Increased neural resources recruitment in the intrinsic organization in major depression

Brain connectivity correlates of the impact of a digital intervention for individuals with subjective cognitive decline on depression and IL-18

Late-life depression represents a significant health concern, linked to disruptions in brain connectivity and immune functioning, mood regulation, and cognitive function. This pilot study explores a digital intervention targeting mental health, brain health, and immune functioning in individuals aged 55-60 with subjective cognitive decline, elevated stress and depressive symptoms. Seventeen participants engaged in a two-week intervention comprising spatial cognition, psychological techniques based on mindfulness, attention-training exercises, and cognitive behavioral therapy. Pre-and post-intervention changes in resting-state functional connectivity, inflammation, and psychological health were evaluated. Key findings include: (1) Reduced self-reported depression with a large effect size, (2) Decreased connectivity within the default mode network (DMN), (3) Enhanced anticorrelation between the DMN-Salience networks that was associated with improved depression scores (4) Reduced salivary IL-18 concentration with a medium effect size, correlated with decreased DMN-amygdala connectivity. There was a trend towards reduced anxiety, with no significant changes in quality of life. To our knowledge, this is the first study to investigate the effect of digital intervention on immune markers, clinical behavioral outcomes, and brain function, demonstrating positive synergistic potential across all three levels. These preliminary findings, which need replication in larger, controlled studies, have important implications for basic science and scalable digital interventions.

Altered dynamic functional connectivity in antagonistic state in first-episode, drug-naïve patients with major depressive disorder

OBJECTIVE

Major depressive disorder (MDD) is known to be characterized by disrupted brain functional network connectivity (FNC) patterns, while the dynamic change mode of different functional networks is unclear. This study aimed to characterize specific dynamic alterations pattern on intrinsic FNC in MDD by combining static FNC (sFNC) and dynamic FNC (dFNC).

METHODS

A total of 48 first-episode drug-naïve MDD and 48 matched healthy controls (HCs) were included in this study. The sFNC and dFNC were analyzed using complete time-series and sliding window approach, respectively. Both sFNC and dFNC differences between groups were analyzed and associations between disease severity and aberrant FNC were explored.

RESULTS

MDD patients exhibited lower sFNC within and between sensory and motor networks than HC. Four dFNC states were identified, including a globally-weakly-connected state, a cognitive-control-dominated state, a globally-positively-connected state, and an antagonistic state. The antagonistic state was marked by strong positive connections within the sensorimotor domain and their anti-correlations with the executive-motor control domain. Notably, MDD patients exhibited significantly longer dwell time in the globally-weakly-connected state, at the cost of significantly shorter dwell time in the antagonistic state. Further, only the mean dwell time of this antagonistic state was significantly anticorrelated to disease severity measures.

CONCLUSIONS

Our study highlights the altered dynamics of the antagonistic state as a fundamental aspect of disrupted FNC in early MDD.

EEG coherences of the default mode network among patients comorbid with major depressive disorder and anxiety symptoms

BACKGROUND

Higher functional connectivity within the default mode network (DMN) has been found in functional magnetic resonance imaging (fMRI) studies of major depressive disorder (MDD). We used electroencephalogram (EEG) coherence as an index of functional connectivity to examine group differences in DMN between the MDD and healthy control (HC) groups during the resting state.

METHODS

MDD patients with comorbid anxiety symptoms (n = 154) and healthy controls (n = 165) completed the questionnaires of depression, anxiety, and rumination. A 19-channel EEG recording was measured under resting state for all participants. EEG coherences of the delta, theta, alpha, beta, and high beta in the anterior DMN (aDMN), posterior DMN (pDMN), aDMN-pDMN, DMN-parahippocampal gyrus (PHG), and DMN-temporal gyrus were compared between the two groups. The correlations between rumination, anxiety, and DMN coherence were examined in the MDD group.

RESULTS

(1) No difference was found in the delta, theta, alpha, and beta within the DMN brain regions between the two groups; the MDD group showed higher high beta coherence within DMN brain regions than the HC group. (2) Rumination was negatively correlated with theta coherence of aDMN, and positively correlated with beta coherence of aDMN and with alpha coherence of pDMN and DMN-PHG. (3) Anxiety was positively correlated with high beta coherence of aDMN, pDMN, and DMN-PHG.

