Abnormal brain anatomical topological organization of the cognitive-emotional and the frontoparietal circuitry in major depressive disorder

Disruptive and complementary effects of depression symptoms on spontaneous brain activity in the subcortical vascular mild cognitive impairment

Background

Although depression symptoms are commonly reported in patients with subcortical vascular mild cognitive impairment (svMCI), their impact on brain functions remains largely unknown, with diagnoses mainly dependent on behavioral assessments.

Methods

In this study, we analyzed resting-state fMRI data from a cohort of 34 svMCI patients, comprising 18 patients with depression symptoms (svMCI+D) and 16 patients without (svMCI-D), along with 34 normal controls (NC). The study used the fraction of the amplitude of low-frequency fluctuations (fALFF), resting-state functional connectivity, correlation analyses, and support vector machine (SVM) techniques.

Results

The fALFF of the right cerebellum (CERE.R) differed among the svMCI+D, svMCI-D, and NC groups. Specifically, the regional mean fALFF of CERE. R was lower in svMCI-D patients compared to NC but higher in svMCI+D patients compared to svMCI-D patients. Moreover, the adjusted fALFF of CERE. R showed a significant correlation with Montreal Cognitive Assessment (MOCA) scores in svMCI-D patients. The fALFF of the right orbital part of the superior frontal gyrus was significantly correlated with Hamilton Depression Scale scores in svMCI+D patients, whereas the fALFF of the right postcingulate cortex (PCC.R) showed a significant correlation with MOCA scores in svMCI-D patients. Furthermore, RSFC between PCC. R and right precuneus, as well as between CERE. R and the right lingual gyrus (LING.R), was significantly reduced in svMCI-D patients compared to NC. In regional analyses, the adjusted RSFC between PCC. R and PreCUN. R, as well as between CERE. R and LING. R, was decreased in svMCI-D patients compared to NC but increased in svMCI+D patients compared to svMCI-D. Further SVM analyses achieved good performances, with an area under the curve (AUC) of 0.82 for classifying svMCI+D, svMCI-D, and NC; 0.96 for classifying svMCI+D and svMCI-D; 0.82 for classifying svMCI+D and NC; and 0.92 for classifying svMCI-D and NC.

Conclusion

The study revealed disruptive effects of cognitive impairment, along with both disruptive and complementary effects of depression symptoms on spontaneous brain activity in svMCI. Moreover, these findings suggest that the identified features might serve as potential biomarkers for distinguishing between svMCI+D, svMCI-D, and NC, thereby guiding clinical treatments such as transcranial magnetic stimulation for svMCI.

Gender differences in major depressive disorder at different ages: a REST-meta-MDD project-based study

BACKGROUND

Major depressive disorder (MDD) is a highly heterogeneous disease, with differences in clinical manifestations among depression patients based on onset ages and genders. The neural mechanisms underlying these differences remain unclear. In this study, we utilized resting state functional imaging data from a large sample database and adopted the ReHo method to investigate gender differences in local brain function in MDD patients across different onset age groups.

METHODS

The study included 364 MDD patients and 695 healthy participants who were part of the REST-meta-MDD project. Regional homogeneity (ReHo) assessed gender disparities in MDD and healthy individuals within groups delineated by gender and onset age (young group: 18-29 years; middle-aged group: 30-45 years).

RESULTS

Among the young MDD groups, there were significant gender differences in the right superior frontal gyrus, right inferior frontal gyrus, left superior temporal gyrus, and right superior parietal lobule, with male MDD patients having higher ReHo values compared to females. When compared to healthy males, male MDD patients exhibited elevated ReHo values in the right superior parietal lobule. In the middle-aged groups, a marked ReHo difference was observed in the bilateral cerebellum posterior lobe, with female MDD patients showing higher ReHo values.

CONCLUSIONS

The functional mechanisms of MDD differ between genders and show distinct variations across different onset age groups. These findings underscore the importance of developing personalized interventions that address the unique needs of MDD patients, tailored to their gender and age, and necessitate the development of antidepressant medications targeted at each gender-age subgroup.

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.

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.

Defocused mode in depressed mood and its changes in time-frequency attention-related beta

Depressed mood has been proposed to possibly possess a unique mode of defocused attention. However, this argument needs to be supported by experimental evidence based on attentional performance. The present study used a perceptual load paradigm, combining factors of perceptual load, distractor-target compatibility, and eccentricity, to investigate the degree of attentional distraction in depressed mood. In addition, the mode of attentional distraction associated with depressed mood was explored with the time-frequency features of electroencephalography (EEG). The behavioral results showed that the high depressed mood (HD) group had significantly higher attentional distraction than the low depressed mood (LD) group. EEG results showed that 1) the beta power (especially beta-2, 18-30 Hz) of the two groups differed in the medio-late part of the attentional distraction, with significantly lower power in the HD group than in the LD group; 2) the results of the correlation between beta-2 power and depression scores revealed a significant negative correlation. These results imply that beta-2 is a potential marker that may be sensitive to depressed mood during attentional processing, which was further supported by the classification results of the support vector machine (SVM) with 80.65% accuracy between the HD and LD groups.

Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

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.

Estimation of Discriminative Multimodal Brain Network Connectivity Using Message-Passing-Based Nonlinear Network Fusion

Effective estimation of brain network connectivity enables better unraveling of the extraordinary complexity interactions of brain regions and helps in auxiliary diagnosis of psychiatric disorders. Considering different modalities can provide comprehensive characterizations of brain connectivity, we propose the message-passing-based nonlinear network fusion (MP-NNF) algorithm to estimate multimodal brain network connectivity. In the proposed method, the initial functional and structural networks were computed from fMRI and DTI separately. Then, we update every unimodal network iteratively, making it more similar to the others in every iteration, and finally converge to one unified network. The estimated brain connectivities integrate complementary information from multiple modalities while preserving their original structure, by adding the strong connectivities present in unimodal brain networks and eliminating the weak connectivities. The effectiveness of the method was evaluated by applying the learned brain connectivity for the classification of major depressive disorder (MDD). Specifically, 82.18% classification accuracy was achieved even with the simple feature selection and classification pipeline, which significantly outperforms the competing methods. Exploration of brain connectivity contributed to MDD identification suggests that the proposed method not only improves the classification performance but also was sensitive to critical disease-related neuroimaging biomarkers.

Altered whole-brain resting-state functional connectivity and brain network topology in typhoon-related post-traumatic stress disorder

Background

Altered resting-state functional connectivity has been found in patients with post-traumatic stress disorder (PTSD). However, the alteration of resting-state functional connectivity at whole-brain level in typhoon-traumatized individuals with PTSD remains largely unknown.

Objectives

To investigate changes in whole-brain resting-state functional connectivity and brain network topology in typhoon-traumatized subjects with and without PTSD.

Design

Cross-sectional study.

