Structural and functional features of treatment-resistant depression: A systematic review and exploratory coordinate-based meta-analysis of neuroimaging studies

Shared genetic architecture of cortical thickness alterations in major depressive disorder and schizophrenia

BACKGROUND

Major depressive disorder (MDD) and schizophrenia (SCZ) are heritable brain disorders characterized by alterations in cortical thickness. However, the shared genetic basis for cortical thickness changes in these disorders remains unclear.

METHODS

We conducted a systematic literature search on cortical thickness in MDD and SCZ through PubMed and Web of Science. A coordinate-based meta-analysis was performed to identify cortical thickness changes. Additionally, utilizing summary statistics from the largest genome-wide association studies for depression (Ncase = 268,615, Ncontrol = 667,123) and SCZ (Ncase = 53,386, Ncontrol = 77,258), we explored shared genomic loci using conjunctional false discovery rate (conjFDR) analysis. Transcriptome-neuroimaging association analysis was then employed to identify shared genes associated with cortical thickness alterations, and enrichment analysis was finally carried out to elucidate the biological significance of these genes.

RESULTS

Our search yielded 34 MDD (Ncase = 1621, Ncontrol = 1507) and 19 SCZ (Ncase = 1170, Ncontrol = 1043) neuroimaging studies for cortical thickness meta-analysis. Specific alterations in the left supplementary motor area were observed in MDD, while SCZ exhibited widespread reductions in various brain regions, particularly in the frontal and temporal areas. The conjFDR approach identified 357 genomic loci jointly associated with MDD and SCZ. Within these loci, 55 genes were found to be associated with cortical thickness alterations in both disorders. Enrichment analysis revealed their involvement in nervous system development, apoptosis, and cell communication.

CONCLUSION

This study revealed the shared genetic architecture underlying cortical thickness alterations in MDD and SCZ, providing insights into common neurobiological pathways. The identified genes and pathways may serve as potential transdiagnostic markers, informing precision medicine approaches in psychiatric care.

Thalamocortical Dysconnectivity in Treatment-Resistant Depression

Thalamocortical connectivity is associated with cognitive and affective processing. The role of thalamocortical connectivity in the pathomechanism of treatment-resistant depression (TRD) remains unclear. This study included 48 patients with TRD and 48 healthy individuals. We investigated thalamocortical connectivity by performing resting-state functional MRI with the bilateral thalamus as the seed. In addition, patients with TRD were evaluated using the Montgomery-Åsberg Depression Rating Scale (MADRS). Compared with the healthy individuals, the patients with TRD exhibited increased functional connectivity (FC) of the thalamus with the insula and superior temporal cortex and reduced the FC of the thalamus with the anterior paracingulate cortex and cerebellum crus II. Our study may support the crucial role of thalamocortical dysconnectivity in the TRD pathomechanism. However, the small sample size may limit the statistical power. A future study with a large sample size of patients with TRD would be required to validate our findings.

Effect of Xiaoyaosan on brain volume and microstructure diffusion changes to exert antidepressant-like effects in mice with chronic social defeat stress

Objective

Depression is a prevalent mental disorder characterized by persistent negative mood and loss of pleasure. Although there are various treatment modalities available for depression, the rates of response and remission remain low. Xiaoyaosan (XYS), a traditional Chinese herbal formula with a long history of use in treating depression, has shown promising effects. However, the underlying mechanism of its therapeutic action remains elusive. The aim of this study is to investigate the neuroimaging changes in the brain associated with the antidepressant-like effects of XYS.

Methods

Here, we combined voxel-based morphometry of T2-weighted images and voxel-based analysis on diffusion tensor images to evaluate alterations in brain morphometry and microstructure between chronic social defeat stress (CSDS) model mice and control mice. Additionally, we examined the effect of XYS treatment on structural disruptions in the brains of XYS-treated mice. Furthermore, we explored the therapeutic effect of 18β-glycyrrhetinic acid (18β-GA), which was identified as the primary compound present in the brain following administration of XYS. Significant differences in brain structure were utilized as classification features for distinguishing mice with depression model form the controls using a machine learning method.