CONCLUSIONS

MDD patients with comorbid anxiety symptoms exhibited hypercoherence within the DMN brain regions. Hypercoherences were related to symptoms of rumination, and anxiety may be a biomarker for MDD patients with comorbid anxiety symptoms.

Potential correlations between abnormal homogeneity of default mode network and personality or lipid level in major depressive disorder

BACKGROUND

Default mode network (DMN) is one of the most recognized resting-state networks in major depressive disorder (MDD). However, the homogeneity of this network in MDD remains incompletely explored. Therefore, this study aims to determine whether there is abnormal network homogeneity (NH) of the DMN in MDD patients. At the same time, correlations between clinical variables and brain functional connectivity are examined.

METHODS

We enrolled 42 patients diagnosed with MDD and 42 HCs. A variety of clinical variables were collected, and data analysis was conducted using the NH and independent component analysis methods.

RESULTS

The study shows that MDD patients have higher NH values in the left superior medial prefrontal cortex (MPFC) and left posterior cingulate cortex (PCC) compared to HCs. Additionally, there is a positive correlation between NH values of the left superior MPFC and Eysenck Personality Questionnaire values. NH values of the left PCC are positively linked to CHOL levels, LDL levels, and utilization scores. However, these correlations lose significance after the Bonferroni correction.

CONCLUSION

Our findings indicate the presence of abnormal DMN homogeneity in MDD, underscoring the significance of DMN in the pathophysiology of MDD. Simultaneously, the study provides preliminary evidence for the correlation between clinical variables and brain functional connectivity.

The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder

Background

A seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care.

Methods

Forty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology.

Results

We observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = -0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = -0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = -0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error-corrected p < .05).

Conclusions

Subgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.

A novel biomarker selection method using multimodal neuroimaging data

Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.

Changed brain entropy and functional connectivity patterns induced by electroconvulsive therapy in majoy depression disorder

OBJECTIVE

Our objective is to innovatively integrate both linear and nonlinear characteristics of brain signals in Electroconvulsive Therapy (ECT) research, with the goal of uncovering deeper insights into the pathogenesis of Major Depressive Disorder (MDD) and identifying novel targets for other physical intervention therapies.

METHODS

We measured brain entropy (BEN) in 42 MDD patients and 42 matched healthy controls (HC) using rs-fMRI data. Brain regions that differed significantly in patients with MDD before and after ECT were extracted. Then, we use these brain regions as seed points to investigate the differences in whole-brain resting-state functional connectivity (RSFC) patterns before and after ECT.

RESULTS

Compared to HCs, patients had higher BEN levels in the right precuneus (PCUN.R) and right angular gyrus (ANG.R). After ECT, patients had lower BEN levels in the PCUN.R and ANG.R. Compared with before ECT, patients showed significantly increased RSFC after ECT between the PCUN.R and right middle temporal gyrus and ANG.R. Significantly increased RSFC was observed between the ANG.R and right middle frontal gyrus and right supramarginal gyrus after ECT.

CONCLUSION

Combining the linear and nonlinear characteristics of brain signals can effectively explore the pathogenesis of depression and provide new targets for ECT.

Causal associations and genetic overlap between COVID-19 and intelligence

OBJECTIVE

COVID-19 might cause neuroinflammation in the brain, which could decrease neurocognitive function. We aimed to evaluate the causal associations and genetic overlap between COVID-19 and intelligence.

METHODS

We performed Mendelian randomization (MR) analyses to assess potential associations between three COVID-19 outcomes and intelligence (N = 269 867). The COVID phenotypes included severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (N = 2 501 486), hospitalized COVID-19 (N = 1 965 329) and critical COVID-19 (N = 743 167). Genome-wide risk genes were compared between the genome-wide association study (GWAS) datasets on hospitalized COVID-19 and intelligence. In addition, functional pathways were constructed to explore molecular connections between COVID-19 and intelligence.

RESULTS

The MR analyses indicated that genetic liabilities to SARS-CoV-2 infection (odds ratio [OR]: 0.965, 95% confidence interval [CI]: 0.939-0.993) and critical COVID-19 (OR: 0.989, 95% CI: 0.979-0.999) confer causal effects on intelligence. There was suggestive evidence supporting the causal effect of hospitalized COVID-19 on intelligence (OR: 0.988, 95% CI: 0.972-1.003). Hospitalized COVID-19 and intelligence share 10 risk genes within 2 genomic loci, including MAPT and WNT3. Enrichment analysis showed that these genes are functionally connected within distinct subnetworks of 30 phenotypes linked to cognitive decline. The functional pathway revealed that COVID-19-driven pathological changes within the brain and multiple peripheral systems may lead to cognitive impairment.

