Altered brain network modules induce helplessness in major depressive disorder

The effects of swLORETA Z-score neurofeedback for patients comorbid with major depressive disorder and anxiety symptoms

BACKGROUND

Patients with major depressive disorder (MDD) exhibit atypical brain activities in the frontal, temporal, and parietal lobes. The study aimed to investigate the effects of standardized weighted low-resolution electromagnetic tomography Z-score neurofeedback (swLZNFB) on symptoms of depression and anxiety, electroencephalography (EEG) parameters, and deep brain activities in patients with MDD.

METHOD

Forty-eight patients with MDD comorbid with anxiety symptoms were assigned to the swLZNFB group and the control group. Participants completed the Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) and a 5-minute resting EEG at the pre-and post-tests. The swLZNFB group received ten sessions of one-hour treatment twice weekly. The control group received treatment as usual. The scores for BDI-II and BAI, number of EEG abnormalities, percentage of EEG abnormalities, and current source density (CSD) measured in the prefrontal cortex (PFC), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and amygdala were compared at pre-and post-tests between the two groups.

RESULTS

There were decreased scores of BDI-II and BAI, number of EEG abnormalities, and percentage of EEG abnormalities at post-test compared with pre-test in the swLZNFB group, and lower scores of BDI-II and BAI at post-test in the swLZNFB group compared with the control group. Moreover, decreased CSD of beta1 and beta3 in the PFC, ACC, PCC, and amygdala at post-test compared to pre-test in the swLZNFB group.

LIMITATIONS

Not a randomized controlled trial.

CONCLUSION

Ten sessions of swLZNFB reduced clinical symptoms and atypical brain activities, it serves as a potential psychological intervention for patients with MDD.

Community detection in the human connectome: Method types, differences and their impact on inference

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

Electroconvulsive Therapy Regulates Brain Connectome Dynamics in Patients With Major Depressive Disorder

BACKGROUND

Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but its underlying neural mechanisms remain largely unknown. The aim of this study was to identify changes in brain connectome dynamics after ECT in MDD and to explore their associations with treatment outcome.

METHODS

We collected longitudinal resting-state functional magnetic resonance imaging data from 80 patients with MDD (50 with suicidal ideation [MDD-SI] and 30 without [MDD-NSI]) before and after ECT and 37 age- and sex-matched healthy control participants. A multilayer network model was used to assess modular switching over time in functional connectomes. Support vector regression was used to assess whether pre-ECT network dynamics could predict treatment response in terms of symptom severity.

RESULTS

At baseline, patients with MDD had lower global modularity and higher modular variability in functional connectomes than control participants. Network modularity increased and network variability decreased after ECT in patients with MDD, predominantly in the default mode and somatomotor networks. Moreover, ECT was associated with decreased modular variability in the left dorsal anterior cingulate cortex of MDD-SI but not MDD-NSI patients, and pre-ECT modular variability significantly predicted symptom improvement in the MDD-SI group but not in the MDD-NSI group.

CONCLUSIONS

We highlight ECT-induced changes in MDD brain network dynamics and their predictive value for treatment outcome, particularly in patients with SI. This study advances our understanding of the neural mechanisms of ECT from a dynamic brain network perspective and suggests potential prognostic biomarkers for predicting ECT efficacy in patients with MDD.

Association between social withdrawal and suicidal ideation in patients with major depressive disorder: The mediational role of emotional symptoms

BACKGROUND

The study was designed to investigate the associations between social withdrawal, emotional symptoms, and suicide ideation in patients with major depressive disorder (MDD).

METHODS

This cross-sectional study included 2678 MDD patients from the National Survey on Symptomatology of Depression (NSSD). Differences in the sociodemographic factors, clinical characteristics, suicide ideation, and emotional symptoms were compared in patients with different frequencies of social withdrawal. Pearson correlation, multiple linear regression analysis, and mediation analysis were employed to assess the contribution of social withdrawal to suicide ideation.

RESULTS

MDD patients with a higher frequency of social withdrawal were prone to have a higher frequency of suicide ideation (p for trend <0.001) and history of suicide behavior (p for trend <0.001). Multiple linear regression analysis showed that there was a dose-response relationship between social withdrawal and suicide ideation in MDD patients, but this association became insignificant after adjusting for emotional symptoms. Mediation analysis suggested that all of the emotional symptoms had significant mediating effects on the association between social withdrawal and suicide ideation in MDD patients (p < 0.05). The magnitude of mediation varied between 4.3 % and 64.3 %, with the largest mediating effect in the feeling of despair (64.3 %), helplessness (41.2 %), and loneliness (40.0 %).

CONCLUSION

Our study provides evidence that social withdrawal was a common clinical presentation and it may increase the risk for suicide through emotional symptoms in MDD patients.

LIMITATIONS

Causal conclusions could not be drawn between social withdrawal, emotional symptoms, and suicide ideation because of the cross-sectional design of the study.

A Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD): study protocol for a randomized clinical trial and prognosis study

BACKGROUND

Major depressive disorder (MDD) with atypical features, namely depression with atypical features (AFD), is one of the most common clinical specifiers of MDD, closely associated with bipolar disorder (BD). However, there is still a lack of clinical guidelines for the diagnosis, treatment, and prognosis of AFD. Our study mainly focuses on three issues about how to identify AFD, what is the appropriate individualized treatment for AFD, and what are the predictive biomarkers of conversion to BD.

METHODS

The Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD) is a multicenter, prospective, open-label study consisting of a 12-week randomized controlled trial (RCT) and a continued follow-up until 4 years or reaching the study endpoint. It is enrolling 480 patients with AFD (120 per treatment arm), 100 patients with BD, and 100 healthy controls (HC). Multivariate dimension information is collected including clinical features, cognitive function, kynurenine pathway metabolomics, and multimodal magnetic resonance imaging (MRI) data. Firstly, multivariate informatics analyses are performed to recognize patients with AFD from participants including the first-episode and recurrent atypical depression, patients with BD, and patients with HC. Secondly, patients with atypical depression are randomly allocated to one of the four treatment groups including "single application of selective serotonin reuptake inhibitor (SSRI) or serotonin-noradrenaline reuptake inhibitor (SNRI)", "SSRI/SNRI combined with mood stabilizer," "SSRI/SNRI combined with quetiapine (≥ 150 mg/day)," or "treatment as usual (TAU)" and then followed up 12 weeks to find out the optimized treatment strategies. Thirdly, patients with atypical depression are followed up until 4 years or switching to BD, to explore the risk factors of conversion from atypical depression to BD and eventually build the risk warning model of conversion to BD.

DISCUSSION

The first enrolment was in August 2019. The iDoT-AFD study explores the clinical and biological markers for the diagnosis, treatment, and prognosis of AFD and further provides evidence for clinical guidelines of AFD.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04209166. Registered on December 19, 2019.

Abnormal Properties of Cortical Functional Brain Network in Major Depressive Disorder: Graph Theory Analysis Based on Electroencephalography-Source Estimates

Studies of scalp electroencephalography (EEG) had shown altered topological organization of functional brain networks in patients with major depressive disorder (MDD). However, most previous EEG-based network analyses were performed at sensor level, while the interpretation of obtained results was not straightforward due to volume conduction effect. To reduce the impact of this defect, the whole cortical functional brain networks of MDD patients were studied during resting state based on EEG-source estimates in this paper. First, scalp EEG signals were recorded from 19 patients with MDD and 20 normal controls under resting eyes-closed state, and cortical neural signals were estimated by using sLORETA method. Then, the correntropy coefficient of wavelet packet coefficients was performed to calculate functional connectivity (FC) matrices in four different frequency bands: δ, θ, α, β, respectively. Afterwards, topological properties of brain networks were analyzed by graph theory approaches. The results showed that the global FC strength of MDD patients was significantly higher than that of healthy subjects in α band. Also, it was found that MDD patients have abnormally increased clustering coefficient and local efficiency in both α and β bands compared to normal people. Furthermore, patients with MDD exhibited increased nodal clustering coefficients in the left lingual gryus and left precuneus in α band. In addition, β band global clustering coefficient was positively correlated with the scores of depression severity. Therefore, the findings indicated the cortical functional brain networks in MDD patients were disruptions, which suggested it would be one of potential causes of depression.