Methods

Twenty-seven patients with typhoon-related PTSD, 33 trauma-exposed controls (TEC), and 30 healthy controls (HC) underwent resting-state functional MRI scanning. The whole brain resting-state functional connectivity network was constructed based on the automated anatomical labeling atlas. The graph theory method was used to analyze the topological properties of the large-scale resting-state functional connectivity network. Whole-brain resting-state functional connectivity and the topological network property were compared by analyzing the variance.

Results

There was no significant difference in the area under the curve of γ, λ, σ, global efficiency, and local efficiency among the three groups. The PTSD group showed increased dorsal cingulate cortex (dACC) resting-state functional connectivity with the postcentral gyrus (PoCG) and paracentral lobe and increased nodal betweenness centrality in the precuneus relative to both control groups. Compared with the PTSD and HC groups, the TEC group showed increased resting-state functional connectivity between the hippocampus and PoCG and increased connectivity strength in the putamen. In addition, compared with the HC group, both the PTSD and TEC groups showed increased connectivity strength and nodal efficiency in the insula.

Conclusion

Aberrant resting-state functional connectivity and topology were found in all trauma-exposed individuals. These findings broaden our knowledge of the neuropathological mechanisms of PTSD.

Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

Gray matter volume reduction in orbitofrontal cortex correlated with plasma glial cell line-derived neurotrophic factor (GDNF) levels within major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a severe mental disorder characterized by reduced gray matter volume (GMV). To date, the pathogenesis of MDD remains unclear, but neurotrophic factors play an essential role in the pathophysiological alterations of MDD during disease development. In particular, plasma glial cell line-derived neurotrophic factor (GDNF) has been suggested as a potential biomarker that may be associated with disease activity and neurological progression in MDD. Our study investigated whether plasma GDNF levels in MDD patients and healthy controls (HCs) are correlated with GMV alterations.

METHODS

We studied 54 MDD patients and 48 HCs. The effect of different diagnoses on whole-brain GMV was investigated using ANOVA (Analysis of Variance). The threshold of significance was p < 0.05, and Gaussian random-field (GRF) correction for error was used. All analyses were controlled for covariates such as ethnicity, handedness, age, and gender that could affect GMV.

RESULT

Compared with the HC group, the GMV in the MDD group was significantly reduced in the right inferior orbitofrontal cortex (OFC), and plasma GDNF levels were significantly higher in the MDD group than in the HC group. In the right inferior OFC, the GDNF levels were positively correlated with GMV reduction in the MDD group, whereas in the HC group, a negative correlation was observed between GDNF levels and GMV reduction.

CONCLUSION

Although increased production of GDNF in MDD may help repair neural damage in brain regions associated with brain disease, its repairing effects may be interfered with and hindered by underlying neuroinflammatory processes.

Individualized prediction of consummatory anhedonia from functional connectome in major depressive disorder

BACKGROUND

Anhedonia is a key symptom of major depressive disorder (MDD) and other psychiatric diseases. The neural basis of anhedonia has been widely examined, yet the interindividual variability in neuroimaging biomarkers underlying individual-specific symptom severity is not well understood.

METHODS

To establish an individualized prediction model of anhedonia, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity profiles of MDD patients.

RESULTS

The CPM can successfully and reliably predict individual consummatory but not anticipatory anhedonia. The predictive model mainly included salience network (SN), frontoparietal network (FPN), default mode network (DMN), and motor network. Importantly, subsequent computational lesion prediction and consummatory-specific model prediction revealed that connectivity of the SN with DMN and FPN is essential and specific for the prediction of consummatory anhedonia.

CONCLUSIONS

This study shows that brain functional connectivity, especially the connectivity of SN-FPN and SN-DMN, can specifically predict individualized consummatory anhedonia in MDD. These findings suggest the potential of functional connectomes for the diagnosis and prognosis of anhedonia in MDD and other disorders.

Brain connectomes in youth at risk for serious mental illness: an exploratory analysis

BACKGROUND

Identifying early biomarkers of serious mental illness (SMI)-such as changes in brain structure and function-can aid in early diagnosis and treatment. Whole brain structural and functional connectomes were investigated in youth at risk for SMI.

METHODS

Participants were classified as healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9) based on clinical assessments. Imaging data was collected from two sites. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers derived from constrained spherical deconvolution of the diffusion tensor imaging (DTI) scans, and from the correlations between brain regions measured with resting state functional magnetic resonance imaging (fMRI) data.

RESULTS

Linear mixed effects analysis and analysis of covariance revealed no significant differences between groups in global or nodal metrics after correction for multiple comparisons. A follow up machine learning analysis broadly supported the findings. Several non-overlapping frontal and temporal network differences were identified in the structural and functional connectomes before corrections.

CONCLUSIONS

Results suggest significant brain connectome changes in youth at transdiagnostic risk may not be evident before illness onset.

The effect of second-generation antidepressant treatment on the attention and mental processing speed of patients with major depressive disorder: A meta-analysis study with structural equation models

Major depressive disorder (MDD) has been linked to attention and mental processing speed deficits that can be improved after pharmacological treatment. However, it is unclear whether a class of antidepressants is more effective than others to ameliorate these deficits in MDD. Additionally, the possible effects of clinical and demographic variables on improving MDD attention and processing speed deficits after antidepressant treatment are unknown. We aimed to study the possible neuropsychological effects of second-generation antidepressant classes on the attention and processing speed of MDD patients and the potential influences of clinical and demographic variables as moderators of these effects using a meta-analytic approach. Twenty-five papers were included in our study. A structural equation model meta-analysis was performed. The improvement of attention and processing speed after pharmacological treatment is clinically relevant but incomplete. Selective serotonin reuptake inhibitors (SSRIs) and dual inhibitors are the drugs causing the greatest improvement in the processing speed of MDD patients. Antidepressant class is an important variable linked to processing speed improvement after MDD treatment. However, the degree of improvement in both cognitive functions is strongly influenced by some clinical and demographic variables of depressed patients, such are age and education of the MDD patients, the duration of the antidepressant treatment, and the depression status of the patients.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

LIMITATIONS

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

Prefrontal tDCS Attenuates Self-Referential Attentional Deployment: A Mechanism Underlying Adaptive Emotional Reactivity to Social-Evaluative Threat