Results

Significant changes in brain volume and diffusion metrics were observed in the CSDS model mice, primarily concentrated in the nucleus accumbens (ACB), primary somatosensory area (SSP), thalamus (TH), hypothalamus (HY), basomedical amygdala nucleus (BMA), caudoputamen (CP), and retrosplenial area (RSP). However, both XYS and 18β-GA treatment prevented disruptions in brain volume and diffusion metrics in certain regions, including bilateral HY, right SSP, right ACB, bilateral CP, and left TH. The classification models based on each type of neuroimaging feature achieved high accuracy levels (gray matter volume: 76.39%, AUC=0.83; white matter volume: 76.39%, AUC=0.92; fractional anisotropy: 82.64%, AUC=0.9; radial diffusivity: 76.39%, AUC=0.82). Among these machine learning analyses, the right ACB, right HY, and right CP were identified as the most important brain regions for classification purposes.

Conclusion

These findings suggested that XYS can prevent abnormal changes in brain volume and microstructure within TH, SSP, ACB, and CP to exert prophylactic antidepressant-like effects in CSDS model mice. The neuroimaging features within these regions demonstrate excellent performance for classifying CSDS model mice from controls while providing valuable insights into the antidepressant effects of XYS.

Similarities and differences between post-traumatic stress disorder and major depressive disorder: Evidence from task-evoked functional magnetic resonance imaging meta-analysis

BACKGROUND

Post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) are psychiatric disorders that can present with overlapping symptoms and shared risk factors. However, the extent to which these disorders share common underlying neuropathological mechanisms remains unclear. To investigate the similarities and differences in task-evoked brain activation patterns between patients with PTSD and MDD.

METHODS

A coordinate-based meta-analysis was conducted across 35 PTSD studies (564 patients and 543 healthy controls) and 125 MDD studies (4049 patients and 4170 healthy controls) using anisotropic effect-size signed differential mapping software.

RESULTS

Both PTSD and MDD patients exhibited increased neural activation in the bilateral inferior frontal gyrus. However, PTSD patients showed increased neural activation in the right insula, left supplementary motor area extending to median cingulate gyrus and superior frontal gyrus (SFG), and left fusiform gyrus, and decreased neural activation in the right posterior cingulate gyrus, right middle temporal gyrus, right paracentral lobule, and right inferior parietal gyrus relative to MDD patients.

CONCLUSION

Our meta-analysis suggests that PTSD and MDD share some similar patterns of brain activation, but also have distinct neural signatures. These findings contribute to our understanding of the potential neuropathology underlying these disorders and may inform the development of more targeted and effective treatment and intervention strategies. Moreover, these results may provide useful neuroimaging targets for the differential diagnosis of MDD and PTSD.

Elucidating genetic and molecular basis of altered higher-order brain structure-function coupling in major depressive disorder

Previous studies have shown that major depressive disorder (MDD) patients exhibit structural and functional impairments, but few studies have investigated changes in higher-order coupling between structure and function. Here, we systematically investigated the effect of MDD on higher-order coupling between structural connectivity (SC) and functional connectivity (FC). Each brain region was mapped into embedding vector by the node2vec algorithm. We used support vector machine (SVM) with the brain region embedding vector to distinguish MDD patients from health controls (HCs) and identify the most discriminative brain regions. Our study revealed that MDD patients had decreased higher-order coupling in connections between the most discriminative brain regions and local connections in rich-club organization and increased higher-order coupling in connections between the ventral attentional network and limbic network compared with HCs. Interestingly, transcriptome-neuroimaging association analysis demonstrated the correlations between regional rSC-FC coupling variations between MDD patients and HCs and α/β-hydrolase domain-containing 6 (ABHD6), β 1,3-N-acetylglucosaminyltransferase-9(β3GNT9), transmembrane protein 45B (TMEM45B), the correlation between regional dSC-FC coupling variations and retinoic acid early transcript 1E antisense RNA 1(RAET1E-AS1), and the correlations between regional iSC-FC coupling variations and ABHD6, β3GNT9, katanin-like 2 protein (KATNAL2). In addition, correlation analysis with neurotransmitter receptor/transporter maps found that the rSC-FC and iSC-FC coupling variations were both correlated with neuroendocrine transporter (NET) expression, and the dSC-FC coupling variations were correlated with metabotropic glutamate receptor 5 (mGluR5). Further mediation analysis explored the relationship between genes, neurotransmitter receptor/transporter and MDD related higher-order coupling variations. These findings indicate that specific genetic and molecular factors underpin the observed disparities in higher-order SC-FC coupling between MDD patients and HCs. Our study confirmed that higher-order coupling between SC and FC plays an important role in diagnosing MDD. The identification of new biological evidence for MDD etiology holds promise for the development of innovative antidepressant therapies.

