Topological Properties of Brain Structural Networks Represent Early Predictive Characteristics for the Occurrence of Bipolar Disorder in Patients With Major Depressive Disorder: A 7-Year Prospective Longitudinal Study

Structural Brain Connectivity and Treatment Improvement in Mood Disorder

Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions, adverse events, and lost therapeutic opportunities. Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the patients with MD went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved (NI). First, the threshold-free network-based statistics (NBS) was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free NBS revealed impaired connections involving regions of the basal ganglia in patients with MD compared with HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula, and amygdala in the MD group. Compared with the NI, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe, and rostral anterior cingulate. Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network, and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.

Gamma band VMPFC-PreCG.L connection variation after the onset of negative emotional stimuli can predict mania in depressive patients

OBJECTIVE

Because of the similar clinical symptoms, it is difficult to distinguish unipolar disorder (UD) from bipolar disorder (BD) in the depressive episode using the available clinical features, especially for those who meet the diagnostic criteria of UD, however, experience the manic episode during the follow-up (tBD).

METHODS

Magnetoencephalography recordings during a sad expression recognition task were obtained from 81 patients (27 BD, 24 tBD, 30 UD) and 26 healthy controls (HCs). Source analysis was applied to localize 64 regions of interest in the low gamma band (30-50 Hz). Regional functional connections (FCs) were constructed respectively within three time periods (early: 0-200 ms, middle: 200-400 ms, and post: 400-600 ms). The network-based statistic method was used to explore the abnormal connection patterns in tBD compared to UD and HC. BD was applied to explore whether such abnormality is still significant between every two groups of BD, tBD, UD, and HC.

RESULTS

The VMPFC-PreCG.L connection was found to be a significantly different connection between tBD and UD in the early time period and between tBD and BD in the middle time period. Furthermore, the middle/early time period ratio of FC value of VMPFC-PreCG.L connection was negatively correlated with the bipolarity index in tBD.

CONCLUSIONS

The VMPFC-PreCG.L connection in different time periods after the onset of sad facial stimuli may be a potential biomarker to distinguish the different states of BD. The FC ratio of VMPFC-PreCG.L connection may predict whether patients with depressive episodes subsequently develop mania.

Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

BACKGROUND

Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects.

METHODS

This study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory-based network analysis was used to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices.

RESULTS

Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate-corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis.

CONCLUSIONS

We found shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results showed a compromised brain communication across a spectrum of major psychiatric disorders.

Shared and specific characteristics of regional cerebral blood flow and functional connectivity in unmedicated bipolar and major depressive disorders

BACKGROUND

Identifying brain similarities and differences between bipolar disorder (BD) and major depressive disorder (MDD) can help us better understand their pathophysiological mechanisms and develop more effective treatments. However, the features of whole-brain regional cerebral blood flow (CBF) and intrinsic functional connectivity (FC) underlying BD and MDD have not been directly compared.

METHODS

Eighty-eight unmedicated BD II depression patients, 95 unmedicated MDD patients, and 96 healthy controls (HCs) underwent three-dimensional arterial spin labeling (3D ASL) and resting-state functional MRI (rs-fMRI). The functional properties of whole brain CBF and seed-based resting-state FC further performed based on those regions with changed CBF were analyzed between the three groups.

RESULTS

The patients with BD and MDD showed commonly increased CBF in the left posterior lobe of the cerebellum and the left middle temporal gyrus (MTG) compared with HCs. The CBF of the left MTG was positively associated with 24-items Hamilton Depression Rating Scale scores in MDD patients. Decreased FC between the left posterior lobe of the cerebellum and the left inferior frontal gyrus (IFG) was observed only in patients with BD compared with HCs.

CONCLUSION

Patients with BD and those with MDD shared common features of CBF in the posterior lobe of the cerebellum and the MTG. The altered posterior lobe of the cerebellum-IFG FC can be considered as a potential biomarker for the differentiation of patients with BD from those with MDD.

