Depression, neuroimaging and connectomics: a selective overview

Resting-state dynamic functional connectivity in major depressive disorder: A systematic review

As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.

Longitudinal association between depressive symptoms and cognitive function: the neurological mechanism of psychological and physical disturbances on memory

BACKGROUND

The neural correlates underlying late-life depressive symptoms and cognitive deterioration are largely unclear, and little is known about the role of chronic physical conditions in such association. This research explores both concurrent and longitudinal associations between late-life depressive symptoms and cognitive functions, with examining the neural substrate and chronic vascular diseases (CVDs) in these associations.

METHODS

A total of 4109 participants (mean age = 65.4, 63.0% females) were evaluated for cognitive functions through various neuropsychological assessments. Depressive symptoms were assessed by the Geriatric Depression Scale and CVDs were self-reported. T1-weighted magnetic resonance imaging (MRI), diffusion tensor imaging, and functional MRI (fMRI) data were acquired in a subsample (n = 791).

RESULTS

Cognitively, higher depressive symptoms were correlated with poor performance across all cognitive domains, with the strongest association with episodic memory (r = ‒0.138, p < 0.001). Regarding brain structure, depressive symptoms were negatively correlated with thalamic volume and white matter integrity. Further, white matter integrity was found to mediate the longitudinal association between depressive symptoms and episodic memory (indirect effect = -0.017, 95% CI -0.045 to -0.002) and this mediation was only significant for those with severe CVDs (β = -0.177, p = 0.008).

CONCLUSIONS

This study is one of the first to provide neural evidence elucidating the longitudinal associations between late-life depressive symptoms and cognitive dysfunction. Additionally, the severity of CVDs strengthened these associations, which enlightens the potential of managing CVDs as an intervention target for preventing depressive symptoms-related cognitive decline.

Contribution of resting-state functional connectivity of the subgenual anterior cingulate to prediction of antidepressant efficacy in patients with major depressive disorder

This study investigated how resting-state functional connectivity (rsFC) of the subgenual anterior cingulate cortex (sgACC) predicts antidepressant response in patients with major depressive disorder (MDD). Eighty-seven medication-free MDD patients underwent baseline resting-state functional MRI scans. After 12 weeks of escitalopram treatment, patients were classified into remission depression (RD, n = 42) and nonremission depression (NRD, n = 45) groups. We conducted two analyses: a voxel-wise rsFC analysis using sgACC as a seed to identify group differences, and a prediction model based on the sgACC rsFC map to predict treatment efficacy. Haufe transformation was used to interpret the predictive rsFC features. The RD group showed significantly higher rsFC between the sgACC and regions in the fronto-parietal network (FPN), including the bilateral dorsolateral prefrontal cortex (DLPFC) and bilateral inferior parietal lobule (IPL), compared to the NRD group. These sgACC rsFC measures correlated positively with symptom improvement. Baseline sgACC rsFC also significantly predicted treatment response after 12 weeks, with a mean accuracy of 72.64% (p < 0.001), mean area under the curve of 0.74 (p < 0.001), mean specificity of 0.82, and mean sensitivity of 0.70 in 10-fold cross-validation. The predictive voxels were mainly within the FPN. The rsFC between the sgACC and FPN is a valuable predictor of antidepressant response in MDD patients. These findings enhance our understanding of the neurobiological mechanisms underlying treatment response and could help inform personalized treatment strategies for MDD.

Altered sleep onset transition in depression: Evidence from EEG activity and EEG functional connectivity analyses

OBJECTIVE

Sleep disorders constitute a principal diagnostic criterion for depression, potentially reflecting the abnormal persistence of brain activity during the sleep onset (SO) transition. Here, we sought to explore the differences in the dynamic changes in the EEG activity and the EEG functional connectivity (FC) during the SO transition in depressed patients.

METHODS

Overnight polysomnography recordings were obtained from thirty-two depressed patients and thirty-three healthy controls. The multiscale permutation entropy (MSPE) and EEG relative power were extracted to characterize EEG activity, and weighted phase lag index (WPLI) was calculated to characterize EEG FC.

RESULTS

The intergroup differences in EEG activity of relative power and MSPE were reversed near SO, which attributed to slower rates of change among depressed patients. Regarding the characteristics of the EEG FC network, depressed patients exhibited significantly higher inter-hemispheric and interregional WPLI values in both delta and alpha bands throughout the SO transition, concomitant with different dynamic properties in the delta band FC. During the process after SO, patients exhibited increased inter-hemispheric long-range links, whereas controls showed more intra-hemispheric ones. Finally, we found significant correlations in the dynamic changes between different EEG measures.

CONCLUSIONS

Our research revealed that the abnormal changes during the SO transition in depressed patients were manifested in both homeostatic and dynamic aspects, which were reflected in EEG FC and EEG activity, respectively.

SIGNIFICANCE

These findings may elucidate the mechanism underlying sleep disorders in depression from the perspective of neural activity.

Functional connectivity of the default mode network in first-episode drug-naïve patients with major depressive disorder

BACKGROUND

Alterations in the default mode network (DMN) have been reported in major depressive disorder (MDD), well-replicated robust alterations of functional connectivity (FC) of DMN remain to be established. Investigating the functional connections of DMN at the overall and subsystem level in early MDD patients has the potential to advance our understanding of the physiopathology of this disorder.

METHODS

We recruited 115 first-episode drug-naïve patients with MDD and 137 demographic-matched healthy controls (HCs). We first compared FC within the DMN, within/between the DMN subsystems, and from DMN subsystems to the whole brain between groups. Subsequently, we explored correlations between clinical features and identified alterations in FC.

RESULTS

First-episode drug-naïve patients with MDD showed significantly increased FC within the DMN, dorsal DMN and medial DMN. Each subsystem showed a distinct FC pattern with other brain networks. Increased FC between the subsystems (core DMN, dorsal DMN) and other networks was associated with more severe depressive symptoms, while medial DMN-related connectivity correlated with memory performance.

LIMITATIONS

The relatively large "pure" MDD sample could only be generalized to a limited population. And, atypical asymmetric FCs in the DMN related to MDD might be missed for only left-lateralized ROIs were used to avoid strong correlations between mirrored (right/left) seed regions.

CONCLUSION

These findings suggest patients with early MDD showed distinct patterns of FC alterations throughout DMN and its subsystems, which were related to illness severity and illness-associated cognitive impairment, highlighting their clinical significance.

Effectiveness of acupuncture plus music therapy for post-stroke depression: Systematic review and meta-analysis

BACKGROUND

Post-stroke depression (PSD) is a prevalent complication of stroke that adversely affects patient outcomes. The etiology of PSD is complex, and no universally effective treatment exists. Acupuncture, with its historical use, combined with music therapy, presents a novel approach for PSD treatment. This study aims to systematically evaluate the clinical efficacy of combining acupuncture with music therapy for PSD through a meta-analysis.

METHODS

We systematically searched both Chinese and English literature in PubMed, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang, and the Chinese Science and Technology Periodical Database (VIP Database) for randomized controlled trials evaluating acupuncture combined with music therapy for PSD. Two independent evaluators conducted quality assessments and data extraction. Statistical analyses were performed using RevMan 5.4 and Stata 18.0 software.