Status and trends of TMS research in depressive disorder: a bibliometric and visual analysis

Background

Depression is a chronic psychiatric condition that places significant burdens on individuals, families, and societies. The rapid evolution of non-invasive brain stimulation techniques has facilitated the extensive clinical use of Transcranial Magnetic Stimulation (TMS) for depression treatment. In light of the substantial recent increase in related research, this study aims to employ bibliometric methods to systematically review the global research status and trends of TMS in depression, providing a reference and guiding future studies in this field.

Methods

We retrieved literature on TMS and depression published between 1999 and 2023 from the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) databases within the Web of Science Core Collection (WoSCC). Bibliometric analysis was performed using VOSviewer and CiteSpace software to analyze data on countries, institutions, authors, journals, keywords, citations, and to generate visual maps.

Results

A total of 5,046 publications were extracted covering the period from 1999 to 2023 in the field of TMS and depression. The publication output exhibited an overall exponential growth trend. These articles were published across 804 different journals, BRAIN STIMULATION is the platform that receives the most articles in this area. The literature involved contributions from over 16,000 authors affiliated with 4,573 institutions across 77 countries. The United States contributed the largest number of publications, with the University of Toronto and Daskalakis ZJ leading as the most prolific institution and author, respectively. Keywords such as "Default Mode Network," "Functional Connectivity," and "Theta Burst" have recently garnered significant attention. Research in this field primarily focuses on TMS stimulation patterns, their therapeutic efficacy and safety, brain region and network mechanisms under combined brain imaging technologies, and the modulation effects of TMS on brain-derived neurotrophic factor (BDNF) and neurotransmitter levels.

Conclusion

In recent years, TMS therapy has demonstrated extensive potential applications and significant implications for the treatment of depression. Research in the field of TMS for depression has achieved notable progress. Particularly, the development of novel TMS stimulation patterns and the integration of TMS therapy with multimodal techniques and machine learning algorithms for precision treatment and investigation of brain network mechanisms have emerged as current research hotspots.

Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance

An estimated 30 % of Major Depressive Disorder (MDD) patients exhibit resistance to conventional antidepressant treatments. Identifying reliable biomarkers of treatment-resistant depression (TRD) represents a major goal of precision psychiatry, which is hampered by the clinical and biological heterogeneity. To uncover biologically-driven subtypes of MDD, we applied an unsupervised data-driven framework to stratify 102 MDD patients on their neuroimaging signature, including extracted measures of cortical thickness, grey matter volumes, and white matter fractional anisotropy. Our novel analytical pipeline integrated different machine learning algorithms to harmonize data, perform data dimensionality reduction, and provide a stability-based relative clustering validation. The obtained clusters were characterized for immune-inflammatory peripheral biomarkers, TRD, history of childhood trauma and depressive symptoms. Our results indicated two different clusters of patients, differentiable with 67 % of accuracy: one cluster (n = 59) was associated with a higher proportion of TRD, and higher scores of energy-related depressive symptoms, history of childhood abuse and emotional neglect; this cluster showed a widespread reduction in cortical thickness (d = 0.43-1.80) and volumes (d = 0.45-1.05), along with fractional anisotropy in the fronto-occipital fasciculus, stria terminalis, and corpus callosum (d = 0.46-0.52); the second cluster (n = 43) was associated with cognitive and affective depressive symptoms, thicker cortices and wider volumes. Multivariate analyses revealed distinct brain-inflammation relationships between the two clusters, with increase in pro-inflammatory markers being associated with decreased cortical thickness and volumes. Our stratification of MDD patients based on structural neuroimaging identified clinically-relevant subgroups of MDD with specific symptomatic and immune-inflammatory profiles, which can contribute to the development of tailored personalized interventions for MDD.