CONCLUSIONS

Our study suggests that COVID-19 may exert a detrimental effect on intelligence. The tau protein and Wnt signaling may mediate the influence of COVID-19 on intelligence.

Changes in brain functional networks in remitted major depressive disorder: a six-month follow-up study

BACKGROUND

Patients with remitted major depressive disorder (rMDD) show abnormal functional connectivity of the central executive network (CEN), salience networks (SN) and default mode network (DMN). It is unclear how these change during remission, or whether changes are related to function.

METHODS

Three spatial networks in 17 patients with rMDD were compared between baseline and the six-month follow-up, and to 22 healthy controls. Correlations between these changes and psychosocial functioning were also assessed.

RESULTS

In the CEN, patients at baseline had abnormal functional connectivity in the right anterior cingulate, right dorsolateral prefrontal cortex (DLPFC) and inferior parietal lobule (IPL) compare with HCs. There were functional connection differences in the right DLPFC and left IPL at baseline during follow-up. Abnormal connectivity in the right DLPFC and medial prefrontal cortex (mPFC) were found at follow-up. In the SN, patients at baseline had abnormal functional connectivity in the insula, left anterior cingulate, left IPL, and right precuneus; compared with baseline, patients had higher connectivity in the right DLPFC at follow-up. In the DMN, patients at baseline had abnormal functional connectivity in the right mPFC. Resting-state functional connectivity of the IPL and DLPFC in the CEN correlated with psychosocial functioning.

CONCLUSIONS

At six-month follow-up, the CEN still showed abnormal functional connectivity in those with rMDD, while anomalies in the SN and DMN has disappeared. Resting-state functional connectivity of the CEN during early rMDD is associated with psychosocial function.

CLINICAL TRIALS REGISTRATION

Pharmacotherapy and Psychotherapy for MDD after Remission on Psychology and Neuroimaging. https://www.

CLINICALTRIALS

gov/ , registration number: NCT01831440 (15/4/2013).

Predicting Dimensional Antidepressant Response to Repetitive Transcranial Magnetic Stimulation using Pretreatment Resting-state Functional Connectivity

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression and has been shown to modulate resting-state functional connectivity (RSFC) of depression-relevant neural circuits. To date, however, few studies have investigated whether individual treatment-related symptom changes are predictable from pretreatment RSFC. We use machine learning to predict dimensional changes in depressive symptoms using pretreatment patterns of RSFC. We hypothesized that changes in dimensional depressive symptoms would be predicted more accurately than scale total scores. Patients with depression (n=26) underwent pretreatment RSFC MRI. Depressive symptoms were assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Random forest regression (RFR) models were trained and tested to predict treatment-related symptom changes captured by the HDRS-17, HDRS-6 and three previously identified HDRS subscales: core mood/anhedonia (CMA), somatic disturbances, and insomnia. Changes along the CMA, HDRS-17, and HDRS-6 were predicted significantly above chance, with 9%, 2%, and 2% of out-of-sample outcome variance explained, respectively (all p<0.01). CMA changes were predicted more accurately than the HDRS-17 (p<0.05). Higher baseline global connectivity (GC) of default mode network (DMN) subregions and the somatomotor network (SMN) predicted poorer symptom reduction, while higher GC of the right dorsal attention (DAN) frontoparietal control (FPCN), and visual networks (VN) predicted reduced CMA symptoms. HDRS-17 and HDRS-6 changes were predicted with similar GC patterns. These results suggest that RSFC spanning the DMN, SMN, DAN, FPCN, and VN subregions predict dimensional changes with greater accuracy than syndromal changes following rTMS. These findings highlight the need to assess more granular clinical dimensions in therapeutic studies, particularly device neuromodulation studies, and echo earlier studies supporting that dimensional outcomes improve model accuracy.

Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies

In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.

Resting state connectivity predictors of symptom change during gaze-contingent music reward therapy of social anxiety disorder

BACKGROUND

Social anxiety disorder (SAD) is common, first-line treatments are often only partially effective, and reliable predictors of treatment response are lacking. Here, we assessed resting state functional connectivity (rsFC) at pre-treatment and during early treatment as a potential predictor of response to a novel attention bias modification procedure, gaze-contingent music reward therapy (GC-MRT).