Social-evaluative threat (SET) - a situation in which one could be negatively evaluated by others - elicits profound (psycho)physiological reactivity which, if chronically present and not adaptively regulated, has deleterious effects on mental and physical health. Decreased self-awareness and increased other-awareness are understood to be an adaptive response to SET. Attentional deployment - the process of selectively attending to certain aspects of emotional stimuli to modulate emotional reactivity - is supported by fronto-parietal and fronto-limbic networks, with the dorsolateral prefrontal cortex being a central hub. The primary aim of the current study was to investigate the effects of active (versus sham) prefrontal transcranial direct current stimulation (tDCS) on self and other-attentional deployment during the exposure to a SET context. Seventy-four female participants received active or sham tDCS and were subsequently exposed to a rigged social feedback paradigm. In this paradigm a series of social evaluations were presented together with a photograph of the supposed evaluator and a self- photograph of the participant, while gaze behavior (time to first fixation, total fixation time) and skin conductance responses (SCRs; a marker of emotional reactivity) were measured. For half of the evaluations, participants could anticipate the valence (negative or positive) of the evaluation a priori. Analyses showed that participants receiving active tDCS were (a) slower to fixate on their self-photograph, (b) spent less time fixating on their self-photograph, and (c) spent more time fixating on the evaluator photograph. During unanticipated evaluations, active tDCS was associated with less time spent fixating on the evaluation. Furthermore, among those receiving active tDCS, SCRs were attenuated as a function of slower times to fixate on the self-photograph. Taken together, these results suggest that in a context of SET, prefrontal tDCS decreases self-attention while increasing other-attention, and that attenuated self-referential attention specifically may be a neurocognitive mechanism through which tDCS reduces emotional reactivity. Moreover, the results suggest that tDCS reduces vigilance toward stimuli that possibly convey threatening information, corroborating past research in this area.

Potential clinical value of circular RNAs as peripheral biomarkers for the diagnosis and treatment of major depressive disorder

Background

circular RNAs (circRNAs) are expressed abundantly in the brain and are implicated in the pathophysiology of neuropsychiatric disease. However, the potential clinical value of circRNAs in major depressive disorder (MDD) remains unclear.

Methods

RNA sequencing was conducted in whole-blood samples in a discovery set (7 highly homogeneous MDD patients and 7 matched healthy controls [HCs]). The differential expression of circRNAs was verified in an independent validation set. The interventional study was conducted to assess the potential effect of the antidepressive treatment on the circRNA expression.

Findings

in the validation set, compared with 52 HCs, significantly decreased circFKBP8 levels (Diff: -0.24; [95% CI -0.39 ~ -0.09]) and significantly elevated circMBNL1 levels (Diff: 0.37; [95% CI 0.09 ~ 0.64]) were observed in 53 MDD patients. The expression of circMBNL1 was negatively correlated with 24-item Hamilton Depression Scale (HAMD-24) scores in 53 MDD patients. A mediation model indicated that circMBNL1 affected HAMD-24 scores through a mediator, serum brain-derived neurotrophic factor. In 53 MDD patients, the amplitude of low-frequency fluctuations in the right orbital part middle frontal gyrus was positively correlated with circFKBP8 and circMBNL1 expression. Furthermore, the interventional study of 53 MDD patients demonstrated that antidepressive treatment partly increased circFKBP8 expression and the change in expression of circFKBP8 was predictive of further reduced HAMD-24 scores.

Interpretation

whole-blood circFKBP8 and circMBNL1 may be potential biomarkers for the diagnosis of MDD, respectively, and circFKBP8 may show great potential for the antidepressive treatment.

White Matter-Based Structural Brain Network of Major Depression

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

White-matter functional topology: a neuromarker for classification and prediction in unmedicated depression

Aberrant topological organization of brain connectomes underlies pathological mechanisms in major depressive disorder (MDD). However, accumulating evidence has only focused on functional organization in brain gray-matter, ignoring functional information in white-matter (WM) that has been confirmed to have reliable and stable topological organizations. The present study aimed to characterize the functional pattern disruptions of MDD from a new perspective-WM functional connectome topological organization. A case-control, cross-sectional resting-state functional magnetic resonance imaging study was conducted on both discovery [91 unmedicated MDD patients, and 225 healthy controls (HCs)], and replication samples (34 unmedicated MDD patients, and 25 HCs). The WM functional networks were constructed in 128 anatomical regions, and their global topological properties (e.g., small-worldness) were analyzed using graph theory-based approaches. At the system-level, ubiquitous small-worldness architecture and local information-processing capacity were detectable in unmedicated MDD patients but were less salient than in HCs, implying a shift toward randomization in MDD WM functional connectomes. Consistent results were replicated in an independent sample. For clinical applications, small-world topology of WM functional connectome showed a predictive effect on disease severity (Hamilton Depression Rating Scale) in discovery sample (r = 0.34, p = 0.001). Furthermore, the topologically-based classification model could be generalized to discriminate MDD patients from HCs in replication sample (accuracy, 76%; sensitivity, 74%; specificity, 80%). Our results highlight a reproducible topologically shifted WM functional connectome structure and provide possible clinical applications involving an optimal small-world topology as a potential neuromarker for the classification and prediction of MDD patients.

Deep phenotyping of attention impairments and the 'Inattention Biotype' in Major Depressive Disorder

BACKGROUND

Attention impairment is an under-investigated feature and diagnostic criterion of Major Depressive Disorder (MDD) that is associated with poorer outcomes. Despite increasing knowledge regarding mechanisms of attention in healthy adults, we lack a detailed characterization of attention impairments and their neural signatures in MDD.

METHODS

Here, we focus on selective attention and advance a deep multi-modal characterization of these impairments in MDD, using data acquired from n = 1008 patients and n = 336 age- and sex-matched healthy controls. Selective attention impairments were operationalized and anchored in a behavioral performance measure, assessed within a battery of cognitive tests. We sought to establish the accompanying neural signature using independent measures of functional magnetic resonance imaging (15% of the sample) and electroencephalographic recordings of oscillatory neural activity.

RESULTS

Greater impairment on the behavioral measure of selective attention was associated with intrinsic hypo-connectivity of the fronto-parietal attention network. Not only was this relationship specific to the fronto-parietal network unlike other large-scale networks; this hypo-connectivity was also specific to selective attention performance unlike other measures of cognition. Selective attention impairment was also associated with lower posterior alpha (8-13 Hz) power at rest and was related to more severe negative bias (frequent misidentifications of neutral faces as sad and lingering attention on sad faces), relevant to clinical features of negative attributions and brooding. Selective attention impairments were independent of overall depression severity and of worrying or sleep problems.

CONCLUSIONS

These results provide a foundation for the clinical translational development of objective markers and targeted therapeutics for attention impairment in MDD.

The concurrent disturbance of dynamic functional and structural brain connectome in major depressive disorder: the prefronto-insular pathway

BACKGROUND

Robust evidence has shown that abnormal function networks, particularly the salience network (SN), are observed in depressed patients. Although white matter structural connectivity may predict time-varying functional connectivity, including symptom phenotype, in psychiatric disorders, there is still a gap in elucidating the concurrent dynamic functional and structural connectivity profiles of the SN in depressed patients.