Contrasting the amygdala activity and functional connectivity profile between antidepressant-free participants with major depressive disorder and healthy controls: A systematic review of comparative fMRI studies

Functional neuroimaging research suggests that the amygdala is implicated in the pathophysiology of major depressive disorder (MDD). This systematic review aimed to identify consistently reported amygdala activity and functional connectivity (FC) abnormalities in antidepressant-free participants with MDD as compared to healthy controls at baseline (i.e., before treatment initiation or experimental manipulation). A search for relevant published studies and registered clinical trials was conducted through OVID (MEDLINE, PsycINFO, and Embase) and ClinicalTrials.gov with an end date of March 7th, 2022. Fifty published studies and two registered clinical trials were included in this review. Participants with MDD frequently exhibited amygdala hyperactivity in response to negative stimuli, abnormal event-related amygdala-anterior cingulate cortex (ACC) FC, and abnormal resting-state amygdala FC with the insula and the prefrontal, temporal, and parietal cortices. Decreased resting-state FC was consistently found between the amygdala and the orbitofrontal cortex, striatum, cerebellum, and middle/inferior frontal gyri. Due to the limited number of studies examining resting-state amygdala activity and FC with specific subregions of interest, including those within the ACC, further investigation is warranted.

Interleukin-6-white matter network differences explained the susceptibility to depression after stressful life events

BACKGROUND

Stressful life events (SLEs) are well-established proximal predictors of the onset of depression. However, the fundamental causes of interindividual differences in depression outcomes are poorly understood. This study addressed this depression susceptibility mechanism using a well-powered sample of adults living in China.

METHODS

Healthy participants with SLEs (n = 185; mean = 47.51 years, 49.73% female), drawn from a longitudinal study on the development of depression, underwent diffusion tensor imaging, interleukin-6 (IL-6) level measurement, and trimonthly standardized clinical and scale evaluations within a two-year period.

RESULTS

Receiver operating characteristic analyses indicated that reduced feeder connection and HIP.R nodal efficiency improved the predictive accuracy of post-SLEs depression (OR_feeder = 0.623, AUC = 0.869, P < 0.001; OR_HIP = 0.459, AUC = 0.855, P < 0.001). The successfully established path analysis model confirmed the significant partial effect of SLEs-IL-6-white matter (WM) network differences-depression (onset and severity) (x²/8 = 1.453, goodness-of-fit [GFI] = 0.935, standard root-mean-square error of approximation [SRMR] = 0.024). Females, individuals with lower exercise frequency (EF) or annual household income (AHI) were more likely to have higher IL-6 level after SLEs (β_{int-female⁎SLEs} = -0.420, P < 0.001; β_{int-exercise⁎SLEs} = -0.412, P < 0.001; β_{int-income⁎SLEs} = -0.302, P = 0.005).

LIMITATIONS

The sample size was restricted due to the limited incidence rate and prospective follow-up design.

CONCLUSIONS

Our results suggested that among healthy adults after SLEs, those who exhibited abnormal IL-6-WM differences were susceptible to developing depression. Females, lower AHI or EF might account for an increased risk of developing these abnormal IL-6-WM differences.

Prefrontal cortex and depression

The prefrontal cortex (PFC) has emerged as one of the regions most consistently impaired in major depressive disorder (MDD). Although functional and structural PFC abnormalities have been reported in both individuals with current MDD as well as those at increased vulnerability to MDD, this information has not translated into better treatment and prevention strategies. Here, we argue that dissecting depressive phenotypes into biologically more tractable dimensions - negative processing biases, anhedonia, despair-like behavior (learned helplessness) - affords unique opportunities for integrating clinical findings with mechanistic evidence emerging from preclinical models relevant to depression, and thereby promises to improve our understanding of MDD. To this end, we review and integrate clinical and preclinical literature pertinent to these core phenotypes, while emphasizing a systems-level approach, treatment effects, and whether specific PFC abnormalities are causes or consequences of MDD. In addition, we discuss several key issues linked to cross-species translation, including functional brain homology across species, the importance of dissecting neural pathways underlying specific functional domains that can be fruitfully probed across species, and the experimental approaches that best ensure translatability. Future directions and clinical implications of this burgeoning literature are discussed.

Effects of escitalopram therapy on resting-state functional connectivity of subsystems of the default mode network in unmedicated patients with major depressive disorder

Antidepressants are often the first-line medications prescribed for patients with major depressive disorder (MDD). Given the critical role of the default mode network (DMN) in the physiopathology of MDD, the current study aimed to investigate the effects of antidepressants on the resting-state functional connectivity (rsFC) within and between the DMN subsystems. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data from 36 unmedicated MDD patients at baseline and after escitalopram treatment for 12 weeks. The rs-fMRI data were also collected from 61 matched healthy controls at the time point with the same interval. Then, we decomposed the DMN into three subsystems based on a template from previous studies and computed the rsFC within and between the three subsystems. Finally, repeated measures analysis of covariance was conducted to identify the main effect of group and time and their interaction effect. We found that the significantly reduced within-subsystem rsFC in the DMN core subsystem in patients with MDD at baseline was increased after escitalopram treatment and became comparable with that in the healthy controls, whereas the reduced within-subsystem rsFC persisted in the DMN dorsal medial prefrontal cortex (dMPFC) and medial temporal subsystems in patients with MDD following escitalopram treatment. In addition, the reduced between-subsystem rsFC between the core and dMPFC subsystem showed a similar trend of change after treatment in patients with MDD. Moreover, our main results were confirmed using the DMN regions from another brain atlas. In the current study, we found different effects of escitalopram on the rsFC of the DMN subsystems. These findings deepened our understanding of the neuronal basis of antidepressants' effect on brain function in patients with MDD. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377 . Registration number: ChiCTR-OOC-17012566.

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.

Resting-state electroencephalographic functional network alterations in major depressive disorder following magnetic seizure therapy

Magnetic seizure therapy (MST) is emerging as a safe and well-tolerated experimental intervention for major depressive disorder (MDD), with very minimal cognitive side-effects. However, the underlying mechanism of action of MST remains uncertain. Here, we used resting-state electroencephalography (RS-EEG) to characterise the physiological effects of MST for treatment resistant MDD. We recorded RS-EEG in 21 patients before and after an open label trial of MST applied over the prefrontal cortex using a bilateral twin coil. RS-EEG was analysed for changes in functional connectivity, network topology, and spectral power. We also ran further baseline comparisons between the MDD patients and a cohort of healthy controls (n = 22). Network-based connectivity analysis revealed a functional subnetwork of significantly increased theta connectivity spanning frontal and parieto-occipital channels following MST. The change in theta connectivity was further found to predict clinical response to treatment. An additional widespread subnetwork of reduced beta connectivity was also elucidated. Graph-based topological analyses showed an increase in functional network segregation and reduction in integration in the theta band, with a decline in segregation in the beta band. Finally, delta and theta power were significantly elevated following treatment, while gamma power declined. No baseline differences between MDD patients and healthy subjects were observed. These results highlight widespread changes in resting-state brain dynamics following a course of MST in MDD patients, with changes in theta connectivity providing a potential physiological marker of treatment response. Future prospective studies are required to confirm these initial findings.