METHODS

Thirty-two adults with SAD were treated with GC-MRT. rsFC was assessed with multi-voxel pattern analysis of fMRI at pre-treatment and after 2-3 weeks. For comparison, 20 healthy control (HC) participants without treatment were assessed twice for rsFC over the same time period. All SAD participants underwent clinical evaluation at pre-treatment, early-treatment (week 2-3), and post-treatment.

RESULTS

SAD and depressive symptoms improved significantly from pre-treatment to post-treatment. After 2-3 weeks of treatment, decreased connectivity between the executive control network (ECN) and salience network (SN), and increased connectivity within the ECN predicted improvement in SAD and depressive symptoms at week 8. Increased connectivity between the ECN and default mode network (DMN) predicted greater improvement in SAD but not depressive symptoms at week 8. Connectivity within the DMN decreased significantly after 2-3 weeks of treatment in the SAD group, while no changes were found in HC over the same time interval.

CONCLUSION

We identified early changes in rsFC during a course of GC-MRT for SAD that predicted symptom change. Connectivity changes within the ECN, ECN-DMN, and ECN-SN may be related to mechanisms underlying the clinical effects of GC-MRT and warrant further study in controlled trials.

Altered fronto-central theta-gamma coupling in major depressive disorder during auditory steady-state responses

OBJECTIVE

Neural oscillations during sensory and cognitive events interact at different frequencies. However, such evidence in major depressive disorder (MDD) remains scarce. We explored the possible abnormal neural oscillations in MDD by analyzing theta-phase/gamma-amplitude coupling (TGC).

METHODS

Resting-state and auditory steady-state response (ASSR) electroencephalography recordings were obtained from 35 first-episode MDD and 35 healthy controls (HCs). TGC during rest, ASSR stimulation, and ASSR baseline between and within groups were analyzed to evaluate MDD alterations. Receiver operating characteristic (ROC), TGC comparison between MDD severity subgroups (mild, moderate, major), and correlations were investigated to determine the potential use of altered TGC for identifying MDD.

RESULTS

In MDD, left fronto-central TGC decreased during stimulation, while right fronto-central TGC increased during baseline. The area under ROC curve for altered TGC was 0.863. Furthermore, during stimulation, moderate and major MDD groups exhibited significantly lower TGC than mild group, and fronto-central TGC was negatively correlated with depression scale scores.

CONCLUSIONS

Our results provided the first evidence for an abnormal TGC response of fronto-central regions in MDD during an ASSR task. Importantly, altered TGC may be promising biomarkers of MDD.

SIGNIFICANCE

Our findings enhance the understanding of physiological mechanisms underlying MDD and aid in its clinical diagnosis.

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.

The impacts of anhedonia on brain functional alterations in patients with major depressive disorder: A resting-state functional magnetic resonance imaging study of regional homogeneity

BACKGROUND

Anhedonia, as one of the core manifestations of major depressive disorder (MDD), has an effect on prognosis of the disease. However, the neuropathology of MDD is complex and the neural basis of anhedonia remains unclear. The aim of the present study was to investigate the impacts of anhedonia on brain functional alterations in patients with MDD.

METHODS

A total of 62 individuals including MDD patients with anhedonia (n = 22), MDD patients without anhedonia (n = 20), and healthy controls (HCs, n = 20) were recruited. All participants underwent resting-state functional magnetic resonance imaging scanning and intrinsic brain function was explored by using regional homogeneity (ReHo) method. A two-sample t-test was performed to explore ReHo differences between MDD patients and HCs, then analysis of variance (ANOVA) was introduced to obtain brain regions with significant differences among three groups, and finally post hoc tests were calculated for inter-group comparisons. Correlations between ReHo values of each survived area and clinical characteristics in MDD patients were further analyzed.

RESULTS

Compared with HCs, MDD showed increased ReHo in the left superior temporal gyrus (STG) and bilateral inferior frontal gyrus (IFG), as well as decreased ReHo in the left superior frontal gyrus (SFG). Interestingly, this relationship was attenuated and no longer significant after consideration for the effect of anhedonia in MDD patients. MDD patients with anhedonia were more likely to exhibit decreased ReHo in the left SFG and left middle cingulate gyrus (MCG) when comparing to HCs. No significant difference was found between MDD patients without anhedonia and HCs, either the two groups of MDD patients. There was no significant association between ReHo values of each survived area and clinical characteristics in MDD patients.