RESULTS

This article contains 11 studies, involving a total of 698 patients. The results of the meta-analysis showed that, compared with the control group, the test group showed significant improvement on multiple outcome measures: HAMD score [mean difference (MD) = -3.18, 95% confidence interval (CI) (-3.61, -2.76), P < .00001], Self-Rating Depression Scale score [MD = -5.12, 95% CI (-6.61, -3.63), P < .00001], Pittsburgh sleep quality index score [MD = -2.40, 95% CI (-2.96, -1.84), P < .00001], BI score [MD = 14.16, 95% CI (4.37, 23.94), P = .005] were all significantly lower, significantly higher effectiveness [risk ratio = 1.21, 95% CI (1.11, 1.33), P < .0001]. These differences were also statistically significant.

CONCLUSION

The use of acupuncture combined with music therapy is effective in reducing depression in PSD patients.

Individual large-scale functional network mapping for major depressive disorder with electroconvulsive therapy

Personalized functional connectivity mapping has been demonstrated to be promising in identifying underlying neurophysiological basis for brain disorders and treatment effects. Electroconvulsive therapy (ECT) has been proved to be an effective treatment for major depressive disorder (MDD) while its active mechanisms remain unclear. Here, 46 MDD patients before and after ECT as well as 46 demographically matched healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. A spatially regularized form of non-negative matrix factorization (NMF) was used to accurately identify functional networks (FNs) in individuals to map individual-level static and dynamic functional network connectivity (FNC) to reveal the underlying neurophysiological basis of therepetical effects of ECT for MDD. Moreover, these static and dynamic FNCs were used as features to predict the clinical treatment outcomes for MDD patients. We found that ECT could modulate both static and dynamic large-scale FNCs at individual level in MDD patients, and dynamic FNCs were closely associated with depression and anxiety symptoms. Importantly, we found that individual FNCs, particularly the individual dynamic FNCs could better predict the treatment outcomes of ECT suggesting that dynamic functional connectivity analysis may be better to link brain functional characteristics with clinical symptoms and treatment outcomes. Taken together, our findings provide new evidence for the active mechanisms and biomarkers for ECT to improve diagnostic accuracy and to guide individual treatment selection for MDD patients.

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.

Association between segmental alterations of white matter bundles and cognitive performance in first-episode, treatment-naïve young adults with major depressive disorder

BACKGROUND

Cumulative evidence has consistently shown that white matter (WM) disruption is associated with cognitive decline in geriatric depression. However, limited research has been conducted on the correlation between these lesions and cognitive performance in untreated young adults with major depressive disorder (MDD), particularly with the specific segmental alterations of the fibers.

METHOD

Diffusion tensor images were performed on 60 first-episode, treatment-naïve young adult patients with MDD and 54 matched healthy controls (HCs). Automated fiber quantification was applied to calculate the tract profiles of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) to evaluate the WM microstructural organization. Correlation analysis was performed to find the associations between the diffusion properties and cognitive performance.

RESULTS

Compared with HCs, patients with MDD exhibited predominantly different diffusion properties in bilateral uncinate fasciculus (UF), corticospinal tracts (CSTs), left superior longitudinal fasciculus and anterior thalamic radiation. The FA of the temporal cortex portion of right UF was positively correlated with working memory. The MD of the temporal component of left UF was negatively correlated with working memory and positively correlated with symptom severity. Additionally, a positive correlation between the MD of left CST and the psychomotor speed, negative correlation between the MD of left CST and the executive functions and complex attentional processes were observed.

CONCLUSIONS

Our study validated the alterations in spatial localization of WM microstructure and its correlations with cognitive performance in first-episode, treatment-naïve young adults with MDD. This study added to the knowledge of the neuropathological basis of MDD.

Changes of brain functional network in Alzheimer's disease and frontotemporal dementia: a graph-theoretic analysis

BACKGROUND

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar clinical features that challenge accurate diagnosis. Despite extensive research, the underlying pathophysiological mechanisms remain unclear, and effective treatments are limited. This study aims to investigate the alterations in brain network connectivity associated with AD and FTD to enhance our understanding of their pathophysiology and establish a scientific foundation for their diagnosis and treatment.

METHODS

We analyzed preprocessed electroencephalogram (EEG) data from the OpenNeuro public dataset, comprising 36 patients with AD, 23 patients with FTD, and 29 healthy controls (HC). Participants were in a resting state with eyes closed. We estimated the average functional connectivity using the Phase Lag Index (PLI) for lower frequencies (delta and theta) and the Amplitude Envelope Correlation with leakage correction (AEC-c) for higher frequencies (alpha, beta, and gamma). Graph theory was applied to calculate topological parameters, including mean node degree, clustering coefficient, characteristic path length, global and local efficiency. A permutation test was then utilized to assess changes in brain network connectivity in AD and FTD based on these parameters.

RESULTS

Both AD and FTD patients showed increased mean PLI values in the theta frequency band, along with increases in average node degree, clustering coefficient, global efficiency, and local efficiency. Conversely, mean AEC-c values in the alpha frequency band were notably diminished, which was accompanied by decreases average node degree, clustering coefficient, global efficiency, and local efficiency. Furthermore, AD patients in the occipital region showed an increase in theta band node degree and decreased alpha band clustering coefficient and local efficiency, a pattern not observed in FTD.

CONCLUSIONS

Our findings reveal distinct abnormalities in the functional network topology and connectivity in AD and FTD, which may contribute to a better understanding of the pathophysiological mechanisms of these diseases. Specifically, patients with AD demonstrated a more widespread change in functional connectivity, while those with FTD retained connectivity in the occipital lobe. These observations could provide valuable insights for developing electrophysiological markers to differentiate between the two diseases.

Structure-function coupling and hierarchy-specific antidepressant response in major depressive disorder

BACKGROUND

Extensive research has explored altered structural and functional networks in major depressive disorder (MDD). However, studies examining the relationships between structure and function yielded heterogeneous and inconclusive results. Recent work has suggested that the structure-function relationship is not uniform throughout the brain but varies across different levels of functional hierarchy. This study aims to investigate changes in structure-function couplings (SFC) and their relevance to antidepressant response in MDD from a functional hierarchical perspective.

METHODS

We compared regional SFC between individuals with MDD (n = 258) and healthy controls (HC, n = 99) using resting-state functional magnetic resonance imaging and diffusion tensor imaging. We also compared antidepressant non-responders (n = 55) and responders (n = 68, defined by a reduction in depressive severity of >50%). To evaluate variations in altered and response-associated SFC across the functional hierarchy, we ranked significantly different regions by their principal gradient values and assessed patterns of increase or decrease along the gradient axis. The principal gradient value, calculated from 219 healthy individuals in the Human Connectome Project, represents a region's position along the principal gradient axis.

RESULTS

Compared to HC, MDD patients exhibited increased SFC in unimodal regions (lower principal gradient) and decreased SFC in transmodal regions (higher principal gradient) (p < 0.001). Responders primarily had higher SFC in unimodal regions and lower SFC in attentional networks (median principal gradient) (p < 0.001).

CONCLUSIONS

Our findings reveal opposing SFC alterations in low-level unimodal and high-level transmodal networks, underscoring spatial variability in MDD pathology. Moreover, hierarchy-specific antidepressant effects provide valuable insights into predicting treatment outcomes.

A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples

BACKGROUND

The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity.

METHODS

We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection.

RESULTS

In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample.