METHODS

We measured static and dynamic functional connectivity (FC) of the SN using resting-state fMRI BOLD time series in 76 subjects (21 with major depressive disorder (MDD), 27 with bipolar depression (BD), and 28 healthy controls (HC)). Hamilton Depression Scale total score was used to measure depression severity. Furthermore, we investigated the concurrent structural connectivity using diffusion kurtosis imaging (DKI)-based tractography.

RESULTS

Our findings suggested that in the presence of MDD, both structural and dynamic (but not static) FC were reduced in the SN, particularly affecting the left prefronto-insular pathways (L.aPFC-L.insula). MDD patients showed decreased connectivity variability within the SN compared with HC. The aberrant dynamic FC in the prefronto-insular pathways of the SN related to severity of depressive symptoms in MDD. Furthermore, compared with BD patients, those with MDD showed significantly decreased dynamic FC in the left prefronto-parietal system (L.aPFC-lateral parietal cortex).

LIMITATIONS

The generalizability of our findings is, to some extent, constrained by the small sample size.

CONCLUSIONS

The integrity of SN connectivity, particularly the prefronto-insular pathway, appears to be a crucial signature of MDD. The perturbed dynamic interaction of SN with prefrontal regions may underlie the clinical severity in depressed patients.

MRI Insights Into Adolescent Neurocircuitry-A Vision for the Future

Adolescence is the time of onset of many psychiatric disorders. Half of pediatric patients present with comorbid psychiatric disorders that complicate both their medical and psychiatric care. Currently, diagnosis and treatment decisions are based on symptoms. The field urgently needs brain-based diagnosis and personalized care. Neuroimaging can shed light on how aberrations in brain circuits might underlie psychiatric disorders and their development in adolescents. In this perspective article, we summarize recent MRI literature that provides insights into development of psychiatric disorders in adolescents. We specifically focus on studies of brain structural and functional connectivity. Ninety-six included studies demonstrate the potential of MRI to assess psychiatrically relevant constructs, diagnose psychiatric disorders, predict their development or predict response to treatment. Limitations of the included studies are discussed, and recommendations for future research are offered. We also present a vision for the role that neuroimaging may play in pediatrics and primary care in the future: a routine neuropsychological and neuropsychiatric imaging (NPPI) protocol for adolescent patients, which would include a 30-min brain scan, a quality control and safety read of the scan, followed by computer-based calculation of the structural and functional brain network metrics that can be compared to the normative data by the pediatrician. We also perform a cost-benefit analysis to support this vision and provide a roadmap of the steps required for this vision to be implemented.

Graph theoretical measures of the uncinate fasciculus subnetwork as predictors and correlates of treatment response in a transdiagnostic psychiatric cohort

The internalizing psychopathologies (IP) are a highly prevalent group of disorders for which little data exists to guide treatment selection. We examine whether graph theoretical metrics from white matter connectomes may serve as biomarkers of disease and predictors of treatment response. We focus on the uncinate fasciculus subnetwork, which has been previously implicated in these disorders. We compared baseline graph measures from a transdiagnostic IP cohort with controls. Patients were randomized to either SSRI or cognitive behavioral therapy and we determined if graph theory metrics change following treatment, and whether these changes correlated with treatment response. Lastly, we investigated whether baseline metrics correlated with treatment response. Several baseline nodal graph metrics differed at baseline. Of note, right amygdala betweenness centrality was increased in patients relative to controls. In addition, white matter integrity of the uncinate fasciculus was decreased at baseline in patients versus controls. The SSRI and CBT cohorts had increased left frontal superior orbital betweenness centrality and left frontal medial orbital clustering coefficient, respectively, suggesting the presence of treatment specific neural correlates of treatment response. This study provides insight on shared white matter network features of IPs and elucidates potential biomarkers of treatment response that may be modality-specific.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSION

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

Paying attention to attention in depression

Attention is the gate through which sensory information enters our conscious experiences. Oftentimes, patients with major depressive disorder (MDD) complain of concentration difficulties that negatively impact their day-to-day function, and these attention problems are not alleviated by current first-line treatments. In spite of attention's influence on many aspects of cognitive and emotional functioning, and the inclusion of concentration difficulties in the diagnostic criteria for MDD, the focus of depression as a disease is typically on mood features, with attentional features considered less of an imperative for investigation. Here, we summarize the breadth and depth of findings from the cognitive neurosciences regarding the neural mechanisms supporting goal-directed attention in order to better understand how these might go awry in depression. First, we characterize behavioral impairments in selective, sustained, and divided attention in depressed individuals. We then discuss interactions between goal-directed attention and other aspects of cognition (cognitive control, perception, and decision-making) and emotional functioning (negative biases, internally-focused attention, and interactions of mood and attention). We then review evidence for neurobiological mechanisms supporting attention, including the organization of large-scale neural networks and electrophysiological synchrony. Finally, we discuss the failure of current first-line treatments to alleviate attention impairments in MDD and review evidence for more targeted pharmacological, brain stimulation, and behavioral interventions. By synthesizing findings across disciplines and delineating avenues for future research, we aim to provide a clearer outline of how attention impairments may arise in the context of MDD and how, mechanistically, they may negatively impact daily functioning across various domains.

Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT)

Purpose

Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with cognitive behavioral therapy (CBT), a first-line treatment for adolescent MDD, and face devastating consequences of increased risk of suicide and many negative health outcomes. “Who will improve with CBT?” is a crucial question that remains unanswered, and treatment planning for adolescent depression remains biologically unguided. The purpose of this study was to utilize machine learning applied to patients' brain imaging data in order to help predict depressive symptom reduction with CBT.

Methods

We applied supervised machine learning to diffusion MRI-based structural connectome data in order to predict symptom reduction in 30 depressed adolescents after three months of CBT. A set of 21 attributes was chosen, including the baseline depression score, age, gender, two global network properties, and node strengths of brain regions previously implicated in depression. The practical and robust J48 pruned tree classifier was utilized with a 10-fold cross-validation.

Results

The classification resulted in an 83% accuracy of predicting depressive symptom reduction. The resulting tree of size seven with only three attributes highlights the role of the right thalamus in predicting depressive symptom reduction with CBT. Additional analysis showed a significant negative correlation between the change in the depressive symptoms and the node strength of the right thalamus.

Conclusions

Our results demonstrate that a machine learning algorithm that exclusively uses structural connectome data and the baseline depression score can predict with a high accuracy depressive symptom reduction in adolescent MDD with CBT. This knowledge can help improve treatment planning for adolescent depression.

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Machine learning predicted symptom reduction in depressed teens with 83% accuracy.

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Resulting prunned classification tree size was 7, with only 3 attributes.

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Change in depression symptoms correlated with node strength of the right thalamus.

Topologically state-independent and dependent functional connectivity patterns in current and remitted depression

OBJECTIVE

Identification of state-independent and -dependent neural biomarkers may provide insight into the pathophysiology and effective treatment of major depressive disorder (MDD), therefore we aimed to investigate the state-independent and -dependent topological alterations of MDD.

