Networks In Major Depressive Disorder Research Articles

Alterations in orbitofrontal cortex communication relate to suicidal attempts in patients with major depressive disorder

BackgroundInvestigating how the interaction between the orbitofrontal cortex (OFC) and various brain regions/functional networks in major depressive disorder (MDD) patients with a history of suicide attempt (SA) holds importance for understanding the neurobiology of this population. MethodsWe employed resting-state functional magnetic resonance imaging (rs-fMRI) to analyze the OFC's functional segregation in 586 healthy individuals. A network analysis framework was then applied to rs-fMRI data from 86 MDD-SA patients and 85 MDD-Control patients, utilizing seed mappings of OFC subregions and a multi-connectivity-indicator strategy involving cross-correlation, total interdependencies, Granger causality, and machine learning. ResultsFour functional subregions of left and right OFC, were designated as seed regions of interest. Relative to the MDD-Control group, the MDD-SA group exhibited enhanced functional connectivity (FC) and attenuated interaction between the OFC and the sensorimotor network, imbalanced communication between the OFC and the default mode network, enhanced FC and interaction between the OFC and the ventral attention network, enhanced interaction between the OFC and the salience network, and attenuated FC between the OFC and the frontoparietal network. LimitationsThe medication and treatment condition of patients with MDD was not controlled, so the medication effect on the alteration model cannot be affirmed. ConclusionThe findings suggest an imbalanced interaction pattern between the OFC subregions and a set of cognition- and emotion-related functional networks/regions in the MDD-SA group.

Disruptions in segregation mechanisms in fMRI-based brain functional network predict the major depressive disorder condition

This study investigates the functional brain network in major depressive disorder using network theory and a consensus network approach. At the macroscopic level, we found significant differences in connectivity measures such as node strength and clustering coefficient, with healthy controls exhibiting higher values. This is consistent with disruptions in functional brain network segregation in patients with major depressive disorder. Consensus network analysis revealed that the central executive and salience networks were predominant in healthy controls, whereas depressed patients showed greater overlap with the default mode network. No differences were found in network efficiency measures, indicating comparable brain network integration between healthy controls and major depressive disorder groups. Importantly, the clustering coefficient emerged as an effective diagnostic biomarker for depression, achieving high sensitivity (90%), specificity (92%), and overall precision (90%). Further analysis at the mesoscale level uncovered unique functional connections distinguishing healthy controls and major depressive disorder groups. Our findings underscore the utility of analyzing functional networks from the macroscale to the mesoscale, and provide insight into overcoming the challenges associated with intersubject variability and the multiple comparisons problem in network analysis.

Dysfunction in sensorimotor and default mode networks in major depressive disorder with insights from global brain connectivity

Dysfunction in sensorimotor and default mode networks in major depressive disorder with insights from global brain connectivity

Investigating changes of functional brain networks in major depressive disorder by graph theoretical analysis of resting-state fMRI

BackgroundMajor Depressive Disorder (MDD), as a chronic mental disorder, causes changes in mood, thoughts, and behavior. The pathophysiology of the disorder and its treatment are still unknown. One of the most notable changes observed in patients with MDD through fMRI is abnormal functional brain connectivity. MethodsPreprocessed data from 60 MDD patients and 60 normal controls (NCs) were selected, which has been performed using the DPARSF toolbox. The whole-brain functional networks and topologies were extracted using graph theory-based methods. A two-sample, two-tailed t-test was used to compare the topological features of functional brain networks between the MDD and NCs groups using the DPABI-Net/Statistical Analysis toolbox. ResultsThe obtained results showed a decrease in both global and local efficiency in MDD patients compared to NCs, and specifically, MDD patients showed significantly higher path length values. Acceptable p-values were obtained with a small sample size and less computational volume compared to the other studies on large datasets. At the node level, MDD patients showed decreased and relatively decreased node degrees in the sensorimotor network (SMN) and the dorsal attention network (DAN), respectively, as well as decreased node efficiency in the SMN, default mode network (DMN), and DAN. Also, MDD patients showed slightly decreased node efficiency in the visual networks (VN) and the ventral attention network (VAN), which were reported after FDR correction with Q < 0.05. LimitationsAll participants were Chinese. ConclusionsCollectively, increased path length, decreased global and local efficiency, and also decreased nodal degree and efficiency in the SMN, DAN, DAN, VN, and VAN were found in patients compared to NCs.