Brain Plasticity Reflects Specialized Cognitive Development Induced by Musical Training

Learning a musical instrument requires a long period of training and might induce structural and functional changes in the brain. Previous studies have shown brain plasticity resulting from training with a musical instrument. However, these studies did not distinguish the effects on brain plasticity of specific musical instruments as they examined the brain of musicians who had learned a single musical instrument/genre and did not control for confounding factors, such as common or interactive effects involved in music training. To address this research gap, the present work investigated musicians who had experience with both a piano and a wind instrument, for example, flute, trumpet, clarinet etc. By examining the difference between the 2 musical instruments in the same subject, we avoided the effects common to all musical instruments and the confounding factors. Therefore, we identified several high-tier brain areas displaying a brain plasticity specific to each musical instrument. Our findings show that learning a musical instrument might result in the development of high cognitive functions reflecting the skills/abilities unique to the instrument played.

The Moderating Effects of Emotions on the Relationship Between Self-Reported Individual Traits and Actual Risky Driving Behaviors

Purpose

Researches addressing driving behaviors have not fully revealed how emotions affect risky driving behaviors and tend to focus on the effects of some negative emotions rather than those of more specific emotions. This study aimed to test the potential moderating effects of eight common driving emotions on the relationship between self-reported individual traits (sensation seeking and driving style) and actual risky driving behaviors, sequentially providing some implications for the risky driving behavior prevention.

Participants and Methods

A total of 78 licensed drivers were recruited from undergraduate students, company employees and taxi drivers in China. The participants’ data on self-reported driving style (SDBS) and self-reported sensation seeking (SSSS) were obtained through questionnaires. The participants’ data on actual risky driving behaviors (ARD) in eight driving emotional activation states were obtained through a series of emotion induction experiments and driving experiments. The Structural Equation Modeling (SEM) and moderating effect tests were employed to investigate the relationships of driving emotions, SDBS, SSSS and ARD.

Results

Results showed that anger and pleasure affected risky driving behaviors positively by enhancing the relationship between SDBS and ARD, while surprise and fear were negatively related to risky driving behaviors by weakening this relationship. Anxiety positively affected risky driving behaviors by synchronously enhancing the relationship between SDBS and ARD and the relationship between SSSS and ARD, while helplessness and relief affected risky driving behaviors negatively by weakening the two relationships. Contempt affected risky driving behaviors positively by enhancing the relation between SSSS and ARD.

Conclusion

The results illustrated the effects of different emotions on risky driving behaviors, and also partly explained the reasons for these effects. This research provided a source of reference for reducing traffic accidents caused by risky driving behaviors.

Cognitive behavioral therapy for patients with mild to moderate depression: Treatment effects and neural mechanisms

In this study, we combined clinical assessment and magnetic resonance imaging (MRI) techniques to investigate the brain mechanisms in mild to moderate depression (MMD) patients following cognitive behavioral therapy (CBT). Data were collected from 30 MMD patients and 18 healthy controls, and we divided patients into two treatment periods (4 weeks, 8 weeks). Clinical assessment indicated that depression characteristics, as quantified by Hamilton Depression Rating Scale (HAMD), were significantly higher in MMD patients than in healthy controls. At the baseline, MRI data revealed abnormalities in the hippocampus and nucleus accumbens (NAc) of patients with MMD, e.g., smaller gray matter volumes of the hippocampus and nucleus accumbens (NAc), as well as weaker functional connectivity between NAc and the posterior cingulate cortex/precuneus. Moreover, the hippocampus and NAc volumes were negatively correlated with the HAMD scores in MMD patients. After CBT intervention, the HAMD scores decreased, and the structural and functional characteristics of NAc in MMD patients obtained at 8-week were improved; e.g., no significant differences in NAc volume or NAc-based functional connectivity between the two groups. Taken together, our results provided evidence suggesting that CBT is an effective treatment for MMD patients. Alterations of gray matter volume and resting-state functional connectivity after 8 weeks of CBT indicated a potential modulation mechanism in brain structural modifications and functional connectivity plasticity within the NAc in MMD patients.

Altered Functional Connectivity Within and Between Salience and Sensorimotor Networks in Patients With Functional Constipation

Functional constipation (FCon) is a common functional gastrointestinal disorder. A considerable portion of patients with FCon is associated with anxiety/depressive status (FCAD). Previous neuroimaging studies mainly focused on patients with FCon without distinguishing FCAD from FCon patients without anxiety/depressive status (FCNAD). Differences in brain functions between these two subtypes remain unclear. Thus, we employed resting-state functional magnetic resonance imaging (RS-fMRI) and graph theory method to investigate differences in brain network connectivity and topology in 41 FCAD, 42 FCNAD, and 43 age- and gender-matched healthy controls (HCs). FCAD/FCNAD showed significantly lower normalized clustering coefficient and small-world-ness. Both groups showed altered nodal degree/efficiency mainly in the rostral anterior cingulate cortex (rACC), precentral gyrus (PreCen), supplementary motor area (SMA), and thalamus. In the FCAD group, nodal degree in the SMA was negatively correlated with difficulty of defecation, and abdominal pain was positively correlated with nodal degree/efficiency in the rACC, which had a lower within-module nodal degree. The salience network (SN) exhibited higher functional connectivity (FC) with the sensorimotor network (SMN) in FCAD/FCNAD, and FC between these two networks was negatively correlated with anxiety ratings in FCAD group. Additionally, FC of anterior insula (aINS)-rACC was only correlated with constipation symptom (i.e., abdominal pain) in the FCNAD group. In the FCAD group, FCs of dorsomedial prefrontal cortex-rACC, PreCen-aINS showed correlations with both constipation symptom (i.e., difficulty of defecation) and depressive status. These findings indicate the differences in FC of the SN-SMN between FCAD and FCNAD and provide neuroimaging evidence based on brain function, which portrays important clues for improving new treatment strategies.

Different Modular Organization Between Early Onset and Late Onset Depression: A Study Base on Granger Causality Analysis

Background: Modular organization reflects the activity patterns of our brain. Different disease states may lead to different activity patterns and clinical features. Early onset depression (EOD) and late onset depression (LOD) share the same clinical symptoms, but have different treatment strategies and prognosis. Thus, explored the modular organization of EOD and LOD might help us understand their pathogenesis.

Method: The study included 82 patients with late life depression (EOD 40, LOD 42) and 90 healthy controls. We evaluated the memory, executive function and processing speed and performed resting-stage functional MRI for all participants. We constructed a functional network based on Granger causality analysis and carried out modularity, normalized mutual information (NMI), Phi coefficient, within module degree z-score, and participation coefficient analyses for all the participants.

Result: The Granger function network analysis suggested that the functional modularity was different among the three groups (P_auc = 0.0300), and NMI analysis confirmed that the partition of EOD was different from that of LOD (P_auc = 0.0190). Rh.10d.ROI (polar frontal cortex) and Rh.IPS1.ROI (dorsal stream visual cortex) were shown to be the potential specific nodes in the modular assignment according to the Phi coefficient (P = 0.0002, P_fdr = 0.0744 & P = 0.0004, P_fdr = 0.0744).

Conclusion: This study reveal that the functional modularity and partition were different between EOD and LOD in Granger function network. These findings support the hypothesis that different pathological changes might exist in EOD and LOD.