CONCLUSION

Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Abnormality in Peripheral and Brain Iron Contents and the Relationship with Grey Matter Volumes in Major Depressive Disorder

Major depressive disorder (MDD) is a prevalent mental illness globally, yet its etiology remains largely elusive. Recent interest in the scientific community has focused on the correlation between the disruption of iron homeostasis and MDD. Prior studies have revealed anomalous levels of iron in both peripheral blood and the brain of MDD patients; however, these findings are not consistent. This study involved 95 MDD patients aged 18-35 and 66 sex- and age-matched healthy controls (HCs) who underwent 3D-T1 and quantitative susceptibility mapping (QSM) sequence scans to assess grey matter volume (GMV) and brain iron concentration, respectively. Plasma ferritin (pF) levels were measured in a subset of 49 MDD individuals and 41 HCs using the Enzyme-linked immunosorbent assay (ELISA), whose blood data were simultaneously collected. We hypothesize that morphological brain changes in MDD patients are related to abnormal regulation of iron levels in the brain and periphery. Multimodal canonical correlation analysis plus joint independent component analysis (MCCA+jICA) algorithm was mainly used to investigate the covariation patterns between the brain iron concentration and GMV. The results of "MCCA+jICA" showed that the QSM values in bilateral globus pallidus and caudate nucleus of MDD patients were lower than HCs. While in the bilateral thalamus and putamen, the QSM values in MDD patients were higher than in HCs. The GMV values of these brain regions showed a significant positive correlation with QSM. The GMV values of bilateral putamen were found to be increased in MDD patients compared with HCs. A small portion of the thalamus showed reduced GMV values in MDD patients compared to HCs. Furthermore, the region of interest (ROI)-based comparison results in the basal ganglia structures align with the outcomes obtained from the "MCCA+jICA" analysis. The ELISA results indicated that the levels of pF in MDD patients were higher than those in HCs. Correlation analysis revealed that the increase in pF was positively correlated with the iron content in the left thalamus. Finally, the covariation patterns obtained from "MCCA+jICA" analysis as classification features effectively differentiated MDD patients from HCs in the support vector machine (SVM) model. Our findings indicate that elevated peripheral ferritin in MDD patients may disrupt the normal metabolism of iron in the brain, leading to abnormal changes in brain iron levels and GMV.

White Matter Tract Integrity Is Reduced in Depression and in Individuals With Genetic Liability to Depression

BACKGROUND

While major depression has been linked to changes in white matter architecture, it remains unclear whether risk factors for depression are directly associated with these alterations. We reexamined white matter fiber tracts in individuals with depressive symptoms and investigated the connection between genetic and environmental risk for depression and structural changes in the brain.

METHODS

We included 19,183 participants from the UK Biobank imaging cohort, with depression status and adverse life experience based on questionnaire data and genetic liability for depression quantified by polygenic scores. The integrity of 27 white matter tracts was assessed using mean fractional anisotropy derived from diffusion magnetic resonance imaging.

RESULTS

White matter integrity was reduced, particularly in thalamic and intracortical fiber tracts, in individuals with depressive symptoms, independent of current symptom status. In a group of healthy individuals without depression, increasing genetic risk and increasing environmental risk were associated with reduced integrity in relevant fiber tracts, particularly in thalamic radiations. This association was stronger than expected based on statistical dependencies between samples, as confirmed by subsequent in silico simulations and permutation tests.

CONCLUSIONS

White matter alterations in thalamic and association tracts are associated with depressive symptoms and genetic risk for depression in unaffected individuals, suggesting an intermediate phenotype at the brain level.

Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimer's disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

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.

Disrupted Structural Brain Networks and Structural-Functional Decoupling in First-Episode Drug-Naïve Adolescent Major Depressive Disorder

PURPOSE

Major depressive disorder (MDD) tends to emerge during adolescence, but the neurobiology of adolescent MDD is still poorly understood. This study aimed to explore the topological organization of white matter structural networks and the relationship between structural and functional connectivity in adolescent MDD.

METHODS

Structural and functional magnetic resonance imaging data were acquired from 94 first-episode drug-naïve adolescent MDD patients and 78 healthy adolescents. Whole brain structural and functional brain networks were constructed for each subject. Then, the topological organization of structural brain networks and the coupling strength between structural and functional connectivity were analyzed.

RESULTS

Compared with controls, adolescent MDD patients showed disrupted small-world, rich-club, and modular organizations. Nodal centralities in the medial part of bilateral superior frontal gyrus, bilateral hippocampus, right superior occipital gyrus, right angular gyrus, bilateral precuneus, left caudate nucleus, bilateral putamen, right superior temporal gyrus, and right temporal pole part of superior temporal gyrus were significantly lower in adolescent MDD patients compared with controls. The coupling strength between structural and functional connectivity was significantly lower in adolescent MDD patients compared with controls.

DISCUSSION

Our findings suggest widespread disruption of structural brain networks and structural-functional decoupling in adolescent MDD, potentially leading to reduced network communication capacity.

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

BACKGROUND

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.

AIMS

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.

METHOD

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.

RESULTS

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.

CONCLUSIONS

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.

Abnormal functional connectivity within the prefrontal cortex is associated with multiple plasma lipid species in major depressive disorder

BACKGROUND

Abnormalities in functional connectivity (FC) in major depressive disorder (MDD) have been widely reported. Analysis of the relationship between FC and plasma lipid profiles would be meaningful in the exploration of pathophysiological mechanisms and helpful for the identification of biomarkers for MDD.

METHODS

Patients with MDD (n = 49) and healthy controls (HC, n = 87) were recruited. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected for FC construction. The plasma lipid profiles were acquired using ultra-performance liquid chromatography (UPLC) and mass spectrometry (MS) analysis and clustered as co-expression modules. The differential FC and lipid modules between HCs and patients with MDD were identified, and then the association between FC and lipid co-expression modules was analyzed using correlation analysis. The modules associated molecular function was explored using metabolite set enrichment analysis (MSEA).

RESULTS

MDD-associated FC and lipid co-expression modules were identified. One module was associated with FC values between the right orbital part of the middle frontal gyrus and the opercular part of the left inferior frontal gyrus, which was enriched in lipid sets of diacylglycerols and fatty alcohols; another module was associated with FC values between the right middle frontal gyrus and the right anterior cingulate and paracingulate gyri, which was enriched in lipid sets of glycerophosphocholines and glycerophosphoethanolamines.

CONCLUSION

Our results indicated that abnormal FC in the prefrontal cortex is associated with multiple plasma lipid species, which may provide novel clues for exploring the pathophysiology of MDD.

Improvement of astrocytic gap junction involves the anti-depressive effect of celecoxib through inhibition of NF-κB

CONTEXT

Celecoxib, a selective cyclooxygenase-2 (COX-2) inhibitor, has been shown to exhibit anti-depressive effects in clinical trials. However, the direct mechanism underlying its effect on neuroinflammation remains unclear. Neuroinflammatory reaction from astrocytes leads to depression, and our previous study found that gap junction disorder between astrocytes aggravated neuroinflammatory reaction in depressed mice.

OBJECTIVE

To investigate the potential mechanism of celecoxib's effects on astrocytic gap junctions during the central nervous inflammation-induced depression.

MATERIALS & METHODS

Stereotaxic injection of lipopolysaccharide (LPS) into the prefrontal cortex (PFC) to establish a model of major depressive disorder (MDD). Celecoxib was administrated into PFC 15 min after LPS injection. The depressive performance was tested by tail suspension test and forced swimming test, and the levels of proinflammation cytokines were determined at mRNA and protein levels. Resting-state functional connection (rsFC) was employed to assess changes in the default mode network (DMN). Additionally, astrocytic gap junctions were also determined by lucifer yellow (LY) diffusion and transmission electron microscope (TEM), and the expression of connexin 43 (Cx43) was measured by western blotting, quantitative polymerase chain reaction, and immunofluorescence.

