from clinical notes. All models applied the extreme gradient boosting (XGBoost) algorithm via five-fold cross-validation. The model using all data sources showed the highest area under the receiver operating characteristic of 0.794, followed by models that used combined brain MRI and structured data, brain MRI and clinical notes, clinical notes and structured data, brain MRI only, structured data only, and clinical notes only (0.770, 0.762, 0.728, 0.703, 0.684, and 0.569, respectively). Classifications of TRD were driven by several predictors, such as previous exposure to antidepressants and antihypertensive medications, sensorimotor network, default mode network, and somatic symptoms. Our findings suggest that a combination of clinical data with neuroimaging and natural language processing variables improves the prediction of TRD.

Gray matter abnormalities in patients with major depressive disorder and social anxiety disorder: a voxel-based meta-analysis

BACKGROUND

Major depressive and social anxiety disorders have a high comorbidity rate and similar cognitive patterns. However, their unique and shared neuroanatomical characteristics have not been fully identified.

METHODS

Voxel-based morphometric studies comparing gray matter volume between patients with major depressive disorder/social anxiety disorder and healthy controls were searched using 4 electronic databases from the inception to March 2022. Stereotactic data were extracted and subsequently tested for convergence and differences using activation likelihood estimation. In addition, based on the result of the meta-analysis, behavioral analysis was performed to assess the functional roles of the regions affected by major depressive disorder and/or social anxiety disorder.

RESULTS

In total, 34 studies on major depressive disorder with 2873 participants, and 10 studies on social anxiety disorder with 1004 subjects were included. Gray matter volume conjunction analysis showed that the right parahippocampal gyrus region, especially the amygdala, was smaller in patients compared to healthy controls. The contrast analysis of major depressive disorder and social anxiety disorder revealed lower gray matter volume in the right lentiform nucleus and medial frontal gyrus in social anxiety disorder and lower gray matter volume in the left parahippocampal gyrus in major depressive disorder. Behavioral analysis showed that regions with lower gray matter volume in social anxiety disorder are strongly associated with negative emotional processes.

CONCLUSIONS

The shared and unique patterns of gray matter volume abnormalities in patients with major depressive and social anxiety disorder may be linked to the underlying neuropathogenesis of these mental illnesses and provide potential biomarkers. PROSPERO registration number: CRD42021277546.

Alterations of brainstem volume in patients with first-episode and recurrent major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a prevalent mental health condition characterized by recurrent episodes in a substantial proportion of patients. The number of previous episodes is one of the most crucial predictors of depression recurrence. However, the underlying neural mechanisms remain unclear. To date, there have been limited neuroimaging studies investigating morphological changes of the brainstem in patients with first-episode MDD (FMDD) and recurrent MDD (RMDD). This study aimed to examine volumetric changes of individual brainstem regions in relation to the number of previous episodes and disease duration.

METHOD

A total of 111 individuals including 36 FMDD, 25 RMDD, and 50 healthy controls (HCs) underwent T1-weighted structural magnetic resonance imaging scans. A Bayesian segmentation algorithm was used to analyze the volume of each brainstem region, including the medulla oblongata, pons, midbrain, and superior cerebellar peduncle (SCP), as well as the whole brainstem volume. Analyses of variance (ANOVA) were performed to obtain brain regions with significant differences among three groups and then post hoc tests were calculated for inter-group comparisons. Partial correlation analyses were further conducted to identify associations between regional volumes and clinical features.

RESULTS

The ANOVA revealed significant brainstem volumetric differences among three groups in the pons, midbrain, SCP, and the whole brainstem (F = 3.996 ~ 5.886, adjusted p = 0.015 ~ 0.028). As compared with HCs, both groups of MDD patients showed decreased volumes in the pons as well as the entire brainstem (p = 0.002 ~ 0.034), however, only the FMDD group demonstrated a significantly reduced volume in the midbrain (p = 0.003). Specifically, the RMDD group exhibited significantly decreased SCP volume when comparing to both FMDD (p = 0.021) group and HCs (p = 0.008). Correlation analyses revealed that the SCP volumes were negatively associated with the number of depressive episodes (r=-0.36, p < 0.01) and illness duration (r=-0.28, p = 0.035) in patients with MDD.