CONCLUSIONS

The present results suggest that the impacts of anhedonia on brain functional alterations in MDD should be emphasized and disturbed intrinsic brain function in the frontal-limbic regions may be associated with anhedonia in MDD patients.

Cross-lagged Relationships Between Depressive Symptoms and Altered Default Mode Network Connectivity Over the Course of Adolescence

BACKGROUND

Although the peak onset of depressive symptoms occurs during adolescence, very few studies have directly examined depression-related changes in resting-state (RS) default mode network activity during adolescence, controlling for potential neural markers of risk.

METHODS

This study used data from a longitudinal adolescent cohort to investigate age-specific, persistent (i.e., lagged), and dynamic associations between RS functional connectivity within the default mode network and depressive symptoms during adolescence using a random intercept cross-lagged panel framework. The Neuroventure sample consisted of 151 adolescents ages 12-14 at study entry without any neurological illness who were assessed three times during a 5-year follow-up with 97% follow-up across the three assessments. Depressive symptoms were measured using the depression subscale of the Brief Symptoms Inventory. RS functional magnetic resonance imaging data were collected using a 3T Siemens Magnetom Trio scanner in a single 6-minute sequence.

RESULTS

After controlling for relationships between random intercepts, future depression risk was predicted by RS couplings in the perigenual anterior cingulate cortex and anterior dorsomedial prefrontal cortex (β = -0.69, p = .014) and in the left inferior parietal lobule and anterior superior frontal gyrus (β = -0.43, p = .035). Increases in depressive symptoms at previous time points significantly predicted changes in functional connectivity between the posterior cingulate cortex and the precuneus and posterior middle temporal gyrus (β = 0.37, p = .039) and between the dorsal precuneus and posterior middle temporal gyrus (β = 0.47, p = .036).

CONCLUSIONS

This study was able to disassociate the RS brain markers of depression from those that appear to follow early-onset depression.

Investigating default mode network connectivity disruption in children of mothers with depression

BACKGROUND

Exposure to maternal major depressive disorder (MDD) bears long-term negative consequences for children's well-being; to date, no research has examined how exposure at different stages of development differentially affects brain functioning.

AIMS

Utilising a unique cohort followed from birth to preadolescence, we examined the effects of early versus later maternal MDD on default mode network (DMN) connectivity.

METHOD

Maternal depression was assessed at birth and ages 6 months, 9 months, 6 years and 10 years, to form three groups: children of mothers with consistent depression from birth to 6 years of age, which resolved by 10 years of age; children of mothers without depression; and children of mothers who were diagnosed with MDD in late childhood. In preadolescence, we used magnetoencephalography and focused on theta rhythms, which characterise the developing brain.

RESULTS

Maternal MDD was associated with disrupted DMN connectivity in an exposure-specific manner. Early maternal MDD decreased child connectivity, presenting a profile typical of early trauma or chronic adversity. In contrast, later maternal MDD was linked with tighter connectivity, a pattern characteristic of adult depression. Aberrant DMN connectivity was predicted by intrusive mothering in infancy and lower mother-child reciprocity and child empathy in late childhood, highlighting the role of deficient caregiving and compromised socio-emotional competencies in DMN dysfunction.

CONCLUSIONS

The findings pinpoint the distinct effects of early versus later maternal MDD on the DMN, a core network sustaining self-related processes. Results emphasise that research on the influence of early adversity on the developing brain should consider the developmental stage in which the adversity occured.

Altered Brain Functional Asymmetry in Patients With Major Depressive Disorder Related to Gastrointestinal Symptoms

Objective

Disrupted brain functional asymmetry has been reported in major depressive disorder (MDD). The comorbidity may be a crucial factor to this functional asymmetry. It is quite common that gastrointestinal (GI) symptoms are comorbid with MDD, but limited evidence focuses on the effect of GI comorbidity on the neuropathology of MDD from a functional lateralization perspective.

Methods

Resting-state functional magnetic resonance imaging was obtained in 28 healthy controls (HCs), 35 MDD patients with GI symptoms (GI-MDD patients), and 17 patients with MDD without GI symptoms (nGI-MDD patients). The parameter of asymmetry (PAS) was used to analyze the imaging data and evaluate the changes of functional asymmetry.