Causal relationship between resting-state networks and depression: a bidirectional two-sample mendelian randomization study

BACKGROUND

Cerebral resting-state networks were suggested to be strongly associated with depressive disorders. However, the causal relationship between cerebral networks and depressive disorders remains controversial. In this study, we aimed to investigate the effect of resting-state networks on depressive disorders using a bidirectional Mendelian randomization (MR) design.

METHODS

Updated summary-level genome-wide association study (GWAS) data correlated with resting-state networks were obtained from a meta-analysis of European-descent GWAS from the Complex Trait Genetics Lab. Depression-related GWAS data were obtained from the FinnGen study involving participants with European ancestry. Resting-state functional magnetic resonance imaging and multiband diffusion imaging of the brain were performed to measure functional and structural connectivity in seven well-known networks. Inverse-variance weighting was used as the primary estimate, whereas the MR-Pleiotropy RESidual Sum and Outliers (PRESSO), MR-Egger, and weighted median were used to detect heterogeneity, sensitivity, and pleiotropy.

RESULTS

In total, 20,928 functional and 20,573 structural connectivity data as well as depression-related GWAS data from 48,847 patients and 225,483 controls were analyzed. Evidence for a causal effect of the structural limbic network on depressive disorders was found in the inverse variance-weighted limbic network (odds ratio, [Formula: see text]; 95% confidence interval, [Formula: see text]; [Formula: see text]), whereas the causal effect of depressive disorders on SC LN was not found(OR=1.0025; CI,1.0005-1.0046; P=0.012). No significant associations between functional connectivity of the resting-state networks and depressive disorders were found in this MR study.

CONCLUSIONS

These results suggest that genetically determined structural connectivity of the limbic network has a causal effect on depressive disorders and may play a critical role in its neuropathology.

Structural and functional brain alterations in subthreshold depression: A multimodal coordinate-based meta-analysis

Imaging studies of subthreshold depression (StD) have reported structural and functional abnormalities in a variety of spatially diverse brain regions. However, there is no consensus among different studies. In the present study, we applied a multimodal meta-analytic approach, the Activation Likelihood Estimation (ALE), to test the hypothesis that StD exhibits spatially convergent structural and functional brain abnormalities compared to healthy controls. A total of 31 articles with 25 experiments were included, collectively representing 1001 subjects with StD. We found consistent differences between StD and healthy controls mainly in the left insula across studies with various neuroimaging methods. Further exploratory analyses found structural atrophy and decreased functional activities in the right pallidum and thalamus in StD, and abnormal spontaneous activity converged to the middle frontal gyrus. Coordinate-based meta-analysis found spatially convergent structural and functional impairments in StD. These findings provide novel insights for understanding the neural underpinnings of subthreshold depression and enlighten the potential targets for its early screening and therapeutic interventions in the future.

Unlocking Therapeutic Synergy: Tailoring Drugs for Comorbidities such as Depression and Diabetes through Identical Molecular Targets in Different Cell Types

Research in the field of pharmacology aims to generate new treatments for pathologies. Nowadays, there are an increased number of chronic disorders that severely and durably handicap many patients. Among the most widespread pathologies, obesity, which is often associated with diabetes, is constantly increasing in incidence, and in parallel, neurodegenerative and mood disorders are increasingly affecting many people. For years, these pathologies have been so frequently observed in the population in a concomitant way that they are considered as comorbidities. In fact, common mechanisms are certainly at work in the etiology of these pathologies. The main purpose of this review is to show the value of anticipating the effect of baseline treatment of a condition on its comorbidity in order to obtain concomitant positive actions. One of the implications would be that by understanding and targeting shared molecular mechanisms underlying these conditions, it may be possible to tailor drugs that address both simultaneously. To this end, we firstly remind readers of the close link existing between depression and diabetes and secondly address the potential benefit of the pleiotropic actions of two major active molecules used to treat central and peripheral disorders, first a serotonin reuptake inhibitor (Prozac ®) and then GLP-1R agonists. In the second part, by discussing the therapeutic potential of new experimental antidepressant molecules, we will support the concept that a better understanding of the intracellular signaling pathways targeted by pharmacological agents could lead to future synergistic treatments targeting solely positive effects for comorbidities.