Decreased dynamism of overlapping brain sub-networks in Major Depressive Disorder

Major Depressive Disorder (MDD) is increasingly recognized as a common brain disorder with aberrant brain networks. Alterations in dynamic functional brain networks have been widely reported in MDD. However, previous studies mainly focused on detecting non-overlapping sub-networks/communities, neglecting the possibility that one brain region may belong to multiple sub-networks/communities. In the present work, we utilized tensor decomposition method to detect overlapping communities and study the dynamism of overlapping sub-networks through 58 patients with MDD and 63 age- and sex-matched healthy controls (HC). The strength vectors of communities were calculated and two-sample t-test was performed to investigate the statistical significance of the differences in dynamism of MDD and HC groups. We found that communities detected in two groups were pairwise region-matching but overlapped brain regions were almost totally different. We considered two region-matching communities in the two groups as a sub-network. Compared to HCs, MDD patients showed significantly decreased dynamism in five sub-networks which could be functionally mapped to Visual Network (VN), Default Mode Network (DMN), Cognitive Control Network (CCN), Bilateral Limbic Network (BLN) and Auditory Network (AN). The results showed that MDD might only have a marginal effect on the holistic detection of communities and the changes of overlapped brain regions in MDD patients might be put down to the alteration of hubs. Further statistical analysis on nine sub-networks showed decreased dynamism of five sub-networks in MDD patients, which might help us achieve a better understanding of mechanism in MDD.

Disrupted intrinsic functional brain topology in patients with major depressive disorder

Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.

Disrupted dynamic functional connectivity in right amygdalar subregions differentiates bipolar disorder from major depressive disorder

Notwithstanding being the object of a growing field of clinical research, the investigation of the dynamic resting-state functional connectivity alterations in psychiatric illnesses is still in its early days. Current research on major depressive disorder (MDD) and bipolar disorder (BD) has evidenced abnormal resting-state functional connectivity (rsFC), especially in regions subserving emotional processing and regulation such as the amygdala. However, dynamic changes in functional connectivity within the amygdalar subregions in distinguishing BD and MDD has not yet been fully understood. In this paper, we aim at analyzing the patterns characterizing dynamic FC (dFC) in the right amygdala to investigate the differences between similarly depressed BD and MDD. A number of 40 BD patients, 61 MDD patients and 63 healthy controls (HCs) underwent functional magnetic resonance imaging (fMRI) at rest. Using the right-amygdala as seed region, we compared the dFC within three subdivisions, namely, laterobasal (LB), centromedial (CM) and superficial (SF) between all the groups. To do so, one-way ANOVA followed by post-hoc t-tests were employed. Compared to HCs, patients with BD had a decreased dFC between right LB and the left postcentral gyrus as well as an increased dFC between right CM and the right cerebellum.Compared to BD patients, patients with MDD showed a decreased dFC between right CM and the cerebellum and an increased dFC between right LB and the left postcentral gyrus. These findings present initial evidence that abnormal patterns of the right-amygdalar subregions shared by BD and MDD supports the differential pathophysiology of these disorders.

Differences in Driving Intention Transitions Caused by Driver's Emotion Evolutions

Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system.

Rumination network dysfunction in major depression: A brain connectome study

BACKGROUND

Rumination is a central feature of major depressive disorder (MDD). Knowledge of the neural structures that underpin rumination offers significant insight into depressive pathophysiology and may help to develop potential intervention strategies for MDD, a mental illness that has become the leading cause of disability worldwide.

METHODS

Using resting-state fMRI and graph theory, this study adopted a connectome approach to examine the functional topological organization of the neural network associated with rumination in MDD. Data from 96 participants were analyzed, including 51 patients with MDD and 45 healthy controls.

RESULTS

We found altered functional integration and segregation of neural networks associated with depressive rumination as indicated by reduced global and local efficiency in MDD patients compared with controls. Interestingly, these metrics correlated positively with depression severity, as measured by the Hamilton Depression Rating Scale. Moreover, mediation analysis indicated that the association between network metrics and depression severity was mediated by the ruminative tendency of patients. Disrupted nodal centralities were located in regions associated with emotional processing, visual mental imagery, and attentional control.

CONCLUSION

Our results highlight rumination as a two-edged sword that reflects a disease-specific neuropathology but also points to a functionality of depressive symptoms with evolutionary meaning.

Abnormal topology of brain functional networks in unipolar depression and bipolar disorder using optimal graph thresholding

Two popular debilitating illness, unipolar depression (UD) and bipolar disorder (BD), have the similar symptoms and tight association on the psychopathological level, leading to a clinical challenge to distinguish them. In order to figure out the underlying common and different mechanism of both mood disorders, resting-state functional magnetic resonance imaging (rs-fMRI) data derived from 36 UD patients, 42 BD patients (specially type I, BD-I) and 45 healthy controls (HC) were analyzed retrospectively in this study. Functional brain networks were firstly constructed on both group and individual levels with a density 0.2, which was determined by a network thresholding approach based on modular similarity. Then we investigated the alterations of modular structure and other topological properties of the functional brain network, including global network characteristics and nodal network measures. The results demonstrated that the functional brain networks of UD and BD-I groups preserved the modularity and small-worldness property. However, compared with HC, reduced number of modules was observed in both patients' groups with shared alterations occurring in hippocampus, para hippocampal gyrus, amygdala and superior parietal gyrus and distinct changes of modular composition mainly in the caudate regions of basal ganglia. Additionally, for the network characteristics, compared to HC, significantly decreased global efficiency and small-worldness were observed in BD-I. For the nodal metrics, significant decrease of local efficiency was found in several regions in both UD and BD-I, while a UD-specified increase of participant coefficient was found in the right paracentral lobule and the right thalamus. These findings may contribute to throw light on the neuropathological mechanisms underlying the two disorders and further help to explore objective biomarkers for the correct diagnosis of UD and BD.

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

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

Effects of the LHPP gene polymorphism on the functional and structural changes of gray matter in major depressive disorder

Background

A single-nucleotide polymorphism (SNP) of the LHPP gene (rs35936514) has been reported to be associated with major depressive disorder (MDD) in genome-wide association studies. However, the systems-level neural effects of rs35936514 that mediate the association are unknown. We hypothesized that variations in rs35936514 would be associated with structural and functional changes in gray matter (GM) at rest in MDD patients.

Methods

A total of 50 MDD patients and 113 healthy controls (HCs) were studied. Functional connectivity (FC) was analyzed by defining the bilateral hippocampus as the seed region. Voxel-based morphometry (VBM) was performed to assess the patterns of GM volume. The subjects were further divided into two groups: a CC homozygous group (CC; 24 MDD and 56 HC) and a risk T-allele carrier group (CT/TT genotypes; 26 MDD and 57 HC). A 2×2 analysis of variance (ANOVA: diagnosis × genotype) was used to determine the interaction effects and main effect (P<0.05).

Results

Significant diagnosis × genotype interaction effects on brain morphology and FC were noted. Compared to other subgroups, the MDD patients with the T allele showed an increased hippocampal FC in the bilateral calcarine cortex and cuneus and a decreased hippocampal FC in the right dorsolateral prefrontal cortex (DLPFC), bilateral anterior cingulate cortex (ACC), and medial prefrontal cortex (MPFC), in addition to reduced GM volume in the right DLPFC, bilateral temporal cortex, and posterior cingulate cortex (PCC).

Conclusions

LHPP gene polymorphisms may affect functional and structural changes in the GM at rest and may play an important role in the pathophysiological mechanisms of MDD.