METHOD

Brain resting-state functional magnetic resonance imaging (fMRI) data were acquired from 59 patients with unmedicated first episode current MDD (cMDD), 48 patients with remitted MDD (rMDD) and 60 demographically matched healthy controls (HCs). Using graph theory, we systematically studied the topological organization of their whole-brain functional networks at the global and nodal level.

RESULTS

At a global level, both patient groups showed decreased normalized clustering coefficient in relative to HCs. On a nodal level, both patient groups showed decreased nodal centrality, predominantly in cortex-mood-regulation brain regions including the dorsolateral prefrontal cortex, posterior parietal cortex and posterior cingulate cortex. By comparison to cMDD patients, rMDD group had a higher nodal centrality in right parahippocampal gyrus.

LIMITATIONS

The present study, an exploratory analysis, may require further confirmation with task-based and experimental studies.

CONCLUSIONS

Deficits in the topological organization of the whole brain and cortex-mood-regulation brain regions in both rMDD and cMDD represent state-independent biomarkers.

Connectome analysis of functional and structural hemispheric brain networks in major depressive disorder

Neuroimaging studies have shown topological disruptions of both functional and structural whole-brain networks in major depressive disorder (MDD). This study examined common and specific alterations between these two types of networks and whether the alterations were differentially involved in the two hemispheres. Multimodal MRI data were collected from 35 MDD patients and 35 healthy controls, whose functional and structural hemispheric networks were constructed, characterized, and compared. We found that functional brain networks were profoundly altered at multiple levels, while structural brain networks were largely intact in patients with MDD. Specifically, the functional alterations included decreases in intra-hemispheric (left and right) and inter-hemispheric (heterotopic) functional connectivity; decreases in local, global and normalized global efficiency for both hemispheric networks; increases in normalized local efficiency for the left hemispheric networks; and decreases in intra-hemispheric integration and inter-hemispheric communication in the dorsolateral superior frontal gyrus, anterior cingulate gyrus and hippocampus. Regarding hemispheric asymmetry, largely similar patterns were observed between the functional and structural networks: the right hemisphere was over-connected and more efficient than the left hemisphere globally; the occipital and partial regions exhibited leftward asymmetry, and the frontal and temporal sites showed rightward lateralization with regard to regional connectivity profiles locally. Finally, the functional-structural coupling of intra-hemispheric connections was significantly decreased and correlated with the disease severity in the patients. Overall, this study demonstrates modality- and hemisphere-dependent and invariant network alterations in MDD, which are helpful for understanding elaborate and characteristic patterns of integrative dysfunction in this disease.

Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis

Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome.

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Delirium is consistently associated with functional network impairments.

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Risk factors are associated with lower structural connectivity strength.

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Risk factors are associated with a less efficient structural network organization.

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Structural impairments make the functional network more vulnerable to deterioration.

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Functional network disintegration seems to be a final common pathway for delirium.

Rehabilitative compensatory mechanism of hierarchical subnetworks in major depressive disorder: A longitudinal study across multi-sites

BACKGROUND

Brain structural connectome comprise of a minority of efficiently interconnected rich club nodes that are regarded as 'high-order regions'. The remission of major depressive disorder (MDD) in response to selective serotonin reuptake inhibitor (SSRI) treatment could be investigated by the hierarchical structural connectomes' alterations of subnetworks.

METHODS

Fifty-five MDD patients who achieved remission underwent diffusion tensors imaging (DTI) scanning from 3 cohorts before and after 8-weeks antidepressant treatment. Five hierarchical subnetworks namely, rich, local, feeder, rich-feeder and feeder-local, were constructed according to the different combinations of connections and nodes as defined by rich club architecture. The critical treatment-related subnetwork pattern was explored by multivariate pattern analysis with support vector machine to differ the pre-/post-treatment patients. Then, relationships between graph metrics of discriminative subnetworks/ nodes and clinical variables were further explored.

RESULTS

The feeder-local subnetwork presented the most discriminative power in differing pre-/post- treatment patients, while the rich-feeder subnetwork had the highest discriminative power when comparing pre-treatment patients and controls. Furthermore, based on the feeder connection, which indicates the information transmission between the core and non-core architectures of brain networks, its topological measures were found to be significantly correlated with the reduction rate of 17-item Hamilton Rating Scale for Depression.

CONCLUSION

Although pathological lesion on MDD relied on abnormal core organization, disease remission was association with the compensation from non-core organization. These results suggested that the dysfunctions arising from hierarchical subnetworks are compensated by increased information interactions between core brain regions and functionally diverse regions.

The Shared and Distinct White Matter Networks Between Drug-Naive Patients With Obsessive-Compulsive Disorder and Schizophrenia

Background: Obsessive-compulsive disorder (OCD) and schizophrenia (SZ) as two severe mental disorders share many clinical symptoms, and have a tight association on the psychopathological level. However, the neurobiological substrates between these two diseases remain unclear. To the best of our knowledge, no study has directly compared OCD with SZ from the perspective of white matter (WM) networks. Methods: Graph theory and network-based statistic methods were applied to diffusion MRI to investigate and compare the WM topological characteristics among 29 drug-naive OCDs, 29 drug-naive SZs, and 65 demographically-matched healthy controls (NC). Results: Compared to NCs, OCDs showed the alterations of nodal efficiency and strength in orbitofrontal (OFG) and middle frontal gyrus (MFG), while SZs exhibited widely-distributed abnormalities involving the OFG, MFG, fusiform gyrus, heschl gyrus, calcarine, lingual gyrus, putamen, and thalamus, and most of these regions also showed a significant difference from OCDs. Moreover, SZs had significantly fewer connections in striatum and visual/auditory cortices than OCDs. The right putamen consistently showed significant differences between both disorders on nodal characteristics and structural connectivity. Conclusions: SZ and OCD present different level of anatomical impairment and some distinct topological patterns, and the former has more serious and more widespread disruptions. The significant differences between both disorders are observed in many regions involving the frontal, temporal, occipital, and subcortical regions. Particularly, putamen may serve as a potential imaging marker to distinguish these two disorders and may be the key difference in their pathological changes.

Structural networks analysis for depression combined with graph theory and the properties of fiber tracts via diffusion tensor imaging

Previous studies have suggested that major depressive disorder was associated with topological properties of impaired white matter. However, most related studies only use one property of nerve fibers to construct whole-brain structural brain network. Considering white matter changes variously, We hypothesized whether the alternations of white matter topological properties could reflect different impairment of white matter integrity. In addition, it is still unknown whether impaired integrity of the white matter fiber tracts has relationship with abnormal topological properties in MDD. This study investigated the impaired white matter by using graph theoretic analyses in a cohort of 37 MDD patients and 38 matched control subjects. In addition, we further investigated fiber tracts differences in three interregional connectivity matrixes of significant different topological regions in MDD. Our graph theoretic analyses demonstrated that 7 different regions were observed for the local measures in patients with MDD compared with control groups. These regions were the central nodes of cortical-limbic network, frontal-cingulate network, default mode network (DMN), cognitive control network(CCN)and affective network (AN). In addition, two impaired white matter pathways which included inferior longitudinal fasciculus (ILF) and cingulum were observed in MDD using fiber tracts analysis. We speculate impaired integrity of ILF is due to the alternations in the number of axons or myelination. The results further demonstrated that the number of fiber tracts of anterior cingulum was associated with the depression scores in MDD.