RESULTS

LPS injection induced significant depressive performance, which was ameliorated by celecoxib treatment. Celecoxib also improved rsFC in the DMN. Furthermore, celecoxib improved astrocytic gap junctions as evidenced by increased LY diffusion, shortened gap junction width, and normalized levels of phosphorylated Cx43. Celecoxib also blocked the phosphorylation of p65, and inhibition of p65 abolished the improvement of Cx43.

DISCUSSION & CONCLUSION

Anti-depressive effects of celecoxib are mediated, at least in part, by the inhibition of nuclear factor- kappa B (NF-κB) and the subsequent improvement of astrocytic gap junction function.

A neuroimaging study of brain activity alterations in treatment-resistant depression after a dual target accelerated transcranial magnetic stimulation

In this study, we designed a new transcranial magnetic stimulation (TMS) protocol using a dual-target accelerated transcranial magnetic stimulation (aTMS) for patients with treatment resistant depression (TRD). There are 58 TRD patients were recruited from the Second People's Hospital of Guizhou Province, who were, respectively, received dual-target (real continuous theta burst stimulation (cTBS) at right orbitofrontal cortex (OFC) and real repetitive transcranial magnetic stimulation (rTMS) at left dorsolateral prefrontal cortex (DLPFC)), single- target (sham cTBS at right OFC and real rTMS at left DLPFC), and sham stimulation (sham cTBS at right OFC and sham rTMS at left DLPFC). Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired before and after aTMS treatment to compare characteristics of brain activities by use of amplitude of low-frequency fluctuations (ALFF), fractional low-frequency fluctuations (fALFF) and functional connectivity (FC). At the same time, Hamilton Depression Scale-24 (HAMD24) were conducted to assess the effect. HAMD24 scores reduced significantly in dual group comparing to the single and sham group. Dual-target stimulation decreased not only the ALFF values of right fusiform gyrus (FG) and fALFF values of the left superior temporal gyrus (STG), but also the FC between the right FG and the bilateral middle frontal gyrus (MFG), left triangular part of inferior frontal gyrus (IFG). Higher fALFF value in left STG at baseline may predict better reaction for bilateral arTMS. Dual-targe stimulation can significantly change resting-state brain activities and help to improve depressive symptoms.

Abnormal fractional amplitude of low-frequency fluctuations and regional homogeneity in major depressive disorder with non-suicidal self-injury

OBJECTIVE

We conducted this resting-state functional magnetic resonance imaging (rsfMRI) study to characterize changes in regional homogeneity (ReHo) or fractional amplitude of low-frequency fluctuations (fALFF) in young adult patients with major depressive disorder (MDD), with or without non-suicidal self-injury (NSSI).

METHODS

We recruited 54 MDD patients with NSSI (MDD/NSSI), 68 MDD patients without NSSI, which is referred to as simple MDD (sMDD), and 66 matched healthy controls (HCs). A combination of fALFF and ReHo analyses was conducted. The effects of NSSI on the brain and their relationship to clinical variables were examined in this study.

RESULTS

MDD/NSSI patients have decreased fALFF in the right superior frontal gyrus (SFG) and the right inferior parietal lobe (IPL), decreased ReHo in the right SFG and the right middle temporal gyrus (MTG) and the left middle occipital gyrus (MOG). fALFF and ReHo values of the right SFG are positively correlated. The ReHo values of the right SFG and the number of recent self-injuries are positively correlated; the fALFF values of the right SFG are negatively correlated with NSSI severity.

CONCLUSIONS

There is a difference in brain activity between MDD/NSSI and sMDD, which may serve as an important physiological marker to determine the risk of self-injury and suicide.

SIGNIFICANCE

Abnormal brain activity in patients with NSSI may provide new perspectives and significant implications on the severity of MDD patients and the prevention of self-injury and suicide.

Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome

BACKGROUND

Major depressive disorder (MDD) is a highly heterogeneous disorder that typically emerges in adolescence and can occur throughout adulthood. Studies aimed at quantitatively uncovering the heterogeneity of individual functional connectome abnormalities in MDD and identifying reproducibly distinct neurophysiological MDD subtypes across the lifespan, which could provide promising insights for precise diagnosis and treatment prediction, are still lacking.

METHODS

Leveraging resting-state functional magnetic resonance imaging data from 1148 patients with MDD and 1079 healthy control participants (ages 11-93), we conducted the largest multisite analysis to date for neurophysiological MDD subtyping. First, we characterized typical lifespan trajectories of functional connectivity strength based on the normative model and quantitatively mapped the heterogeneous individual deviations among patients with MDD. Then, we identified neurobiological MDD subtypes using an unsupervised clustering algorithm and evaluated intersite reproducibility. Finally, we validated the subtype differences in baseline clinical variables and longitudinal treatment predictive capacity.

RESULTS

Our findings indicated great intersubject heterogeneity in the spatial distribution and severity of functional connectome deviations among patients with MDD, which inspired the identification of 2 reproducible neurophysiological subtypes. Subtype 1 showed severe deviations, with positive deviations in the default mode, limbic, and subcortical areas and negative deviations in the sensorimotor and attention areas. Subtype 2 showed a moderate but converse deviation pattern. More importantly, subtype differences were observed in depressive item scores and the predictive ability of baseline deviations for antidepressant treatment outcomes.

CONCLUSIONS

These findings shed light on our understanding of different neurobiological mechanisms underlying the clinical heterogeneity of MDD and are essential for developing personalized treatments for this disorder.

Social support and overeating in young women: The role of altering functional network connectivity patterns and negative emotions

Research suggests that social support has a protective effect on emotional health and emotionally induced overeating. Women are especially more sensitive to benefits from social support when facing eating problems. Although it has been demonstrated that social support can affect the neural processes of emotion regulation and reward perception, it is unclear how social support alters synergistic patterns in large-scale brain networks associated with negative emotions and overeating. We used a large sample of young women aged 17-22 years (N = 360) to examine how social support influences the synchrony of five intrinsic networks (executive control network [ECN], default mode network, salience network [SN], basal ganglia network, and precuneus network [PN]) and how these networks influence negative affect and overeating. Additionally, we explored these analyses in another sample of males (N = 136). After statistically controlling for differences in age and head movement, we observed significant associations of higher levels of social support with increased intra- and inter-network functional synchrony, particularly for ECN-centered network connectivity. Subsequent chain-mediated analyses showed that social support predicted overeating through the ECN-SN and ECN-PN network connectivity and negative emotions. However, these results were not found in men. These findings suggest that social support influences the synergistic patterns within and between intrinsic networks related to inhibitory control, emotion salience, self-referential thinking, and reward sensitivity. Furthermore, they reveal that social support and its neural markers may play a key role in young women's emotional health and eating behavior.