Multiple cognition associated multimodal brain networks in major depressive disorder.

Major depressive disorder frequently leads to cognitive impairments, significantly affecting patients' quality of life. However, the neurobiological mechanisms underlying cognitive deficits remain unclear. This study aimed to explore multimodal imaging biomarkers associated with cognitive function in major depressive disorder. Five cognitive scores (sustained attention, visual recognition memory, pattern recognition memory, executive function, and working memory) were used as references to guide the fusion of gray matter volume and amplitude of the low frequency fluctuation. Social function was assessed after 2yr. Linear regression analysis was performed to identify brain features that were associated with social function of patients with major depressive disorder. Finally, we included 131 major depressive disorder and 145 healthy controls. A multimodal frontal-insula-occipital network associated with sustained attention was found to be associated with social functioning in major depressive disorders. Analysis across different cognitive domains revealed that gray matter volume exhibited greater sensitivity to differences, while amplitude of the low frequency fluctuation consistently decreased in the right temporal-occipital-hippocampus circuit. The consistent functional changes across the 5 cognitive domains were related to symptom severity. Overall, these findings provide insights into biomarkers associated with multiple cognitive domains in major depressive disorder. These results may contribute to the development of effective treatment targeting cognitive deficits and social function.

Potential correlations between abnormal homogeneity of default mode network and personality or lipid level in major depressive disorder.

Default mode network (DMN) is one of the most recognized resting-state networks in major depressive disorder (MDD). However, the homogeneity of this network in MDD remains incompletely explored. Therefore, this study aims to determine whether there is abnormal network homogeneity (NH) of the DMN in MDD patients. At the same time, correlations between clinical variables and brain functional connectivity are examined. We enrolled 42 patients diagnosed with MDD and 42 HCs. A variety of clinical variables were collected, and data analysis was conducted using the NH and independent component analysis methods. The study shows that MDD patients have higher NH values in the left superior medial prefrontal cortex (MPFC) and left posterior cingulate cortex (PCC) compared to HCs. Additionally, there is a positive correlation between NH values of the left superior MPFC and Eysenck Personality Questionnaire values. NH values of the left PCC are positively linked to CHOL levels, LDL levels, and utilization scores. However, these correlations lose significance after the Bonferroni correction. Our findings indicate the presence of abnormal DMN homogeneity in MDD, underscoring the significance of DMN in the pathophysiology of MDD. Simultaneously, the study provides preliminary evidence for the correlation between clinical variables and brain functional connectivity.

Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set.

Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.

Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification.

Brain functional connectivity (FC) networks inferred from functional magnetic resonance imaging (fMRI) have shown altered or aberrant brain functional connectome in various neuropsychiatric disorders. Recent application of deep neural networks to connectome-based classification mostly relies on traditional convolutional neural networks (CNNs) using input FCs on a regular Euclidean grid to learn spatial maps of brain networks neglecting the topological information of the brain networks, leading to potentially sub-optimal performance in brain disorder identification. We propose a novel graph deep learning framework that leverages non-Euclidean information inherent in the graph structure for classifying brain networks in major depressive disorder (MDD). We introduce a novel graph autoencoder (GAE) architecture, built upon graph convolutional networks (GCNs), to embed the topological structure and node content of large fMRI networks into low-dimensional representations. For constructing the brain networks, we employ the Ledoit-Wolf (LDW) shrinkage method to efficiently estimate high-dimensional FC metrics from fMRI data. We explore both supervised and unsupervised techniques for graph embedding learning. The resulting embeddings serve as feature inputs for a deep fully-connected neural network (FCNN) to distinguish MDD from healthy controls (HCs). Evaluating our model on resting-state fMRI MDD dataset, we observe that the GAE-FCNN outperforms several state-of-the-art methods for brain connectome classification, achieving the highest accuracy when using LDW-FC edges as node features. The graph embeddings of fMRI FC networks also reveal significant group differences between MDD and HCs. Our framework demonstrates the feasibility of learning graph embeddings from brain networks, providing valuable discriminative information for diagnosing brain disorders.

Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis.

Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.

Effects of escitalopram therapy on effective connectivity among core brain networks in major depressive disorder

BackgroundPatients with major depressive disorder (MDD) have abnormal functional interaction among large-scale brain networks, indicated by aberrant effective connectivity of the default mode network (DMN), salience network (SN), and dorsal attention network (DAN). However, it remains unclear whether antidepressants can normalize the altered effective connectivity in MDD. MethodsIn this study, we collected resting-state functional magnetic resonance imaging data from 46 unmedicated patients with MDD at baseline and after 12 weeks of escitalopram treatment. We also collected data from 58 healthy controls (HCs) at the same time point with the same interval. Using spectral dynamic causal modeling and parametric empirical Bayes, we examined group differences, time effect and their interaction on the casual interactions among the regions of interest in the three networks. ResultsCompared with HCs, patients with MDD showed increased positive (excitatory) connections within the DMN, decreased positive connections within the SN and DAN, decreased absolute value of negative (inhibitory) connectivity from the SN and DAN to the DMN, and decreased positive connections between the DAN and the SN. Furthermore, we found that six connections related to the DAN showed decreased group differences in effective connectivity between MDD and HCs during follow-up compared to the baseline. ConclusionsOur findings suggest that escitalopram therapy can partly improve the disrupted effective connectivity among high-order brain functional networks in MDD. These findings deepened our understanding of the neural basis of antidepressants' effect on brain function in patients with MDD.

Identification and discovery of imaging genetic patterns using fusion self-expressive network in major depressive disorder

Identification and discovery of imaging genetic patterns using fusion self-expressive network in major depressive disorder

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

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. 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. 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. 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.

Frequency specificity of aberrant triple networks in major depressive disorder: a resting-state effective connectivity study.

Major depressive disorder (MDD) has been associated with aberrant effective connectivity (EC) among the default mode network (DMN), salience network (SN), and central executive network (CEN)-collectively referred to as triple networks. However, prior research has predominantly concentrated on broad frequency bands (0.01-0.08 Hz or 0.01-0.15 Hz), ignoring the influence of distinct rhythms on triple network causal dynamics. In the present study, we aim to investigate EC alterations within the triple networks across various frequency bands in patients with MDD. Utilizing a data-driven frequency decomposition approach and a multivariate Granger causality analysis, we characterized frequency-specific EC patterns of triple networks in 49 MDD patients and 54 healthy controls. A support vector machine classifier was subsequently employed to assess the discriminative capacity of the frequency-specific EC features. Our findings revealed that, compared to controls, patients exhibited not only enhanced mean EC within the CEN in the conventional frequency band (0.01-0.08 Hz), but also decreased mean EC from the SN to the DMN in a higher frequency band (0.12-0.18 Hz), and increased mean EC from the CEN to the SN in a sub-frequency band (0.04-0.08 Hz); the latter was significantly correlated with disease severity. Moreover, optimal classification performance for distinguishing patients from controls was attained by combining EC features across all three frequency bands, with the area under the curve (AUC) value of 0.8831 and the corresponding accuracy, sensitivity, and specificity of 89.97%, 92.63%, and 87.32%, respectively. These insights into EC changes within the triple networks across multiple frequency bands offer valuable perspectives on the neurobiological basis of MDD and could aid in developing frequency-specific EC features as potential biomarkers for disease diagnosis.