Resting-state functional connectivity in medication-naïve adolescents with major depressive disorder

Adolescence is a vulnerable period for major depressive disorder (MDD). The aim of our study was to investigate resting-state functional connectivity (RSFC) in first-episode, medication-naïve adolescent MDD patients. Twenty-three drug-naïve adolescents diagnosed with first-episode MDD and 27 healthy participants were enrolled. Seed-to-voxel RSFC analyses were performed. The frontolimbic circuit regions of interest included the amygdala, anterior cingulate cortex, insula, and hippocampus. A correlation analysis between the RSFC and Children's Depression Inventory, Hamilton depression rating scale, and duration of episodes was performed. The adolescents with MDD exhibited the following characteristics: a lower RSFC between the right amygdala and right superior frontal gyrus; a lower RSFC between the right hippocampus and clusters including the right insula and right middle frontal gyrus; a higher RSFC between the left insula and clusters including the bilateral middle frontal gyrus, right superior frontal gyrus, and right frontal pole; and a higher RSFC between the left dorsal anterior cingulate cortex and a cluster including the left insula. Medication-naïve adolescents with depression display lower connectivity of several brain regions implicated in processing, regulation, and memory of emotions. Higher connectivity was observed in brain regions that potentially explain rumination, impaired concentration, and physiological arousal.

Association of LHPP genetic variation (rs35936514) with structural and functional connectivity of hippocampal-corticolimbic neural circuitry

A single nucleotide polymorphism at the LHPP gene (rs35936514) has been reported to be associated with major depressive disorder (MDD) in genome-wide association studies. We conducted a neuroimaging analysis to explore whether and which brain neural systems are affected by LHPP variation. Since LHPP variants seem to be associated with the hippocampus, we assessed the relationship between rs35936514 variation and structural-functional connectivity within a hippocampal-corticolimbic neural system implicated in MDD. A total of 122 Chinese subjects were divided into a CC homozygous group (CC genotype, n = 60) and a T allele-carrier group (CT/TT genotypes, n = 62). All subjects participated in resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) scans. Structural and functional connectivity data analyses were then performed. Compared to the CC group, the T allele-carrier group showed significantly higher fractional anisotropy (FA) values in the fornix as well as increased functional connectivity from the hippocampus to the rostral part of the anterior cingulate cortex (rACC). Moreover, a significant negative correlation between fornix FA value and hippocampus-rACC functional connectivity was identified (P < 0.05). These findings suggest that there is a relationship between rs35936514 variation and both structural and functional hippocampal-corticolimbic neural system involvement in MDD. LHPP may play an important role in the neuropathophysiology of MDD.

Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI

Major depressive disorder (MDD) is known to be associated with altered interactions between distributed brain regions. How these regional changes relate to the reorganization of cortical functional systems, and their modulation by antidepressant medication, is relatively unexplored. To identify changes in the community structure of cortical functional networks in MDD, we performed a multiscale community detection algorithm on resting-state functional connectivity networks of unmedicated MDD (uMDD) patients (n = 46), medicated MDD (mMDD) patients (n = 38), and healthy controls (n = 50), which yielded a spectrum of multiscale community partitions. we selected an optimal resolution level by identifying the most stable community partition for each group. uMDD and mMDD groups exhibited a similar reconfiguration of the community structure of the visual association and the default mode systems but showed different reconfiguration profiles in the frontoparietal control (FPC) subsystems. Furthermore, the central system (somatomotor/salience) and 3 frontoparietal subsystems showed strengthened connectivity with other communities in uMDD but, with the exception of 1 frontoparietal subsystem, returned to control levels in mMDD. These findings provide evidence for reconfiguration of specific cortical functional systems associated with MDD, as well as potential effects of medication in restoring disease-related network alterations, especially those of the FPC system.

Dynamic community structure in major depressive disorder: A resting-state MEG study

BACKGROUND

Major Depressive Disorder (MDD), characterized by depressed mood or anhedonia, is associated with altered functional connectivity (FC) within and between large scale networks such as the Default Mode Network (DMN), the Central Executive Network (CEN) and the Salience Network (SN). Since aberrant FC exhibits temporal variability and could give rise to distorted reconfiguration of functional brain networks, an in-depth analysis of the community structure could provide further insight into the synchrony of networks. We hypothesized that alterations in dynamic network community structure in MDD could be temporally accompanied by disrupted conscious states of these three networks.

METHODS

26 MDD patients and 25 healthy controls were scanned using a whole-head resting-state Magnetoencephalography (MEG) machine. A novel multilayer modularity framework explored the functional modulation of these networks. Recruitment (R) and integration (I) provided the strength of interaction within networks or across networks, respectively.

RESULTS

The brain regions in the DMN, CEN and SN were transiently integrated and segmented in both patients and controls. R of CEN and I of SN were significantly greater in MDD compared to controls.

CONCLUSION

Intrinsic resting-state networks dynamically interact and reorganize into distinct functional modules in both patients and controls. However, the CEN "hyper-intertwines" with itself and SN "hyper-integrates" among the network of interest in depressed patients compared to controls. Network-level alterations in R and I revealed a more generalized system-level effect rather than a focal-wise effect from a neural dynamic perspective. This could potentially highlight an abnormal network-based mechanism in depression.

Aberrant brain network topology in fronto-limbic circuitry differentiates euthymic bipolar disorder from recurrent major depressive disorder

INTRODUCTION

Previous studies have established graph theoretical analysis of functional network connectivity (FNC) as a potential tool to detect neurobiological underpinnings of psychiatric disorders. Despite the promising outcomes in studies that examined FNC aberrancies in bipolar disorder (BD) and major depressive disorder (MDD), there is still a lack of research comparing both mood disorders, especially in a nondepressed state. In this study, we used graph theoretical network analysis to compare brain network properties of euthymic BD, euthymic MDD and healthy controls (HC) to evaluate whether these groups showed distinct features in FNC.

METHODS

We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 BD patients, 15 patients with recurrent MDD as well as 30 age- and gender-matched HC. Graph theoretical analyses were then applied to investigate functional brain networks on a global and regional network level.

RESULTS

Global network analysis revealed a significantly higher mean global clustering coefficient in BD compared to HC. We further detected frontal, temporal and subcortical nodes in emotion regulation areas such as the limbic system and associated regions exhibiting significant differences in network integration and segregation in BD compared to MDD patients and HC. Participants with MDD and HC only differed in frontal and insular network centrality.

CONCLUSION

In conclusion, our findings indicate that a significantly altered brain network topology in the limbic system might be a trait marker specific to BD. Brain network analysis in these regions may therefore be used to differentiate euthymic BD not only from HC but also from patients with MDD.

Personality traits are related with dynamic functional connectivity in major depression disorder: A resting-state analysis

BACKGROUND

Major depressive disorder (MDD) is one of the most well-known psychiatric disorders, which can be destructive for its damage to people's normal cognitive, emotional and social functions. Personality refers to the unique and stable character of thinking and behavior style of an individual, which has long been thought as a key influence factor for MDD. Although some knowledge about the common neural basic between MDD and personality traits has been acquired, there are few studies exploring dynamic neural mechanism behind them, which changes brain connectivity pattern rapidly to adapt to the environment over time.

METHODS

In this study, the emerging dynamic functional network connectivity (DFNC) method was used in resting-state fMRI data to find the differences between healthy group (N = 107) and MDD group (N = 109) in state-based dynamic measures, and the correlations between these measures and personality traits (extraversion and neuroticism in Eysenck Personality Questionnaire, EPQ) were explored.

RESULTS

The results showed that MDD was significantly less than the health control group in dwell time and fraction time of state 4, which was positively correlated with extraversion score and negatively correlated with neuroticism score. Further exploration on state 4 showed that it had low modularity, hyper-connectedness of sensory-related regions and DMN, and weak connections between cortex and subcortical areas, which suggested that the absence of this state in MDD might represent a decrease in activity and positive emotions.