Multi-view graph network learning framework for identification of major depressive disorder

Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) exhibits non-Euclidean topological structures, which have pathological foundations and serve as ideal objective data for intelligent diagnosis of major depressive disorder (MDD) patients. Additionally, the fully connected FC demonstrates uniform spatial structures. To learn and integrate information from these two structural forms for a more comprehensive identification of MDD patients, we propose a novel hierarchical learning structure called Multi-View Graph Neural Network (MV-GNN). In MV-GNN, the collaborative FC of subjects is filtered and reconstructed from topological view to obtain the reconstructed FC, incorporating various threshold values to calculate the topological attributes of brain regions. ROC analysis is performed on the average scores of these attributes for MDD and healthy control (HC) groups to determine an efficient threshold. Group differences analysis is conducted on the efficient topological attributes of brain regions, followed by their selection. These efficient attributes, along with the reconstructed FC, are combined to construct a graph view using self-attention graph pooling and graph convolutional neural networks, enabling efficient embedding. To extract efficient FC pattern difference information from spatial view, a dual leave-one-out cross-feature selection method is proposed. It selects and extracts relevant information from uniformly sized FC structures' high-dimensional spatial features, constructing a relationship view between brain regions. This approach incorporates both the whole graph topological view and spatial relationship view in a multi-layered structure, fusing them using gating mechanisms. By incorporating multiple views, it enhances the inference of whether subjects suffer from MDD and reveals differential information between MDD and HC groups across different perspectives. The proposed model structure is evaluated through leave-one-site cross-validation and achieves an average accuracy of 65.61% in identifying MDD patients at a single-center site, surpassing state-of-the-art methods in MDD recognition. The model provides valuable discriminatory information for objective diagnosis of MDD and serves as a reference for pathological foundations.

Abnormal multi-layered dynamic cortico-subcortical functional connectivity in major depressive disorder and generalized anxiety disorder

Comorbidity has been frequently observed between generalized anxiety disorder (GAD) and major depressive disorder (MDD), however, common and distinguishable alterations in the topological organization of functional brain networks remain poorly understood. We sought to determine a robust and sensitive functional connectivity marker for diagnostic classification and symptom severity prediction. Multi-layered dynamic functional connectivity including whole brain, network-node and node-node layers via graph theory and gradient analyses were applied to functional MRI resting-state data obtained from 31 unmedicated GAD and 34 unmedicated MDD patients as well as 33 age and education matched healthy controls (HC). GAD and MDD symptoms were assessed using Penn State Worry Questionnaire and Beck Depression Inventory II, respectively. Three network measures including global properties (i.e., global efficiency, characteristic path length), regional nodal property (i.e., degree) and connectivity gradients were computed. Results showed that both patient groups exhibited abnormal dynamic cortico-subcortical topological organization compared to healthy controls, with MDD > GAD > HC in degree of randomization. Furthermore, our multi-layered dynamic functional connectivity network model reached 77% diagnostic accuracy between GAD and MDD and was highly predictive of symptom severity, respectively. Gradients of functional connectivity for superior frontal cortex-subcortical regions, middle temporal gyrus-subcortical regions and amygdala-cortical regions contributed more in this model compared to other gradients. We found shared and distinct cortico-subcortical connectivity features in dynamic functional brain networks between GAD and MDD, which together can promote the understanding of common and disorder-specific topological organization dysregulations and facilitate early neuroimaging-based diagnosis.

Altered single-subject gray matter structural networks in first-episode drug-naïve adolescent major depressive disorder

Although previous studies have demonstrated regional gray matter (GM) structural abnormalities in adolescents with major depressive disorder (MDD), how the topological organization of GM networks is affected in these patients is still unclear. Structural magnetic resonance imaging data were acquired from 100 first-episode drug-naïve adolescent MDD patients and 80 healthy controls (HCs). Whole-brain GM structural network was constructed for each subject, and a graph theory analysis was used to calculate the topological metrics of GM networks. Adolescent MDD patients showed significantly lower cluster coefficient and local efficiency compared to HCs. Compared to controls, adolescent MDD patients showed higher nodal centralities in the bilateral cuneus, left lingual gyrus, and right middle occipital gyrus and lower nodal centralities in the bilateral dorsolateral superior frontal gyrus, bilateral middle frontal gyrus, right anterior cingulate and paracingulate gyri, bilateral hippocampus, bilateral amygdala, bilateral caudate nucleus, and bilateral thalamus. Nodal centralities of the hippocampus were negatively associated with symptom severity and illness duration. Our findings suggest disrupted topological organization of GM structural networks in adolescent MDD patients. Impaired local segregation and abnormal nodal centralities in the prefrontal-subcortical-limbic areas and visual cortex regions may play important roles in the neurobiology of adolescent-onset MDD.

Functional connectivity mediating passive coping style and perceived stress in predicting anxiety

BACKGROUND

Passive coping style (CS) and perceived stress play significant roles as influencing factors in the development of anxiety. However, the underlying neurobiological mechanism linking passive CS and perceived stress to anxiety susceptibility remains elusive. Thus, we aimed to investigate the relationships among passive CS, brain functional connectivity, perceived stress, and anxiety in young adults.

METHODS

Data from the longitudinal Gene-Brain-Behavior Project(GBB) and Southwest University Longitudinal Imaging Multimodal Project(SLIM) were used. We confirmed the relationship among anxiety, passive CS and perceived stress. Then, we investigated the mediated functional connectivity between passive CS and perceived stress, and used these functional connections to predict present anxiety and follow-up anxiety one year later.

RESULTS

Anxiety scores were significantly positively correlated with passive CS and perceived stress. At the brain network level, connections within the default mode network (DMN) and between the somatomotor network (SMN) and subcortical network (SUN) mediated the relationship between passive CS and perceived stress. Furthermore, present anxiety and follow-up anxiety one year later could be predicted by these mediated functional connections. Nodes with greater predictive contribution were mainly located in the left anterior cingulate gyrus (ACC), left inferior parietal gyrus (IPG), right superior frontal gyrus (SFG), and left middle frontal gyrus (MFG), mainly distributed on the DMN.

CONCLUSION

These findings demonstrated that the mediated neurobiological mechanisms between passive CS and perceived stress could be used to predict present and future anxiety, which enhance understanding of the neurobiological basis of anxiety susceptibility in this passive CS and perceived stress and may have implications for early preventing and intervening mental disorders.

Deciphering the neural mechanisms of miR-134 in major depressive disorder with population-based and person-specific imaging transcriptomic techniques

MiR-134 has emerged as a potential molecular biomarker for the detection and management of major depressive disorder (MDD). Nevertheless, the specific effects of miR-134 as a regulatory element on brain function and its implications for the clinical presentation of MDD are not yet fully understood. In order to investigate the potential neural mechanisms that contribute to the relationship between miR-134 and MDD, we employed a parallel two-stage cross-scale multi-omics approach. This involved utilizing the anterior cingulate cortex (ACC) functional connectivity as a means to connect microscopic molecular structures with macroscopic brain function in two separate cohorts: the MDD-I dataset (56 MDD patients and 51 healthy controls) and the MDD-II dataset (57 MDD patients and 52 healthy controls). We found a stable ACC functional dysconnectivity pattern of MDD and established the hierarchical cross-scale association from molecular organizations of miR-134 target genes to macroscopic brain functional dysconnectivity and associated behavior, as revealed by population-based analysis. Additionally, our person-specific imaging transcriptomic study revealed that individual exosomal miR-134 expression levels impact on individual clinical symptoms of MDD by modulating ACC-related functional dysconnectivity. Together, our findings provide compelling evidence of the correlation between miR-134 and depression across multi scales within the gene-brain-behavior context.