Effect of electroconvulsive therapy on brain functional network in major depressive disorder

Electroconvulsive therapy (ECT) is an interventional technique capable of highly effective neuromodulation in major depressive disorder (MDD), but its antidepressant mechanism remains unclear. By recording the resting-state electroencephalogram (RS-EEG) of 19 MDD patients before and after ECT, we analyzed the modulation effect of ECT on the resting-state brain functional network of MDD patients from multiple perspectives: estimating spontaneous EEG activity power spectral density (PSD) using Welch algorithm; constructing brain functional network based on imaginary part coherence (iCoh) and calculate functional connectivity; using minimum spanning tree theory to explore the topological characteristics of brain functional network. The results show that PSD, functional connectivity, and topology in multiple frequency bands were significantly changed after ECT in MDD patients. The results of this study reveal that ECT changes the brain activity of MDD patients, which provides an important reference in the clinical treatment and mechanism analysis of MDD.

Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder

Depression (major depressive disorder, MDD) is a common and serious medical illness. Globally, it is estimated that 5% of adults suffer from depression. Recently, imaging genetics receives growing attention and become a powerful strategy for discoverying the associations between genetic variants (e.g., single-nucleotide polymorphisms, SNPs) and multi-modality brain imaging data. However, most of the existing MDD imaging genetic research studies conducted by clinicians usually utilize simple statistical analysis methods and only consider single-modality brain imaging, which are limited in the deeper discovery of the mechanistic understanding of MDD. It is therefore imperative to utilize a powerful and efficient technology to fully explore associations between genetic variants and multi-modality brain imaging. In this study, we developed a novel imaging genetic association framework to mine the multi-modality phenotype network between genetic risk variants and multi-stage diagnosis status. Specifically, the multi-modality phenotype network consists of voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI). Thereafter, an association model based on multi-task learning strategy was adopted to fully explore the relationship between the MDD risk SNP and the multi-modality phenotype network. The multi-stage diagnosis status was introduced to further mine the relation among the multiple modalities of different subjects. A multi-modality brain imaging data and genotype data were collected by us from two hospitals. The experimental results not only demonstrate the effectiveness of our proposed method but also identify some consistent and stable brain regions of interest (ROIs) biomarkers from the node and edge features of multi-modality phenotype network. Moreover, four new and potential risk SNPs associated with MDD were discovered.

Identification and characterization of noncoding RNAs-associated competing endogenous RNA networks in major depressive disorder.

Major depressive disorder (MDD) is a common and serious mental illness. Many novel genes in MDD have been characterized by high-throughput methods such as microarrays or sequencing. Recently, noncoding RNAs (ncRNAs) were suggested to be involved in the complicated environmental-genetic regulatory network of MDD occurrence; however, the interplay among RNA species, including protein-coding RNAs and ncRNAs, in MDD remains unclear. To investigate the RNA expression datasets downloaded from a public database and construct a network based on differentially expressed long noncoding RNA (lncRNAs), microRNAs (miRNAs), and mRNAs between MDD and controls. Gene expression data were searched in NCBI Gene Expression Omnibus using the search term "major depressive disorder." Six array datasets from humans were related to the search term: GSE19738, GSE32280, GSE38206, GSE52790, GSE76826, and GSE81152. These datasets were processed for initial assessment and subjected to quality control and differential expression analysis. Differentially expressed lncRNAs, miRNAs, and mRNAs were determined, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed, and protein-protein interaction network was generated. The results were analyzed for their association with MDD. After analysis, 3 miRNAs, 12 lncRNAs, and 33 mRNAs were identified in the competing endogenous RNA network. Two of these miRNAs were earlier shown to be involved in psychiatric disorders, and differentially expressed mRNAs were found to be highly enriched in pathways related to neurogenesis and neuroplasticity as per Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The expression of hub gene fatty acid 2-hydroxylase was enriched, and the encoded protein was found to be involved in myelin formation, indicating that neurological development and signal transduction are involved in MDD pathogenesis. The present study presents candidate ncRNAs involved in the neurogenesis and neuroplasticity pathways related to MDD.