CONCLUSION

We found the dynamic functional connectivity mechanism underlying MDD, confirmed our hypothesis that there existed the interacted relationship between trait, disease and the brain's dynamic characteristic, and suggested some reference for treatment of depression.

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.

Abnormal dynamic functional connectivity of amygdalar subregions in untreated patients with first-episode major depressive disorder

BACKGROUND

Accumulating evidence supports the concept of the amygdala as a complex of structurally and functionally heterogeneous nuclei rather than as a single homogeneous structure. However, changes in resting-state functional connectivity in amygdalar subregions have not been investigated in major depressive disorder (MDD). Here, we explored whether amygdalar subregions - including the laterobasal, centromedial (CM) and superficial (SF) areas - exhibited distinct disruption patterns for different dynamic functional connectivity (dFC) properties, and whether these different properties were correlated with clinical information in patients with MDD.

METHODS

Thirty untreated patients with first-episode MDD and 62 matched controls were included. We assessed between-group differences in the mean strength of dFC in each amygdalar subregion in the whole brain using general linear model analysis.

RESULTS

The patients with MDD showed decreased strength in positive dFC between the left CM/SF and brainstem and between the left SF and left thalamus; they showed decreased strength in negative dFC between the left CM and right superior frontal gyrus (p < 0.05, family-wise error-corrected). We found significant positive correlations between age at onset and the mean positive strength of dFC in the left CM/brainstem in patients with MDD.

LIMITATIONS

The definitions of amygdalar subregions were based on a cytoarchitectonic delineation, and the temporal resolution of the fMRI was slow (repetition time = 2 s).

CONCLUSION

These findings confirm the distinct dynamic functional pathway of amygdalar subregions in MDD and suggest that the limbic-cortical-striato-pallido-thalamic circuitry plays a crucial role in the early stages of MDD.

Persistent antidepressant effect of low-dose ketamine and activation in the supplementary motor area and anterior cingulate cortex in treatment-resistant depression: A randomized control study

A single low-dose ketamine infusion exhibited a rapid antidepressant effect within 1h. Despite its short biological half-life (approximately 3h), the antidepressant effect of ketamine has been demonstrated to persist for several days. However, changes in brain function responsible for the persistent antidepressant effect of a single low-dose ketamine infusion remain unclear METHODS: Twenty-four patients with treatment-resistant depression (TRD) were randomized into three groups according to the treatment received: 0.5mg/kg ketamine, 0.2mg/kg ketamine, and normal saline infusion. Standardized uptake values (SUVs) of glucose metabolism measured through ¹⁸F-FDG positron-emission-tomography before infusion and 1day after a 40-min ketamine or normal saline infusion were used for subsequent whole-brain voxel-wise analysis and were correlated with depressive symptoms, as defined using the Hamilton Depression Rating Scale-17 (HDRS-17) score RESULTS: The voxel-wise analysis revealed that patients with TRD receiving the 0.5mg/kg ketamine infusion had significantly higher SUVs (corrected for family-wise errors, P = 0.014) in the supplementary motor area (SMA) and dorsal anterior cingulate cortex (dACC) than did those receiving the 0.2mg/kg ketamine infusion. The increase in the SUV in the dACC was negatively correlated with depressive symptoms at 1day after ketamine infusion DISCUSSION: The persistent antidepressant effect of a 0.5mg/kg ketamine infusion may be mediated by increased activation in the SMA and dACC. The higher increase in dACC activation was related to the reduction in depressive symptoms after ketamine infusion. A 0.5mg/kg ketamine infusion facilitated the glutamatergic neurotransmission in the SMA and dACC, which may be responsible for the persistent antidepressant effect of ketamine much beyond its half-life.

Resting state brain network function in major depression - Depression symptomatology, antidepressant treatment effects, future research

The alterations of functional connectivity brain networks in major depressive disorder (MDD) have been subject of a large number of studies. Using different methodologies and focusing on diverse aspects of the disease, research shows heterogeneous results lacking integration. Disrupted network connectivity has been found in core MDD networks like the default mode network (DMN), the central executive network (CEN), and the salience network, but also in cerebellar and thalamic circuitries. Here we review literature published on resting state brain network function in MDD focusing on methodology, and clinical characteristics including symptomatology and antidepressant treatment related findings. There are relatively few investigations concerning the qualitative aspects of symptomatology of MDD, whereas most studies associate quantitative aspects with distinct resting state functional connectivity alterations. Such depression severity associated alterations are found in the DMN, frontal, cerebellar and thalamic brain regions as well as the insula and the subgenual anterior cingulate cortex. Similarly, different therapeutical options in MDD and their effects on brain function showed patchy results. Herein, pharmaceutical treatments reveal functional connectivity alterations throughout multiple brain regions notably the DMN, fronto-limbic, and parieto-temporal regions. Psychotherapeutical interventions show significant functional connectivity alterations in fronto-limbic networks, whereas electroconvulsive therapy and repetitive transcranial magnetic stimulation result in alterations of the subgenual anterior cingulate cortex, the DMN, the CEN and the dorsal lateral prefrontal cortex. While it appears clear that functional connectivity alterations are associated with the pathophysiology and treatment of MDD, future research should also generate a common strategy for data acquisition and analysis, as a least common denominator, to set the basis for comparability across studies and implementation of functional connectivity as a scientifically and clinically useful biomarker.

Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits

Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient, which is an indicator that describes the organization pattern of hub in the network. To address this question, diffusion tensor imaging of 69 participants [15 LLD-MD patients; 24 patients with LLD with intact memory (LLD-IM); and 30 healthy controls (HC)] was applied to construct a brain network for each individual. A full-scale battery of neuropsychological tests were used for grouping, and evaluating executive function, processing speed and memory. Rich-club analysis and global network properties were utilized to describe the topological features in each group. Network-based statistics (NBS) were calculated to identify the impaired subnetwork in the LLD-MD group relative to that in the LLD-IM group. We found that compared with HC participants, patients with LLD (LLD-MD and LLD-IM) had relatively impaired rich-club organizations and rich-club connectivity. In addition, LLD-MD group exhibited lower feeder and local connective average strength than LLD-IM group. Furthermore, global network properties, such as the shortest path length, connective strength, efficiency and fault tolerant efficiency, were significantly decreased in the LLD-MD group relative to those in the LLD-IM and HC groups. According to NBS analysis, a subnetwork, including right cognitive control network (CCN) and corticostriatal circuits, were disrupted in LLD-MD patients. In conclusion, the disease effects of LLD were prevalent in rich-club organization. Feeder and local connections, especially in the subnetwork including right CCN and corticostriatal circuits, were further impaired in those with memory deficits. Global network properties were disrupted in LLD-MD patients relative to those in LLD-IM patients.