Accelerated intermittent theta burst stimulation in major depression induces decreases in modularity: A connectome analysis

Accelerated intermittent theta burst stimulation (aiTBS) is a noninvasive neurostimulation technique that shows promise for improving clinical outcome in patients suffering from treatment-resistant depression (TRD). Although it has been suggested that aiTBS may evoke beneficial neuroplasticity effects in neuronal circuits, the effects of aiTBS on brain networks have not been investigated until now. Fifty TRD patients were enrolled in a randomized double-blind sham-controlled crossover trial involving aiTBS, applied to the left dorsolateral prefrontal cortex. Diffusion-weighted MRI data were acquired at each of three time points (T1 at baseline; T2 after the first week of real/sham aiTBS stimulation; and T3 after the second week of treatment). Graph analysis was performed on the structural connectivity to examine treatment-related changes in the organization of brain networks. Changes in depression severity were assessed using the Hamilton Depression Rating Scale (HDRS). Baseline data were compared with 60 healthy controls. We observed a significant reduction in depression symptoms over time (p < 0.001). At T1, both TRD patients and controls exhibited a small-world topology in their white matter networks. More importantly, the TRD patients demonstrated a significantly shorter normalized path length (p AUC = 0.01), and decreased assortativity (p AUC = 0.035) of the structural networks, compared with the healthy control group. Within the TRD group, graph analysis revealed a less modular network configuration between T1 and T2 in the TRD group who received real aiTBS stimulation in the first week (p < 0.013). Finally, there were no significant correlations between changes on HDRS scores and reduced modularity. Application of aiTBS in TRD is characterized by reduced modularity, already evident 4 days after treatment. These findings support the potential clinical application of such noninvasive brain stimulation in TRD.

Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders

Background

Brain connectome research based on graph theoretical analysis shows that small-world topological properties play an important role in the structural and functional alterations observed in patients with psychiatric disorders. However, the reported global topological alterations in small-world properties are controversial, are not consistently conceptualized according to agreed-upon criteria, and are not critically examined for consistent alterations in patients with each major psychiatric disorder.

Methods

Based on a comprehensive PubMed search, we systematically reviewed studies using noninvasive neuroimaging data and graph theoretical approaches for 6 major psychiatric disorders: schizophrenia, major depressive disorder (MDD), attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), obsessive–compulsive disorder (OCD) and posttraumatic stress disorder (PTSD). Here, we describe the main patterns of altered small-world properties and then systematically review the evidence for these alterations in the structural and functional connectome in patients with these disorders.

Results

We selected 40 studies of schizophrenia, 33 studies of MDD, 5 studies of ADHD, 5 studies of BD, 7 studies of OCD and 5 studies of PTSD. The following 4 patterns of altered small-world properties are defined from theperspectives of segregation and integration: "regularization," "randomization," "stronger small-worldization" and "weaker small-worldization." Although more differences than similarities are noted in patients with these disorders, a prominent trend is the structural regularization versus functional randomization in patients with schizophrenia.

Limitations

Differences in demographic and clinical characteristics, preprocessing steps and analytical methods can produce contradictory results, increasing the difficulty of integrating results across different studies.

Conclusion

Four psychoradiological patterns of altered small-world properties are proposed. The analysis of altered smallworld properties may provide novel insights into the pathophysiological mechanisms underlying psychiatric disorders from a connectomic perspective. In future connectome studies, the global network measures of both segregation and integration should be calculated to fully evaluate altered small-world properties in patients with a particular disease.

A computational network control theory analysis of depression symptoms

Rumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

A brain network model for depression: From symptom understanding to disease intervention

Understanding the neural substrates of depression is crucial for diagnosis and treatment. Here, we review recent studies of functional and effective connectivity in depression, in terms of functional integration in the brain. Findings from these studies, including our own, point to the involvement of at least four networks in patients with depression. Elevated connectivity of a ventral limbic affective network appears to be associated with excessive negative mood (dysphoria) in the patients; decreased connectivity of a frontal-striatal reward network has been suggested to account for loss of interest, motivation, and pleasure (anhedonia); enhanced default mode network connectivity seems to be associated with depressive rumination; and diminished connectivity of a dorsal cognitive control network is thought to underlie cognitive deficits especially ineffective top-down control of negative thoughts and emotions in depressed patients. Moreover, the restoration of connectivity of these networks-and corresponding symptom improvement-following antidepressant treatment (including medication, psychotherapy, and brain stimulation techniques) serves as evidence for the crucial role of these networks in the pathophysiology of depression.

Impaired Prefrontal-Amygdala Pathway, Self-Reported Emotion, and Erection in Psychogenic Erectile Dysfunction Patients With Normal Nocturnal Erection

Background: Neuroimaging studies have demonstrated that the prefrontal cortex and amygdala play an important role in sexual arousal (SA). However, little is known about the interactions between the prefrontal and cortex amygdala, which mediate the cognitive regulation of emotion and SA. Objective: We seek to determine whether nocturnal erection of psychogenic erectile dysfunction (pED) patients are normal and whether there are changes of topological organization in the prefrontal-amygdala pathway of brain network in pED. In addition, whether there are correlations between network property changes and self-reported emotion and erection. Design, setting, and participants: We used the RigiScan device to evaluate erectile function of patients and employed diffusion MRI and graph theory to construct brain networks of 21 pED patients and 24 healthy controls. Outcome measurements and statistical analysis: We considered four nodal metrics and their asymmetry scores, and nocturnal penile tumescence (NPT) parameters, to evaluate the topological properties of brain networks of pED and their relationships with the impaired self-reported emotion and erection. Results and limitations: All the pED patients showed normal nocturnal penile erection, however impaired self-reported erection and negative emotion. In addition, patients showed lower connectivity degree and strength in the left prefrontal-amygdala pathway. We also found that pED exhibited lower leftward asymmetry in the inferior frontal gyrus. Furthermore, patients showed more hub regions and fewer pivotal connections. Moreover, the degree of the left amygdala of pED showed significantly negative correlation with the self-reported erection and positive correlation with the self-reported negative emotion. Conclusions: Together, these results suggest normal nocturnal erection in pED. However, abnormalities of brain network organization in pED, particularly in the left prefrontal-amygdala pathway, are associated with the impaired self-reported erection and negative emotion.