Neuroanatomy of post-stroke depression: the association between symptom clusters and lesion location

Post-stroke depression affects about 30% of stroke patients and often hampers functional recovery. The diagnosis of depression encompasses heterogeneous symptoms at emotional, motivational, cognitive, behavioural or somatic levels. Evidence indicates that depression is caused by disruption of bio-aminergic fibre tracts between prefrontal and limbic or striatal brain regions comprising different functional networks. Voxel-based lesion-symptom mapping studies reported discrepant findings regarding the association between infarct locations and depression. Inconsistencies may be due to the usage of sum scores, thereby mixing different symptoms of depression. In this cross-sectional study, we used multivariate support vector regression for lesion-symptom mapping to identify regions significantly involved in distinct depressive symptom domains and global depression. MRI lesion data were included from 200 patients with acute first-ever ischaemic stroke (mean 0.9 ± 1.5 days of post-stroke). The Montgomery-Åsberg Depression Rating interview assessed depression severity in five symptom domains encompassing motivational, emotional and cognitive symptoms deficits, anxiety and somatic symptoms and was examined 8.4 days of post-stroke (±4.3). We found that global depression severity, irrespective of individual symptom domains, was primarily linked to right hemispheric lesions in the dorsolateral prefrontal cortex and inferior frontal gyrus. In contrast, when considering distinct symptom domains individually, the analyses yielded much more sensitive results in regions where the correlations with the global depression score yielded no effects. Accordingly, motivational deficits were associated with lesions in orbitofrontal cortex, dorsolateral prefrontal cortex, pre- and post-central gyri and basal ganglia, including putamen and pallidum. Lesions affecting the dorsal thalamus, anterior insula and somatosensory cortex were significantly associated with emotional symptoms such as sadness. Damage to the dorsolateral prefrontal cortex was associated with concentration deficits, cognitive symptoms of guilt and self-reproach. Furthermore, somatic symptoms, including loss of appetite and sleep disturbances, were linked to the insula, parietal operculum and amygdala lesions. Likewise, anxiety was associated with lesions impacting the central operculum, insula and inferior frontal gyrus. Interestingly, symptoms of anxiety were exclusively left hemispheric, whereas the lesion-symptom associations of the other domains were lateralized to the right hemisphere. In conclusion, this large-scale study shows that in acute stroke patients, differential post-stroke depression symptom domains are associated with specific structural correlates. Our findings extend existing concepts on the neural underpinnings of depressive symptoms, indicating that differential lesion patterns lead to distinct depressive symptoms in the first weeks of post-stroke. These findings may facilitate the development of personalized treatments to improve post-stroke rehabilitation.

Down-regulated miR-16-2 in peripheral blood is positively correlated with decreased bilateral insula volume in patients with major depressive disorder

BACKGROUND

The downregulated microRNA-16-2-3p (miR-16-2) had been believed to be associated with major depressive disorder (MDD). This study aimed to investigate the potential of miR-16-2 as a biomarker for MDD by analysing its expression levels, furthermore, to explore the relationship between miR-16-2, clinical symptoms and alterations in grey matter volume (GMV) in MDD patients.

METHODS

Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression level of miR-16-2 in 48 drug-naïve patients with MDD and 50 healthy controls (HCs). We conducted ROC curve analysis to assess the diagnostic value of miR-16-2 in MDD, and evaluated its ability to predict antidepressant response by reassessing depressive and anxiety symptoms after treatment. Voxel-based morphometry was carried out to explore alterations in regional GMV that may be associated with MDD. Pearson analysis was used to explore the relationship between miR-16-2 expression, clinical symptoms, and altered GMV in the brains of MDD patients.

RESULTS

We found that MDD patients had significantly downregulated miR-16-2 expression, which was negatively correlated with HAMD-17 and HAMA-14 scores, and had great diagnostic value for MDD (AUC = 0.806, 95 % CI: 0.721-0.891). In addition, MDD patients had significantly lower GMV in the bilateral insula and left superior temporal gyrus (STG_L) than HCs. GMV reduction in the bilateral insula was found to be correlated with miR-16-2 expression.

CONCLUSIONS

Our findings support the potential value of miRNA-16-2 as a biomarker for MDD. It also suggests that miRNA-16-2 may be associated with abnormal insula and involved in pathophysiological mechanisms of MDD.

Variation in functional networks between clinical and subclinical discharges in childhood absence epilepsy: A multi-frequency MEG study

OBJECTIVE

Two types of spike-and-wave discharges (SWDs) exist in childhood absence epilepsy (CAE): clinical discharges are prolonged and manifest primarily as impaired consciousness, whereas subclinical discharges are brief with few objectively visible symptoms. This study aimed to compare neural functional network and default mode network (DMN) activity between clinical and subclinical discharges to better understand the underlying mechanism of CAE.

METHODS

Using magnetoencephalography (MEG) data from 21 patients, we obtained 25 segments each of clinical discharges and subclinical discharges. Amplitude envelope correlation analysis was used to construct functional networks and graph theory was used to calculate network topological data. We then compared differences in functional connectivity within the DMN between clinical and subclinical discharges. All statistical comparisons were performed using paired-sample tests.

RESULTS

Compared to subclinical discharges, the functional network of clinical discharges exhibited higher synchronization - particularly in the parahippocampal gyrus - as early as 10 s before the seizure. Additionally, the functional network of clinical SWDs presented an anterior shift of key nodes in the alpha frequency band. Regarding clinical discharge progression, there were gradual increases in the parameter node strengths (S), clustering coefficients (C), and global efficiency (E) of the functional networks, while the path lengths (L) decreased. These changes were most prominent at the onset of discharges and followed by some recovery in the high-frequency bands, but no significant change in the low-frequency bands. Furthermore, connections within the DMN during the discharge period were significantly stronger for clinical discharge compared to subclinical discharges.

CONCLUSIONS

These findings suggest that a more regular network before abnormal discharges in clinical discharges contributes to SWD explosion and that the parahippocampal gyrus plays an important role in maintaining oscillations. An absence seizure is a gradual process and the emergence of SWDs may be accompanied by initiation of inhibitory mechanisms. Enhanced functional connectivity among DMN brain regions may indicate that patients have entered a state of introspection, and functional abnormalities in the parahippocampal gyrus may be associated with patients' transient memory loss.

Aberrant single-subject morphological brain networks in first-episode, treatment-naive adolescents with major depressive disorder

Background

Neuroimaging-based connectome studies have indicated that major depressive disorder (MDD) is associated with disrupted topological organization of large-scale brain networks. However, the disruptions and their clinical and cognitive relevance are not well established for morphological brain networks in adolescent MDD.

Objective

To investigate the topological alterations of single-subject morphological brain networks in adolescent MDD.

Methods

Twenty-five first-episode, treatment-naive adolescents with MDD and 19 healthy controls (HCs) underwent T1-weighted magnetic resonance imaging and a battery of neuropsychological tests. Single-subject morphological brain networks were constructed separately based on cortical thickness, fractal dimension, gyrification index, and sulcus depth, and topologically characterized by graph-based approaches. Between-group differences were inferred by permutation testing. For significant alterations, partial correlations were used to examine their associations with clinical and neuropsychological variables in the patients. Finally, a support vector machine was used to classify the patients from controls.

Results

Compared with the HCs, the patients exhibited topological alterations only in cortical thickness-based networks characterized by higher nodal centralities in parietal (left primary sensory cortex) but lower nodal centralities in temporal (left parabelt complex, right perirhinal ectorhinal cortex, right area PHT and right ventral visual complex) regions. Moreover, decreased nodal centralities of some temporal regions were correlated with cognitive dysfunction and clinical characteristics of the patients. These results were largely reproducible for binary and weighted network analyses. Finally, topological properties of the cortical thickness-based networks were able to distinguish the MDD adolescents from HCs with 87.6% accuracy.

Conclusion

Adolescent MDD is associated with disrupted topological organization of morphological brain networks, and the disruptions provide potential biomarkers for diagnosing and monitoring the disease.