Topologically convergent and divergent functional connectivity patterns in unmedicated unipolar depression and bipolar disorder

Bipolar disorder (BD), particularly BD II, is frequently misdiagnosed as unipolar depression (UD), leading to inappropriate treatment and poor clinical outcomes. Although depressive symptoms may be expressed similarly in UD and BD, the similarities and differences in the architecture of brain functional networks between the two disorders are still unknown. In this study, we hypothesized that UD and BD II patients would show convergent and divergent patterns of disrupted topological organization of the functional connectome, especially in the default mode network (DMN) and the limbic network. Brain resting-state functional magnetic resonance imaging (fMRI) data were acquired from 32 UD-unmedicated patients, 31 unmedicated BD II patients (current episode depressed) and 43 healthy subjects. Using graph theory, we systematically studied the topological organization of their whole-brain functional networks at the following three levels: whole brain, modularity and node. First, both the UD and BD II patients showed increased characteristic path length and decreased global efficiency compared with the controls. Second, both the UD and BD II patients showed disrupted intramodular connectivity within the DMN and limbic system network. Third, decreased nodal characteristics (nodal strength and nodal efficiency) were found predominantly in brain regions in the DMN, limbic network and cerebellum of both the UD and BD II patients, whereas differences between the UD and BD II patients in the nodal characteristics were also observed in the precuneus and temporal pole. Convergent deficits in the topological organization of the whole brain, DMN and limbic networks may reflect overlapping pathophysiological processes in unipolar and bipolar depression. Our discovery of divergent regional connectivity that supports emotion processing could help to identify biomarkers that will aid in differentiating these disorders.

Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders

Although functional magnetic resonance imaging (fMRI) has long been used to assess task-related brain activity in neuropsychiatric disorders, it has not yet become a widely available clinical tool. Resting-state fMRI (rs-fMRI) has been the subject of recent attention in the fields of basic and clinical neuroimaging research. This method enables investigation of the functional organization of the brain and alterations of resting-state networks (RSNs) in patients with neuropsychiatric disorders. Rs-fMRI does not require participants to perform a demanding task, in contrast to task fMRI, which often requires participants to follow complex instructions. Rs-fMRI has a number of advantages over task fMRI for application with neuropsychiatric patients, for example, although applications of task fMR to participants for healthy are easy. However, it is difficult to apply these applications to patients with psychiatric and neurological disorders, because they may have difficulty in performing demanding cognitive task. Here, we review the basic methodology and analysis techniques relevant to clinical studies, and the clinical applications of the technique for examining neuropsychiatric disorders, focusing on mood disorders (major depressive disorder and bipolar disorder) and dementia (Alzheimer's disease and mild cognitive impairment).

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.

Reconfiguration of hub-level community structure in depressions: A follow-up study via diffusion tensor imaging

BACKGROUND

The role of abnormal communications among large-scale brain networks have been given increasing attentions in the pathophysiology of major depressive disorder (MDD). However, few studies have investigated the effect of antidepressant medication treatment on the information communication of structural brain networks, especially converged from the individual analysis.

METHODS

Nineteen unipolar MDD patients completed two diffusion tensor imaging (DTI) scans before and after 8-week treatment with selective serotonin reuptake inhibitor. DTI data of 37 matched healthy controls were acquired. We focused on a hub-level community structure network, and investigated whether it had differences on the whole structure and which regions drove these differences in terms of modular affiliation and hub role shift. Data were analyzed by the novel permutation network framework, which appraised the topological consistency of hubs and reserved an individual information.

RESULTS

Compared to the pre-treatment state, post-treatment patients exhibited increasing number of modular members in the modules that included the right medial superior frontal gyrus (SFGmed) or the thalamus. Moreover, the result suggested a hub role shift of the left insula from a provincial-hub before treatment to a connector-hub after treatment. Additionally, reduced inter-module degree in the right SFGmed was positively correlated with the reduced sum score of 17-item Hamilton depression rating scale at the follow-up.

CONCLUSIONS

Antidepressant medication treatment might be associated with modular reconfigurations of hubs within the fronto-limbic circuit. Moreover, increased inter-module connections of the left insula might improve its integration ability, promoting the remission of MDD. The correlation results of the right SFGmed suggested it might be a valuable indicator for treatment response.

Altered resting-state functional connectivity in emotion-processing brain regions in adults who were born very preterm

BACKGROUND

Very preterm birth (VPT; <32 weeks of gestation) has been associated with impairments in emotion regulation, social competence and communicative skills. However, the neuroanatomical mechanisms underlying such impairments have not been systematically studied. Here we investigated the functional integrity of the amygdala connectivity network in relation to the ability to recognize emotions from facial expressions in VPT adults.

METHOD

Thirty-six VPT-born adults and 38 age-matched controls were scanned at rest in a 3-T MRI scanner. Resting-state functional connectivity (rs-fc) was assessed with SPM8. A seed-based analysis focusing on three amygdalar subregions (centro-medial/latero-basal/superficial) was performed. Participants' ability to recognize emotions was assessed using dynamic stimuli of human faces expressing six emotions at different intensities with the Emotion Recognition Task (ERT).

RESULTS

VPT individuals compared to controls showed reduced rs-fc between the superficial subregion of the left amygdala, and the right posterior cingulate cortex (p = 0.017) and the left precuneus (p = 0.002). The VPT group further showed elevated rs-fc between the left superficial amygdala and the superior temporal sulcus (p = 0.008). Performance on the ERT showed that the VPT group was less able than controls to recognize anger at low levels of intensity. Anger scores were significantly associated with rs-fc between the superficial amygdala and the posterior cingulate cortex in controls but not in VPT individuals.

CONCLUSIONS

These findings suggest that alterations in rs-fc between the amygdala, parietal and temporal cortices could represent the mechanism linking VPT birth and deficits in emotion processing.

Hippocampal and motor fronto-cortical neuroligin1 is increased in an animal model of depression

Neuroligins (NLGNs) regulate synaptic excitability, neuronal signaling and sleep. We hypothesize that alteration of NLGNs is involved in the pathology of depression and tested the hypothesis in a model of depression using Wistar Kyoto (WKy) rat and its control, the Wistar (Wis) rat. We first evaluated behavioral deficits using the forced swim test and then characterized alterations of NLGN1 and NLGN2 with RT-PCR and Western Blotting in the prefrontal cortex, motor frontal cortex and hippocampus. Compared with controls of Wis rats, (1) the WKy rats had significantly shorter swim time and longer immobile time; (2) NLGN1 mRNA levels was higher in the motor frontal cortex and hippocampus in the WKy model; (3) NLGN1 protein was significantly higher in the motor frontal cortex, the prefrontal cortex and the hippocampus in the WKy model; (4) NLGN2 mRNA was significantly higher in the motor frontal cortex but significantly lower in the hippocampus in the WKy model. We concluded that NLGN1 gene and protein expression is higher in the motor frontal cortex, hippocampus and in the prefrontal cortex in the WKy rats suggesting that alterations of NLGN1 is involved in the pathology of depression but need to be further evaluated in human.

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.

Acute deep brain stimulation in the thalamic reticular nucleus protects against acute stress and modulates initial events of adult hippocampal neurogenesis

Deep brain stimulation (DBS) is used as an alternative therapeutic procedure for pharmacoresistant psychiatric disorders. Recently the thalamic reticular nucleus (TRN) gained attention due to the description of a novel pathway from the amygdala to this nucleus suggesting that may be differentially disrupted in mood disorders. The limbic system is implicated in the regulation of these disorders that are accompanied by neuroplastic changes. The hippocampus is highly plastic and shows the generation of new neurons, process affected by stress but positively regulated by antidepressant drugs. We explored the impact of applying acute DBS to the TRN (DBS-TRN) in male Wistar rats exposed to acute stress caused by the forced-swim Porsolt's test (FST) and on initial events of hippocampal neurogenesis. After the first session of forced-swim, rats were randomly subdivided in a DBS-TRN and a Sham group. Stimulated rats received 10min of DBS, thus the depressant-like behavior reflected as immobility was evaluated in the second session of forced-swim. Locomotricity was evaluated in the open field test. Cell proliferation and doublecortin-associated cells were quantified in the hippocampus of other cohorts of rats. No effects of electrode implantation were found in locomotricity. Acute DBS-TRN reduced immobility in comparison to the Sham group (p<0.001). DBS-TRN increased cell proliferation (Ki67 or BrdU-positive cells; p=0.02, p=0.02) and the number of doublecortin-cells compared to the Sham group (p<0.02). Similar effects were found in rats previously exposed to the first session of forced-swim. Our data could suggest that TRN brain region may be a promising target for DBS to treat intractable depression.