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

Bipolar disorder (BD) and major depressive disorder (MDD) are associated with different brain functional and structural abnormalities, but BD is hard to distinguish from MDD until the first manic or hypomanic episode. The aim of this study was to examine whether the topological properties of the brain structural network could be used to differentiate BD from MDD patients before their first manic/hypomanic episode. Diffusion tensor images were collected from 80 MDD patients and 53 healthy controls (HCs); 78 patients completed the follow-up study lasting 7 years. Among them, 12 patients were converted to BD and 64 patients remained MDD. Topological properties of the brain structural networks at baseline were compared among patients who converted to BD, patients who did not develop BD, and HCs. Patients who converted to BD displayed reduced nodal local efficiency in the left inferior frontal gyrus(IFG) compared with HCs and patients who did not convert to BD. There was no significant difference in the nodal global efficiency among the three groups. The findings suggest that the nodal local efficiency in the left IFG could serve as a potential biomarker to predict the conversion of MDD to BD before the occurrence of the first manic or hypomanic episode.

Identify changes of brain regional homogeneity in early and later adult onset patients with first-episode depression using resting-state fMRI

Objective

Previous work exhibited different brain grey matter volume (GMV) changes between patients with early adult onset depression (EOD, age 18–29) and later adult onset depression (LOD, age 30–44) by using 30-year-old as the cut-off age. To identify whether regional homogeneity (ReHo) changes are also different between EOD and LOD by using same cut-off age, we used resting-state functional magnetic resonance imaging (fMRI) to detect the abnormal ReHo between patients with EOD and LOD in the present study.

Methods

Resting-state fMRI scans of 58 patients with EOD, 62 patients with LOD, 60 young healthy controls (HC), and 52 old HC were obtained. The ReHo approach was used to analyze the images.

Results

The ANOVA analysis revealed that the ReHo values in the frontoparietal, occipital, and cerebellar regions were significantly different among the four groups. Relative to patients with LOD, patients with EOD displayed significantly increased ReHo in the left precuneus, and decreased ReHo in the right fusiform. The ReHo values in the left precuneus and the right fusiform had no significant correlation with the score of the depression rating scale or illness duration in both patient subgroups. Compared to young HC, patients with EOD showed significantly increased ReHo in the right frontoparietal regions and the right calcarine. Furthermore, the increased ReHo in the right frontoparietal regions, right insula and left hippocampus, and decreased ReHo in the left inferior occipital gyrus, right middle occipital gyrus, left calcarine, and left supplementary motor area were observed in patients with LOD when compared to old HC.

Conclusions

The ReHo of brain areas that were related to mood regulation was changed in the first-episode, drug-naive adult patients with MDD. Adult patients with EOD and LOD exhibited different ReHo abnormalities relative to each age-matched comparison group, suggesting that depressed adult patients with different age-onset might have different pathological mechanism.

Microstructural brain abnormalities in medication-free patients with major depressive disorder: a systematic review and meta-analysis of diffusion tensor imaging

BACKGROUND

Multiple meta-analyses of diffusion tensor imaging (DTI) studies have reported impaired white matter integrity in patients with major depressive disorder (MDD). However, owing to inclusion of medicated patients in these studies, it is difficult to conclude whether these reported alterations are associated with MDD or confounded by medication effects. A meta-analysis of DTI studies on medication-free (medication-naive and medication washout) patients with MDD would therefore be necessary to disentangle MDD-specific effects.

METHODS

We analyzed white matter alterations between medication-free patients with MDD and healthy controls using anisotropic effect size-signed differential mapping (AES-SDM). We used DTI query software for fibre tracking.

RESULTS

Both pooled and subgroup meta-analyses in medication washout patients showed robust fractional anisotropy (FA) reductions in white matter of the right cerebellum hemispheric lobule, body of the corpus callosum (CC) and bilateral superior longitudinal fasciculus III (SLF III), whereas FA reductions in the genu of the CC and right anterior thalamic projections were seen in only medication-naive patients. Fibre tracking showed that the main tracts with observed FA reductions included the right cerebellar tracts, body of the CC, bilateral SLF III and arcuate fascicle.

LIMITATIONS

The analytic techniques, patient characteristics and clinical variables of the included studies were heterogeneous; we could not exclude the effects of nondrug therapies owing to a lack of data.

CONCLUSION

By excluding the confounding influences of current medication status, findings from the present study may provide a better understanding of the underlying neuropathology of MDD.

Brain connectivity networks and longitudinal trajectories of depression symptoms in adolescence

High levels of depression during adolescence may contribute to the risk for future depression later in life. This study examined the relationship between the developmental timing of depressive symptoms, and brain structural outcomes in late adolescence. In a prior work, we examined longitudinal trajectories of depressive symptoms in 243 adolescents (121 males and 122 females), and identified four subgroups: a normative group with stable low levels of depression, two groups with declining symptoms, and one group with increasing symptoms. For the current paper, diffusion-weighted MRI images were acquired at the final wave of the study, and used to perform white matter tractography and brain network analysis. The four depression trajectory groups were tested for differences in brain connectivity variables. This revealed differences in several frontal and temporal regions. The groups that had experienced elevated depression symptoms in early adolescence differed from the normative group in a greater number of areas than the group who had experienced depression later. Affected tracts corresponded to areas of white matter that are still maturing during this period, particularly frontolimbic regions. These findings support the proposition that the timing and duration of depression symptoms during adolescence are associated with brain structural outcomes.

Childhood maltreatment is associated with alteration in global network fiber-tract architecture independent of history of depression and anxiety

Childhood maltreatment is a major risk factor for psychopathology. It is also associated with alterations in the network architecture of the brain, which we hypothesized may play a significant role in the development of psychopathology. In this study, we analyzed the global network architecture of physically healthy unmedicated 18-25 year old subjects (n=262) using diffusion tensor imaging (DTI) MRI and tractography. Anatomical networks were constructed from fiber streams interconnecting 90 cortical or subcortical regions for subjects with no-to-low (n=122) versus moderate-to-high (n=140) exposure to maltreatment. Graph theory analysis revealed lower degree, strength, global efficiency, and maximum Laplacian spectra, higher pathlength, small-worldness and Laplacian skewness, and less deviation from artificial networks in subjects with moderate-to-high exposure to maltreatment. On balance, local clustering was similar in both groups, but the different clusters were more strongly interconnected in the no-to-low exposure group. History of major depression, anxiety and attention deficit hyperactivity disorder did not have a significant impact on global network measures over and above the effect of maltreatment. Maltreatment is an important factor that needs to be taken into account in studies examining the relationship between network differences and psychopathology.