Differences in White Matter Structural Networks in Family Risk of Major Depressive Disorder and Suicidality: A Connectome Analysis

Background

Depression and suicide are leading global causes of disability and death and are highly familial. Family and individual history of depression are associated with neurobiological differences including decreased white matter connectivity; however, this has only been shown for individual regions. We use graph theory models to account for the network structure of the brain with high levels of specialization and integration and examine whether they differ by family history of depression or of suicidality within a three-generation longitudinal family study with well-characterized clinical histories.

Methods

Clinician interviews across three generations were used to classify family risk of depression and suicidality. Then, we created weighted network models using 108 cortical and subcortical regions of interest for 96 individuals using diffusion tensor imaging derived fiber tracts. Global and local summary measures (clustering coefficient, characteristic path length, and global and local efficiencies) and network-based statistics were utilized for group comparison of family history of depression and, separately, of suicidality, adjusted for personal psychopathology.

Results

Clustering coefficient (connectivity between neighboring regions) was lower in individuals at high family risk of depression and was associated with concurrent clinical symptoms. Network-based statistics showed hypoconnected subnetworks in individuals with high family risk of depression and of suicidality, after controlling for personal psychopathology. These subnetworks highlighted cortical-subcortical connections including between the superior frontal cortex, thalamus, precuneus, and putamen.

Conclusions

Family history of depression and of suicidality are associated with hypoconnectivity between subcortical and cortical regions, suggesting brain-wide impaired information processing, even in those personally unaffected.

The association of white matter connectivity with prevalence, incidence and course of depressive symptoms: The Maastricht Study

BACKGROUND

Altered white matter brain connectivity has been linked to depression. The aim of this study was to investigate the association of markers of white matter connectivity with prevalence, incidence and course of depressive symptoms.

METHODS

Markers of white matter connectivity (node degree, clustering coefficient, local efficiency, characteristic path length, and global efficiency) were assessed at baseline by 3 T MRI in the population-based Maastricht Study (n = 4866; mean ± standard deviation age 59.6 ± 8.5 years, 49.0% women; 17 406 person-years of follow-up). Depressive symptoms (9-item Patient Health Questionnaire; PHQ-9) were assessed at baseline and annually over seven years of follow-up. Major depressive disorder (MDD) was assessed with the Mini-International Neuropsychiatric Interview at baseline only. We used negative binominal, logistic and Cox regression analyses, and adjusted for demographic, cardiovascular, and lifestyle risk factors.

RESULTS

A lower global average node degree at baseline was associated with the prevalence and persistence of clinically relevant depressive symptoms [PHQ-9 ⩾ 10; OR (95% confidence interval) per standard deviation = 1.21 (1.05-1.39) and OR = 1.21 (1.02-1.44), respectively], after full adjustment. On the contrary, no associations were found of global average node degree with the MDD at baseline [OR 1.12 (0.94-1.32) nor incidence or remission of clinically relevant depressive symptoms [HR = 1.05 (0.95-1.17) and OR 1.08 (0.83-1.41), respectively]. Other connectivity measures of white matter organization were not associated with depression.

CONCLUSIONS

Our findings suggest that fewer white matter connections may contribute to prevalent depressive symptoms and its persistence but not to incident depression. Future studies are needed to replicate our findings.

Functional connectomics in depression: insights into therapies

Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.

The characteristics of brain network in patient with post-stroke depression under cognitive task condition

Objectives

Post-stroke depression (PSD) may be associated with the altered brain network property. This study aimed at exploring the brain network characteristics of PSD under the classic cognitive task, i.e., the oddball task, in order to promote our understanding of the pathogenesis and the diagnosis of PSD.

Methods

Nineteen stroke survivors with PSD and 18 stroke survivors with no PSD (non-PSD) were recruited. The functional near-infrared spectroscopy (fNIRS) covering the dorsolateral prefrontal cortex was recorded during the oddball task state and the resting state. The brain network characteristics were extracted using the graph theory and compared between the PSD and the non-PSD subjects. In addition, the classification performance between the PSD and non-PSD subjects was evaluated using features in the resting and the task state, respectively.

Results

Compared with the resting state, more brain network characteristics in the task state showed significant differences between the PSD and non-PSD groups, resulting in better classification performance. In the task state, the assortativity, clustering coefficient, characteristic path length, and local efficiency of the PSD subjects was larger compared with the non-PSD subjects while the global efficiency of the PSD subjects was smaller than that of the non-PSD subjects.

Conclusion

The altered brain network properties associated with PSD in the cognitive task state were more distinct compared with the resting state, and the ability of the brain network to resist attack and transmit information was reduced in PSD patients in the task state.

Significance

This study demonstrated the feasibility and superiority of investigating brain network properties in the task state for the exploration of the pathogenesis and new diagnosis methods for PSD.

Measures of Connectivity and Dorsolateral Prefrontal Cortex Volumes and Depressive Symptoms Following Treatment With Selective Serotonin Reuptake Inhibitors in Adolescents

Importance

Selective serotonin reuptake inhibitors (SSRIs) are considered a first-line pharmacological treatment for adolescent depression with moderate or higher levels of symptom severity. Thus, it is important to understand neurobiological changes related to SSRIs during the course of treatment for adolescents with depression.

Objective

To examine neurobiological changes associated with SSRI treatment in adolescents with major depressive disorder (MDD) by measuring longitudinal changes in volume and resting-state functional connectivity (rsFC) in the dorsolateral prefrontal cortex (DLPFC), a core region of cognitive control.

Design, Setting, and Participants

This cohort study was conducted with an open-label design. Adolescents with MDD and healthy controls were recruited at the Seoul National University Hospital (Seoul, South Korea). Adolescents with MDD were treated with escitalopram for 8 weeks. Data analysis was conducted between April 2021 and February 2022.

Main Outcomes and Measures

Depressive symptoms were assessed using the Children's Depression Rating Scale-Revised. The outcome measure was defined as the change in Children's Depression Rating Scale-Revised scores from week 0 (before treatment) to week 8 (after treatment) or upon termination. Participants completed structural and resting-state functional magnetic resonance imaging (rsfMRI) assessments before (week 0) and after (week 8) SSRI treatment. Repeated measures analysis of variance and liner mixed model analyses were used to examine the longitudinal associations of SSRI treatment with DLPFC volume and rsFC between responders who showed at least a 40% decrease in depressive symptoms and nonresponders who did not.

Results

Ninety-five adolescents with MDD and 57 healthy controls were initially recruited. The final analyses of volume included 36 responders (mean [SD] age, 15.0 [1.6] years; 25 girls [69.4%]) and 26 nonresponders (mean [SD] age, 15.3 [1.5] years; 19 girls [73.1%]). Analyses of rsFC included 33 responders (mean [SD] age, 15.2 [1.5] years; 21 girls [63.6%]) and 26 nonresponders (mean [SD] age, 15.3 [1.5] years; 19 girls [73.1%]). The longitudinal associations of SSRI treatment were more evident in responders than in nonresponders. Responders showed significantly increased right DLPFC volume, decreased bilateral DLPFC rsFC with the superior frontal gyri, and decreased left DLPFC rsFC with the ventromedial PFC after treatment compared with before treatment. Furthermore, increased right DLPFC volume was correlated with decreased rsFC between the right DLPFC and superior frontal gyri after SSRI treatment.

Conclusions and Relevance

The preliminary results of this cohort study suggest that the DLPFC volumetric and rsFC changes may serve as potential neurobiological treatment markers that are associated with symptom improvement in adolescents with MDD.

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.