Dynamic functional connectivity of the central autonomic network in major depressive disorder

Dynamic functional connectivity of the central autonomic network in major depressive disorder

Disrupted effective connectivity of the default, salience and dorsal attention networks in major depressive disorder: a study using spectral dynamic causal modelling of resting-state fMRI

<h3>Background:</h3> Understanding the neural basis for major depressive disorder (MDD) is essential for its diagnosis and treatment. Aberrant activation and functional connectivity of the default mode network (DMN), salience network (SN) and dorsal attention network (DAN) have been found consistently in patients with MDD. However, whether effective connectivity within and between these networks is altered in MDD remains unknown. The primary objective of this study was to investigate the effective connectivity of the 3 networks in patients with MDD at rest. <h3>Methods:</h3> We included 63 patients with MDD (35 first-episode and 28 recurrent) and 74 healthy controls, and collected resting-state functional MRI data for all participants. We defined 15 regions of interest from the 3 functional brain networks of interest using group independent component analysis. We estimated the coupling parameters that reflected the causal interactions among these regions using spectral dynamic causal modelling. We used parametric empirical Bayes to determine commonalities across groups, differences between patients with MDD and healthy controls, and differences between patients with recurrent and first-episode MDD. <h3>Results:</h3> We found positive (excitatory) connections within each network, negative (inhibitory) connections from the SN and DAN to the DMN, and positive connections from the DAN to the SN across groups. Compared to healthy controls, patients with MDD showed increased positive connections within the DMN, a decreased absolute value of negative connectivity from the SN to the DMN, and increased positive connections from the SN to the DAN. We also found that patients with recurrent MDD showed remarkably different effective connections compared to patients with first-episode MDD, especially related to the DAN. <h3>Limitations:</h3> Because of the relatively small sample size and the unclear medication history of the MDD sample, the present findings are in need of replication. <h3>Conclusion:</h3> These findings suggest that effective connectivity among high-order brain functional networks during rest was disrupted in patients with MDD. Moreover, patients with recurrent MDD exhibited different effective connections compared to patients with first-episode MDD. These differences in effective connectivity might provide new insights into the neural substrates of MDD.

Neural effects of childhood maltreatment on dynamic large-scale brain networks in major depressive disorder

Emerging evidence suggests that childhood maltreatment (CM) alters trajectories of brain development to affect network architecture and is a risk factor for the development and maintenance of depression. The current study aimed to explore the association between CM and depressive severity on the large-scale resting-state networks (RSNs) level in major depressive disorder (MDD) patients and explored the network basis of clinical symptoms. 42 healthy controls without childhood maltreatment, 13 healthy controls with CM, 35 MDD without CM and 50 MDD with CM were included in the study population. Group differences in ten large-scale RSNs, associations between CM and depressive symptom dimensions and network variables were tested. And we explored whether symptom-related networks might discriminate between the four groups. We found one-versus-all-others-network showed an inverted U-shaped curve across groups. Network variables were significantly associated with Hamilton Depression Scale subscores and Childhood Trauma Questionnaire subscores. Different symptoms showed different imaging patterns, and overlapping connections of patterns had better ability to distinguish groups. Our findings suggest that CM could lead to significant changes in both network measures and connections in healthy individuals and MDD. These results deepen our understanding of the neuroimaging mechanisms of CM and MDD.

TU-169. Effects of rTMS on pain-related brain networks in major depressive disorder with physical pain

TU-169. Effects of rTMS on pain-related brain networks in major depressive disorder with physical pain