CONCLUSION

The present findings provided evidence of decreased brainstem volume involving in the pathophysiology of MDD, particularly, volumetric reduction in the SCP might represent a neurobiological marker for RMDD. Further research is needed to confirm our observations and deepen our understanding of the neural mechanisms underlying depression recurrence.

Neural activity changes in first-episode, drug-naïve patients with major depressive disorder after transcutaneous auricular vagus nerve stimulation treatment: A resting-state fMRI study

Introduction

Major depressive disorder (MDD) is a disease with prominent individual, medical, and economic impacts. Drug therapy and other treatment methods (such as Electroconvulsive therapy) may induce treatment-resistance and have associated side effects including loss of memory, decrease of reaction time, and residual symptoms. Transcutaneous auricular vagus nerve stimulation (taVNS) is a novel and non-invasive treatment approach which stimulates brain structures with no side-effects. However, it remains little understood whether and how the neural activation is modulated by taVNS in MDD patients. Herein, we used the regional homogeneity (ReHo) to investigate the brain activity in first-episode, drug-naïve MDD patients after taVNS treatment.

Materials and methods

Twenty-two first-episode, drug-naïve MDD patients were enrolled in the study. These patients received the first taVNS treatment at the baseline time, and underwent resting-state MRI scanning twice, before and after taVNS. All the patients then received taVNS treatments for 4 weeks. The severity of depression was assessed by the 17-item Hamilton Depression Rating Scale (HAMD) at the baseline time and after 4-week’s treatment. Pearson analysis was used to assess the correlation between alterations of ReHo and changes of the HAMD scores. Two patients were excluded due to excessive head movement, two patients lack clinical data in the fourth week, thus, imaging analysis was performed in 20 patients, while correlation analysis between clinical and imaging data was performed in only 18 patients.

Results

There were significant differences in the ReHo values in first-episode, drug-naïve MDD patients between pre- or post- taVNS. The primary finding is that the patients exhibited a significantly lower ReHo in the left/right median cingulate cortex, the left precentral gyrus, the left postcentral gyrus, the right calcarine cortex, the left supplementary motor area, the left paracentral lobule, and the right lingual gyrus. Pearson analysis revealed a positive correlation between changes of ReHo in the right median cingulate cortex/the left supplementary motor area and changes of HAMD scores after taVNS.

Conclusion

The decreased ReHo were found after taVNS. The sensorimotor, limbic and visual-related brain regions may play an important role in understanding the underlying neural mechanisms and be the target brain regions in the further therapy.

Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing

OBJECTIVE

This study aims to construct and use natural language processing and other methods to analyze major depressive disorder (MDD) and radiology studies' publications in the PubMed database to understand the historical growth, current state, and potential expansion trend.

METHODS

All MDD radiology studies publications from January 2002 to January 2022 were downloaded from PubMed using R, a statistical computing language. R and the interpretive general-purpose programming language Python were used to extract publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analysis. The generative statistical algorithm "Latent Dirichlet allocation" (LDA) was applied to identify specific research focus and trends. The unsupervised Leuven algorithm was used to build a network to identify relationships between research focus.

RESULTS

A total of 5,566 publications on MDD and radiology research were identified, and there is a rapid upward trend. The top-cited publications were 11,042, and the highly-cited publications focused on improving diagnostic performance and establishing imaging standards. Publications came from 76 countries, with the most from research institutions in the United States and China. Hospitals and radiology departments take the lead in research and have an advantage. The extensive field of study contains 12,058 Medical Subject Heading (MeSH) terms. Based on the LDA algorithm, three areas were identified that have become the focus of research in recent years, "Symptoms and treatment," "Brain structure and imaging," and "Comorbidities research."

CONCLUSION

Latent Dirichlet allocation analysis methods can be well used to analyze many texts and discover recent research trends and focus. In the past 20 years, the research on MDD and radiology has focused on exploring MDD mechanisms, establishing standards, and constructing imaging methods. Recent research focuses are "Symptoms and sleep," "Brain structure study," and "functional connectivity." New progress may be made in studies on MDD complications and the combination of brain structure and metabolism.