Graph theory approach for the structural-functional brain connectome of depression

To decipher the organizational styles of neural underpinning in major depressive disorder (MDD), the current article reviewed recent neuroimaging studies (published during 2015-2020) that applied graph theory approach to the diffusion tensor imaging data or functional brain activation data acquired during task-free resting state. The global network organization of resting-state functional connectivity network in MDD were diverse according to the onset age and medication status. Intra-modular functional connections were weaker in MDD compared to healthy controls (HC) for default mode and limbic networks. Weaker local graph metrics of default mode, frontoparietal, and salience network components in MDD compared to HC were also found. On the contrary, brain regions comprising the limbic, sensorimotor, and subcortical networks showed higher local graph metrics in MDD compared to HC. For the brain white matter-based structural connectivity network, the global network organization was comparable to HC in adult MDD but was attenuated in late-life depression. Local graph metrics of limbic, salience, default-mode, subcortical, insular, and frontoparietal network components in structural connectome were affected from the severity of depressive symptoms, burden of perceived stress, and treatment effects. Collectively, the current review illustrated changed global network organization of structural and functional brain connectomes in MDD compared to HC and were varied according to the onset age and medication status. Intra-modular functional connectivity within the default mode and limbic networks were weaker in MDD compared to HC. Local graph metrics of structural connectome for MDD reflected severity of depressive symptom and perceived stress, and were also changed after treatments. Further studies that explore the graph metrics-based neural correlates of clinical features, cognitive styles, treatment response and prognosis in MDD are required.

The integrated understanding of structural and functional connectomes in depression: A multimodal meta-analysis of graph metrics

BACKGROUND

From the perspective of information processing, an integrated understanding of the structural and functional connectomes in depression patients is important, a multimodal meta-analysis is required to detect the robust alterations in graph metrics across studies.

METHODS

Following a systematic search, 952 depression patients and 1447 controls in nine diffusion magnetic resonance imaging (dMRI) and twelve rest state functional MRI (rs-fMRI) studies with high methodological quality met the inclusion criteria and were included in the meta-analysis.

RESULTS

Regarding the dMRI results, no significant differences of meta-analytic metrics were found; regarding the rs-fMRI results, the modularity and local efficiency were found to be significantly lower in the depression group than in the controls (Hedge's g = -0.330 and -0.349, respectively).

CONCLUSION

Our findings suggested a lower modularity and network efficiency in the rs-fMRI network in depression patients, indicating that the pathological imbalances in brain connectomes needs further exploration.

LIMITATIONS

Included number of trials was low and heterogeneity should be noted.

White Matter-Based Structural Brain Network of Major Depression

Major depressive disorder (MDD) is frequently characterized as a disorder of the disconnection syndrome. Diffusion tensor imaging (DTI) has played a critical role in supporting this view, with much investigation providing a large amount of evidence of structural connectivity abnormalities in the disorder. Recent research on the human connectome combined neuroimaging techniques with graph theoretic methods to highlight the disrupted topological properties of large-scale structural brain networks under depression, involving global metrics (e.g., global and local efficiencies), and local nodal properties (e.g., degree and betweenness), as well as other related metrics, including a modular structure, assortativity, and (rich) hubs. Here, we review the studies of white matter networks in the case of MDD with the application of these techniques, focusing principally on the consistent findings and the clinical significance of DTI-based network research, while discussing the key methodological issues that frequently arise in the field. The already published literature shows that MDD is associated with a widespread structural connectivity deficit. Topological alteration of structural brain networks in the case of MDD points to decreased overall connectivity strength and reduced global efficiency as well as decreased small-worldness and network resilience. These structural connectivity disturbances entail potential functional consequences, although the relationship between the two is very sophisticated and requires further investigation. In summary, the present study comprehensively maps the structural connectomic disturbances in patients with MDD across the entire brain, which adds important weight to the view suggesting connectivity abnormalities of this disorder and highlights the potential of network properties as diagnostic biomarkers in the psychoradiology field. Several common methodological issues of the study of DTI-based networks are discussed, involving sample heterogeneity and fiber crossing problems and the tractography algorithms. Finally, suggestions for future perspectives, including imaging multimodality, a longitudinal study and computational connectomics, in the further study of white matter networks under depression are given. Surmounting these challenges and advancing the research methods will be required to surpass the simple mapping of connectivity changes to illuminate the underlying psychiatric pathological mechanism.

Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder

Major depressive disorder (MDD) is associated to affected brain wiring. Little is known whether these changes are stable over time and hence might represent a biological predisposition, or whether these are state markers of current disease severity and recovery after a depressive episode. Human white matter network ("connectome") analysis via network science is a suitable tool to investigate the association between affected brain connectivity and MDD. This study examines structural connectome topology in 464 MDD patients (mean age: 36.6 years) and 432 healthy controls (35.6 years). MDD patients were stratified categorially by current disease status (acute vs. partial remission vs. full remission) based on DSM-IV criteria. Current symptom severity was assessed continuously via the Hamilton Depression Rating Scale (HAMD). Connectome matrices were created via a combination of T1-weighted magnetic resonance imaging (MRI) and tractography methods based on diffusion-weighted imaging. Global tract-based metrics were not found to show significant differences between disease status groups, suggesting conserved global brain connectivity in MDD. In contrast, reduced global fractional anisotropy (FA) was observed specifically in acute depressed patients compared to fully remitted patients and healthy controls. Within the MDD patients, FA in a subnetwork including frontal, temporal, insular, and parietal nodes was negatively associated with HAMD, an effect remaining when correcting for lifetime disease severity. Therefore, our findings provide new evidence of MDD to be associated with structural, yet dynamic, state-dependent connectome alterations, which covary with current disease severity and remission status after a depressive episode.

Disrupted Structural Brain Network Organization Behind Depressive Symptoms in Major Depressive Disorder

Major depressive disorder (MDD) is a severe and devastating condition. However, the anatomical basis behind the affective symptoms, cognitive symptoms, and somatic-vegetative symptoms of MDD is still unknown. To explore the mechanism behind the depressive symptoms in MDD, we used diffusion tensor imaging (DTI)-based structural brain connectivity analysis to investigate the network difference between MDD patients and healthy controls (CN), and to explore the association between network metrics and patients' clinical symptoms. Twenty-six patients with MDD and 25 CN were included. A baseline 24-item Hamilton rating scale for depression (HAMD-24) score ≥ 21 and seven factors (anxiety/somatization, weight loss, cognitive disturbance, diurnal variation, retardation, sleep disturbance, hopelessness) scores were assessed. When compared with healthy subjects, significantly higher characteristic path length and clustering coefficient as well as significantly lower network efficiencies were observed in patients with MDD. Furthermore, MDD patients demonstrated significantly lower nodal degree and nodal efficiency in multiple brain regions including superior frontal gyrus (SFG), supplementary motor area (SMA), calcarine fissure, middle temporal gyrus (MTG), and inferior temporal gyrus (ITG). We also found that the characteristic path length of MDD patients was associated with weight loss. Moreover, significantly lower global efficiency of MDD patients was correlated with higher total HAMD score, anxiety somatization, and cognitive disturbance. The nodal degree in SFG was also found to be negatively associated with disease duration. In conclusion, our results demonstrated that MDD patients had impaired structural network features compared to CN, and disruption of optimal network architecture might be the mechanism behind the depressive symptoms and emotion disturbance in MDD patients.

Neurobiological commonalities and distinctions among 3 major psychiatric disorders: a graph theoretical analysis of the structural connectome

BACKGROUND

White matter network alterations have increasingly been implicated in major depressive disorder, bipolar disorder and schizophrenia. The aim of this study was to identify shared and distinct white matter network alterations among the 3 disorders.

METHODS

We used analysis of covariance, with age and gender as covariates, to investigate white matter network alterations in 123 patients with schizophrenia, 123 with bipolar disorder, 124 with major depressive disorder and 209 healthy controls.

RESULTS

We found significant group differences in global network efficiency (F = 3.386, p = 0.018), nodal efficiency (F = 8.015, p < 0.001 corrected for false discovery rate [FDR]) and nodal degree (F = 5.971, pFDR < 0.001) in the left middle occipital gyrus, as well as nodal efficiency (F = 6.930, pFDR < 0.001) and nodal degree (F = 5.884, pFDR < 0.001) in the left postcentral gyrus. We found no significant alterations in patients with major depressive disorder. Post hoc analyses revealed that compared with healthy controls, patients in the schizophrenia and bipolar disorder groups showed decreased global network efficiency, nodal efficiency and nodal degree in the left middle occipital gyrus. Furthermore, patients in the schizophrenia group showed decreased nodal efficiency and nodal degree in the left postcentral gyrus compared with healthy controls.

LIMITATIONS

Our findings could have been confounded in part by treatment differences.

CONCLUSION

Our findings implicate graded white matter network alterations across the 3 disorders, enhancing our understanding of shared and distinct pathophysiological mechanisms across diagnoses and providing vital insights into neuroimaging-based methods for diagnosis and research.