Anomalous single-subject based morphological cortical networks in drug-naive, first-episode major depressive disorder

Major depressive disorder (MDD) has been associated with disruptions in the topological organization of brain morphological networks in group-level data. Such disruptions have not yet been identified in single-patients, which is needed to show relations with symptom severity and to evaluate their potential as biomarkers for illness. To address this issue, we conducted a cross-sectional structural brain network study of 33 treatment-naive, first-episode MDD patients and 33 age-, gender-, and education-matched healthy controls (HCs). Weighted graph-theory based network models were used to characterize the topological organization of brain networks between the two groups. Compared with HCs, MDD patients exhibited lower normalized global efficiency and higher modularity in their whole-brain morphological networks, suggesting impaired integration and increased segregation of morphological brain networks in the patients. Locally, MDD patients exhibited lower efficiency in anatomic organization for transferring information predominantly in default-mode regions including the hippocampus, parahippocampal gyrus, precuneus and superior parietal lobule, and higher efficiency in the insula, calcarine and posterior cingulate cortex, and in the cerebellum. Morphological connectivity comparisons revealed two subnetworks that exhibited higher connectivity strength in MDD mainly involving neocortex-striatum-thalamus-cerebellum and thalamo-hippocampal circuitry. MDD-related alterations correlated with symptom severity and differentiated individuals with MDD from HCs with a sensitivity of 87.9% and specificity of 81.8%. Our findings indicate that single subject grey matter morphological networks are often disrupted in clinically relevant ways in treatment-naive, first episode MDD patients. Circuit-specific changes in brain anatomic network organization suggest alterations in the efficiency of information transfer within particular brain networks in MDD. Hum Brain Mapp 38:2482-2494, 2017. © 2017 Wiley Periodicals, Inc.

Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder

Background:

Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients.

Methods:

The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory.

Results:

Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions.

Conclusions:

All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network.

Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: a theoretical review of the evidence and future directions for clinical translation

Complex emotional, cognitive and self-reflective functions rely on the activation and connectivity of large-scale neural circuits. These circuits offer a relevant scale of focus for conceptualizing a taxonomy for depression and anxiety based on specific profiles (or biotypes) of neural circuit dysfunction. Here, the theoretical review first outlines the current consensus as to what constitutes the organization of large-scale circuits in the human brain identified using parcellation and meta-analysis. The focus is on neural circuits implicated in resting reflection (default mode), detection of "salience," affective processing ("threat" and "reward"), "attention," and "cognitive control." Next, the current evidence regarding which type of dysfunctions in these circuits characterize depression and anxiety disorders is reviewed, with an emphasis on published meta-analyses and reviews of circuit dysfunctions that have been identified in at least two well-powered case:control studies. Grounded in the review of these topics, a conceptual framework is proposed for considering neural circuit-defined "biotypes." In this framework, biotypes are defined by profiles of extent of dysfunction on each large-scale circuit. The clinical implications of a biotype approach for guiding classification and treatment of depression and anxiety is considered. Future research directions will develop the validity and clinical utility of a neural circuit biotype model that spans diagnostic categories and helps to translate neuroscience into clinical practice in the real world.

Alternations of White Matter Structural Networks in First Episode Untreated Major Depressive Disorder with Short Duration

It is crucial to explore the pathogenesis of major depressive disorder (MDD) at the early stage for the better diagnostic and treatment strategies. It was suggested that MDD might be involving in functional or structural alternations at the brain network level. However, at the onset of MDD, whether the whole brain white matter (WM) alterations at network level are already evident still remains unclear. In the present study, diffusion MRI scanning was adopt to depict the unique WM structural network topology across the entire brain at the early stage of MDD. Twenty-one first episode, short duration (<1 year) and drug-naïve depression patients, and 25 healthy control (HC) subjects were recruited. To construct the WM structural network, atlas-based brain regions were used for nodes, and the value of multiplying fiber number by the mean fractional anisotropy along the fiber bundles connected a pair of brain regions were used for edges. The structural network was analyzed by graph theoretic and network-based statistic methods. Pearson partial correlation analysis was also performed to evaluate their correlation with the clinical variables. Compared with HCs, the MDD patients had a significant decrease in the small-worldness (σ). Meanwhile, the MDD patients presented a significantly decreased subnetwork, which mainly involved in the frontal-subcortical and limbic regions. Our results suggested that the abnormal structural network of the orbitofrontal cortex and thalamus, involving the imbalance with the limbic system, might be a key pathology in early stage drug-naive depression. And the structural network analysis might be potential in early detection and diagnosis of MDD.

Abnormal dynamic community structure of the salience network in depression

PURPOSE

To detect the consecutive variations of the internetwork interactions over time, which helps to discover the underlying dysfunction of depressive disorders. Abnormal interactions of resting-state functional networks have been reported in depression. However, little is known regarding the dynamics of how these crucial networks interact and the disease-related dysfunction.

MATERIALS AND METHODS

Functional magnetic resonance imaging data at 3.0T in the resting state were acquired from 20 depressed patients and 20 healthy controls. Twelve resting-state networks were extracted by group-independent component analysis, and their interactions were calculated through a sliding windowed Granger causality model analysis. The acquired effective connectivity matrices were used to construct multislice networks with modular structures that were detected via a multislice community detection method.

RESULTS

No significant differences were observed in the modularity and total module numbers between the depressed patients and the healthy controls. The P values were 0.133 with a confidence interval (-0.0001 0.0093) and 0.136 with a confidence interval (-0.30 0.90), respectively. However, the depressed patients exhibited decreased flexibility of the salience network (SN) compared with the controls (P = 0.048, corrected, with a confidence interval 0.0068 0.066).

CONCLUSION

SN was inclined to participate less in the multiple brain functional modules across the resting time in depression, and infrequently changed its modular allegiance. These findings support the potential importance of the SN in the neuropathological mechanism of depression.

LEVEL OF EVIDENCE

1 J. Magn. Reson. Imaging 2017;45:1135-1143.

Disorganized cortical thickness covariance network in major depressive disorder implicated by aberrant hubs in large-scale networks

Major depressive disorder is associated with abnormal anatomical and functional connectivity, yet alterations in whole cortical thickness topology remain unknown. Here, we examined cortical thickness in medication-free adult depression patients (n = 76) and matched healthy controls (n = 116). Inter-regional correlation was performed to construct brain networks. By applying graph theory analysis, global (i.e., small-worldness) and regional (centrality) topology was compared between major depressive disorder patients and healthy controls. We found that in depression patients, topological organization of the cortical thickness network shifted towards randomness, and lower small-worldness was driven by a decreased clustering coefficient. Consistently, altered nodal centrality was identified in the isthmus of the cingulate cortex, insula, supra-marginal gyrus, middle temporal gyrus and inferior parietal gyrus, all of which are components within the default mode, salience and central executive networks. Disrupted nodes anchored in the default mode and executive networks were associated with depression severity. The brain systems involved sustain core symptoms in depression and implicate a structural basis for depression. Our results highlight the possibility that developmental and genetic factors are crucial to understand the neuropathology of depression.