Enhanced amygdala-cingulate connectivity associates with better mood in both healthy and depressive individuals after sleep deprivation

Sleep loss robustly disrupts mood and emotion regulation in healthy individuals but can have a transient antidepressant effect in a subset of patients with depression. The neural mechanisms underlying this paradoxical effect remain unclear. Previous studies suggest that the amygdala and dorsal nexus (DN) play key roles in depressive mood regulation. Here, we used functional MRI to examine associations between amygdala- and DN-related resting-state connectivity alterations and mood changes after one night of total sleep deprivation (TSD) in both healthy adults and patients with major depressive disorder using strictly controlled in-laboratory studies. Behavioral data showed that TSD increased negative mood in healthy participants but reduced depressive symptoms in 43% of patients. Imaging data showed that TSD enhanced both amygdala- and DN-related connectivity in healthy participants. Moreover, enhanced amygdala connectivity to the anterior cingulate cortex (ACC) after TSD associated with better mood in healthy participants and antidepressant effects in depressed patients. These findings support the key role of the amygdala-cingulate circuit in mood regulation in both healthy and depressed populations and suggest that rapid antidepressant treatment may target the enhancement of amygdala-ACC connectivity.

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

BACKGROUND

Neuroimaging studies on major depressive disorder (MDD) have identified an extensive range of brain structural abnormalities, but the exact neural mechanisms associated with MDD remain elusive. Most previous studies were performed with voxel- or surface-based morphometry which were univariate methods without considering spatial information across voxels/vertices.

METHODS

Brain morphology was investigated using voxel-based morphometry (VBM) and source-based morphometry (SBM) in 1082 MDD patients and 990 healthy controls (HCs) from the REST-meta-MDD Consortium. We first examined group differences in regional grey matter (GM) volumes and structural covariance networks between patients and HCs. We then compared first-episode, drug-naïve (FEDN) patients, and recurrent patients. Additionally, we assessed the effects of symptom severity and illness duration on brain alterations.

RESULTS

VBM showed decreased GM volume in various regions in MDD patients including the superior temporal cortex, anterior and middle cingulate cortex, inferior frontal cortex, and precuneus. SBM returned differences only in the prefrontal network. Comparisons between FEDN and recurrent MDD patients showed no significant differences by VBM, but SBM showed greater decreases in prefrontal, basal ganglia, visual, and cerebellar networks in the recurrent group. Moreover, depression severity was associated with volumes in the inferior frontal gyrus and precuneus, as well as the prefrontal network.

CONCLUSIONS

Simultaneous application of VBM and SBM methods revealed brain alterations in MDD patients and specified differences between recurrent and FEDN patients, which tentatively provide an effective multivariate method to identify potential neurobiological markers for depression.

Targeted neurostimulation reverses a spatiotemporal biomarker of treatment-resistant depression

Major depressive disorder (MDD) is widely hypothesized to result from disordered communication across brain-wide networks. Yet, prior resting-state-functional MRI (rs-fMRI) studies of MDD have studied zero-lag temporal synchrony (functional connectivity) in brain activity absent directional information. We utilize the recent discovery of stereotyped brain-wide directed signaling patterns in humans to investigate the relationship between directed rs-fMRI activity, MDD, and treatment response to FDA-approved neurostimulation paradigm termed Stanford neuromodulation therapy (SNT). We find that SNT over the left dorsolateral prefrontal cortex (DLPFC) induces directed signaling shifts in the left DLPFC and bilateral anterior cingulate cortex (ACC). Directional signaling shifts in the ACC, but not the DLPFC, predict improvement in depression symptoms, and moreover, pretreatment ACC signaling predicts both depression severity and the likelihood of SNT treatment response. Taken together, our findings suggest that ACC-based directed signaling patterns in rs-fMRI are a potential biomarker of MDD.

Biotypes of major depressive disorder identified by a multiview clustering framework

BACKGROUND

The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy.

METHODS

We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls).

RESULTS

Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed.

LIMITATIONS

The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes.

CONCLUSIONS

Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.

Eight-week antidepressant treatment changes intrinsic functional brain topology in first-episode drug-naïve patients with major depressive disorder

BACKGROUND

A recent study revealed disrupted topological organization of whole-brain networks in patients with major depressive disorder (MDD); however, these results were mostly driven by recurrent MDD patients, rather than first-episode drug-naïve (FEDN) patients. Furthermore, few longitudinal studies have explored the effects of antidepressant therapy on the topological organization of whole-brain networks.

METHODS

We collected clinical and neuroimaging data from 159 FEDN MDD patients and 152 normal controls (NCs). A total of 115 MDD patients completed an eight-week antidepressant treatment procedure. Topological features of brain networks were calculated using graph theory-based methods and compared between FEDN MDD patients and NCs, as well as before and after treatment.

RESULTS

Decreased global efficiency, local efficiency, small-worldness, and modularity were found in pretreatment FEDN MDD patients compared with NCs. Nodal degrees, betweenness, and efficiency decreased in several networks compared with NCs. After antidepressant treatment, the global efficiency increased, while the local efficiency, the clustering coefficient of the network, the path length, and the normalized characteristic path length decreased. Moreover, the reduction rate of the normalized characteristic path length was positively correlated with the reduction rate of retardation factor scores.

LIMITATIONS

The interaction effects of groups and time on the topological features were not explored because of absence of the eighth-week data of NC group.

CONCLUSIONS

The topological architecture of functional brain networks is disrupted in FEDN MDD patients. After antidepressant therapy, the global efficiency shifted toward recovery, but the local efficiency deteriorated, suggesting a correlation between recovery of retardation symptoms and global efficiency.

Biochemical metabolism in the anterior cingulate cortex and cognitive function in major depressive disorder with or without insomnia syndrome

BACKGROUND

Major depressive disorder (MDD) and insomnia have been linked to deficiencies in cognitive performance. However, the underlying mechanism of cognitive impairment in MDD patients with insomnia symptoms (IS) remains unclear. This study aimed to explore the effects of IS in patients with MDD by comparing cognitive function indices among those with IS, those without insomnia symptoms (NIS), and healthy controls (HCs). In addition, we assessed whether the dysfunction of central nervous system (CNS) is one of the important pathophysiologic mechanisms of IS in patients with MDD by comparing the biochemical metabolism ratios in the anterior cingulate cortex (ACC).

METHOD

Fifty-five MDD with IS, 39 MDD without IS, and 47 demographically matched HCs underwent the MATRICS Consensus Cognitive Battery (MCCB) assessment and proton magnetic resonance spectroscopy (¹H-MRS). MCCB cognitive scores and biochemical metabolism in ACC were assessed and compared between groups.

RESULTS

Compared to the HCs group, IS and NIS groups scored significantly lower in seven MCCB cognitive domains (speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning problem solving and social cognition). IS group showed a lower speed of processing and lower Cho/Cr ratio in the left ACC vs. NIS group and HCs. Also, in IS group, the Cho/Cr ratio in the left ACC was positively correlated with the composite T-score.

CONCLUSION

Patients with comorbidity of MDD with IS may exhibit more common MCCB cognitive impairments than those without IS, particularly speed of processing. Also, dysfunction of ACC may underlie the neural substrate of cognitive impairment in MDD with IS.

Connectomics reconciles seemingly irreconcilable neuroimaging findings

Neuroimaging studies have reported heterogeneity of regional anatomical localization for the same disease, impeding reproducible conclusions regarding brain alterations. In recent work, Cash and colleagues help to reconcile inconsistent findings in functional neuroimaging studies in depression by identifying reliable and clinically valuable distributed brain networks from a connectomic perspective.