Brain structural connectivity distinguishes patients at risk for cognitive decline after carotid interventions

While brain connectivity analyses have been demonstrated to identify ill patients for a number of diseases, their ability to predict cognitive impairment after brain injury is not well established. Traditional post brain injury models, such as stroke, are limited for this evaluation because pre-injury brain connectivity patterns are infrequently available. Patients with severe carotid stenosis, in contrast, often undergo non-emergent revascularization surgery, allowing the collection of pre and post-operative imaging, may experience brain insult due to perioperative thrombotic/embolic infarcts or hypoperfusion, and can suffer post-operative cognitive decline. We hypothesized that a distributed function such as memory would be more resilient in patients with brains demonstrating higher degrees of modularity. To test this hypothesis, we analyzed preoperative structural connectivity graphs (using T1 and DWI MRI) for 34 patients that underwent carotid intervention, and evaluated differences in graph metrics using the Brain Connectivity Toolbox. We found that patients with lower binary component number, binary community number and weighted community number prior to surgery were at greater risk for developing cognitive decline. These findings highlight the promise of brain connectivity analyses to predict cognitive decline following brain injury and serve as a clinical decision support tool. Hum Brain Mapp 37:2185-2194, 2016. © 2016 Wiley Periodicals, Inc.

Disrupted Structural and Functional Connectivity in Prefrontal-Hippocampus Circuitry in First-Episode Medication-Naïve Adolescent Depression

BACKGROUND

Evidence implicates abnormalities in prefrontal-hippocampus neural circuitry in major depressive disorder (MDD). This study investigates the potential disruptions in prefrontal-hippocampus structural and functional connectivity, as well as their relationship in first-episode medication-naïve adolescents with MDD in order to investigate the early stage of the illness without confounds of illness course and medication exposure.

METHODS

Diffusion tensor imaging and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 26 first-episode medication-naïve MDD adolescents and 31 healthy controls (HC). Fractional anisotropy (FA) values of the fornix and the prefrontal-hippocampus functional connectivity was compared between MDD and HC groups. The correlation between the FA value of fornix and the strength of the functional connectivity in the prefrontal cortex (PFC) region showing significant differences between the two groups was identified.

RESULTS

Compared with the HC group, adolescent MDD group had significant lower FA values in the fornix, as well as decreased functional connectivity in four PFC regions. Significant negative correlations were observed between fornix FA values and functional connectivity from hippocampus to PFC within the HC group. There was no significant correlation between the fornix FA and the strength of functional connectivity within the adolescent MDD group.

CONCLUSIONS

First-episode medication-naïve adolescent MDD showed decreased structural and functional connectivity as well as deficits of the association between structural and functional connectivity shown in HC in the PFC-hippocampus neural circuitry. These findings suggest that abnormal PFC-hippocampus neural circuitry may present in the early onset of MDD and play an important role in the neuropathophysiology of MDD.

Hippocampal structure and function are maintained despite severe innate peripheral inflammation

Chronic peripheral inflammation mediated by cytokines such as TNFα, IL-1β, and IL-6 is associated with psychiatric disorders like depression and anxiety. However, it remains elusive which distinct type of peripheral inflammation triggers neuroinflammation and affects hippocampal plasticity resulting in depressive-like behavior. We hypothesized that chronic peripheral inflammation in the human TNF-α transgenic (TNFtg) mouse model of rheumatoid arthritis spreads into the central nervous system and induces depressive state manifested in specific behavioral pattern and impaired adult hippocampal neurogenesis. TNFtg mice showed severe erosive arthritis with increased IL-1β and IL-6 expression in tarsal joints with highly elevated human TNF-α levels in the serum. Intriguingly, IL-1β and IL-6 mRNA levels were not altered in the hippocampus of TNFtg mice. In contrast to the pronounced monocytosis in joints and spleen of TNFtg mice, signs of hippocampal microgliosis or astrocytosis were lacking. Furthermore, locomotion was impaired, but there was no locomotion-independent depressive behavior in TNFtg mice. Proliferation and maturation of hippocampal neural precursor cells as well as survival of newly generated neurons were preserved in the dentate gyrus of TNFtg mice despite reduced motor activity and peripheral inflammatory signature. We conclude that peripheral inflammation in TNFtg mice is mediated by chronic activation of the innate immune system. However, severe peripheral inflammation, though impairing locomotor activity, does not elicit depressive-like behavior. These structural and functional findings indicate the maintenance of hippocampal immunity, cellular plasticity, and behavior despite peripheral innate inflammation.

Reduced connectivity and inter-hemispheric symmetry of the sensory system in a rat model of vulnerability to developing depression

Defining the markers corresponding to a high risk of developing depression in humans would have major clinical significance; however, few studies have been conducted since they are not only complex but also require homogeneous groups. This study compared congenital learned helpless (cLH) rats, selectively bred for high stress sensitivity and learned helplessness (LH) behavior, to congenital non-learned helpless (cNLH) rats that were bred for resistance to uncontrollable stress. Naïve cLH rats show some depression-like behavior but full LH behavior need additional stress, making this model ideal for studying vulnerability to depression. Resting-state functional connectivity obtained from seed correlation analysis was calculated for multiple regions that were selected by anatomy AND by a data-driven approach, independently. Significance was determined by t-statistic AND by permutation analysis, independently. A significant reduction in functional connectivity was observed by both analyses in the cLH rats in the sensory, motor, cingulate, infralimbic, accumbens and the raphe nucleus. These reductions corresponded primarily to reduced inter-hemispheric connectivity. The main reduction however was in the sensory system. It is argued that reduced connectivity and inter-hemispheric connectivity of the sensory system reflects an internal convergence state which may precede other depressive symptomatology and therefore could be used as markers for vulnerability to the development of depression.

Circuit to construct mapping: a mathematical tool for assisting the diagnosis and treatment in major depressive disorder

Major depressive disorder (MDD) is a serious condition with a lifetime prevalence exceeding 16% worldwide. MDD is a heterogeneous disorder that involves multiple behavioral symptoms on the one hand and multiple neuronal circuits on the other hand. In this review, we integrate the literature on cognitive and physiological biomarkers of MDD with the insights derived from mathematical models of brain networks, especially models that can be used for fMRI datasets. We refer to the recent NIH research domain criteria initiative, in which a concept of "constructs" as functional units of mental disorders is introduced. Constructs are biomarkers present at multiple levels of brain functioning - cognition, genetics, brain anatomy, and neurophysiology. In this review, we propose a new approach which we called circuit to construct mapping (CCM), which aims to characterize causal relations between the underlying network dynamics (as the cause) and the constructs referring to the clinical symptoms of MDD (as the effect). CCM involves extracting diagnostic categories from behavioral data, linking circuits that are causal to these categories with use of clinical neuroimaging data, and modeling the dynamics of the emerging circuits with attractor dynamics in order to provide new, neuroimaging-related biomarkers for MDD. The CCM approach optimizes the clinical diagnosis and patient stratification. It also addresses the recent demand for linking circuits to behavior, and provides a new insight into clinical treatment by investigating the dynamics of neuronal circuits underneath cognitive dimensions of MDD. CCM can serve as a new regime toward personalized medicine, assisting the diagnosis and treatment of MDD.