Results

The GI-MDD patients showed increased PAS scores in the left inferior frontal gyrus (IFG) and superior medial prefrontal cortex (MPFC) and decreased PAS scores in the right postcentral gyrus in comparison with nGI-MDD patients. The PAS scores of the left IFG and left superior MPFC were correlated with the severity of GI problems and could be applied to distinguish GI-MDD patients from nGI-MDD patients with an accuracy, a sensitivity, and a specificity of 92.31, 100, and 76.47%, respectively. Furthermore, GI-MDD and nGI-MDD patients both displayed increased PAS scores in the PCC/precuneus.

Conclusions

This study revealed the influence of concomitant GI symptoms on functional asymmetry in MDD patients. Increased PAS scores of the left IFG and superior MPFC might represent an unbalanced regulation of brain over GI function and had the potential to be regarded as distinctive features related to functional GI symptoms in MDD.

Dysfunctional Interaction Between the Dorsal Attention Network and the Default Mode Network in Patients With Type 2 Diabetes Mellitus

The risk of cognitive impairment in patients with type 2 diabetes mellitus (T2DM) is significantly higher than that in the general population, but the exact neurophysiological mechanism underlying this is still unclear. An abnormal change in the intrinsic anticorrelation of the dorsal attention network (DAN) and the default mode network (DMN) is thought to be the mechanism underlying cognitive deficits that occur in many psychiatric disorders, but this association has rarely been tested in T2DM. This study explored the relationship between the interaction patterns of the DAN-DMN and clinical/cognitive variables in patients with T2DM. Forty-four patients with T2DM and 47 sex-, age-, and education-matched healthy controls (HCs) underwent neuropsychological assessments, independent component analysis (ICA), and functional network connection analysis (FNC). The relationship of DAN-DMN anticorrelation with the results of a battery of neuropsychological tests was also assessed. Relative to the HC group, the DMN showed decreased functional connectivity (FC) in the right precuneus, and the DAN showed decreased FC in the left inferior parietal lobule (IPL) in patients with T2DM. Subsequent FNC analysis revealed that, compared with the HC group, the T2DM patients displayed significantly increased inter-network connectivity between the DAN and DMN. These abnormal changes were correlated with the scores of multiple neuropsychological assessments (P < 0.05). These findings indicate abnormal changes in the interaction patterns of the DAN-DMN may be involved in the neuropathology of attention and general cognitive dysfunction in T2DM patients.

Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder

Background: Major depressive disorder (MDD) is a severe mental illness marked by a continuous sense of sadness and a loss of interest. The default mode network (DMN) is a group of brain areas that are more active during rest and deactivate when engaged in task-oriented activities. The DMN of MDD has been found to have aberrant static functional network connectivity (FNC) in recent studies. In this work, we extend previous findings by evaluating dynamic functional network connectivity (dFNC) within the DMN subnodes in MDD. Methods: We analyzed resting-state functional magnetic resonance imaging data of 262 patients with MDD and 277 healthy controls (HCs). We estimated dFNCs for seven subnodes of the DMN, including the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), using a sliding window approach, and then clustered the dFNCs into five brain states. Classification of MDD and HC subjects based on state-specific FC was performed using a logistic regression classifier. Transition probabilities between dFNC states were used to identify relationships between symptom severity and dFNC data in MDD patients. Results: By comparing state-specific FNC between HC and MDD, a disrupted connectivity pattern was observed within the DMN. In more detail, we found that the connectivity of ACC is stronger, and the connectivity between PCu and PCC is weaker in individuals with MDD than in those of HC subjects. In addition, MDD showed a higher probability of transitioning from a state with weaker ACC connectivity to a state with stronger ACC connectivity, and this abnormality is associated with symptom severity. This is the first research to look at the dFC of the DMN in MDD with a large sample size. It provides novel evidence of abnormal time-varying DMN configuration in MDD and offers links to symptom severity in MDD subjects. Impact Statement This study is the first attempt that explored the temporal change on default mode network (DMN) connectivity in a relatively large cohort of patients with major depressive disorder (MDD). We also introduced a new hypothesis that explains the inconsistency in DMN functional network connectivity (FNC) comparison between MDD and healthy control based on static FNC in the previous literature. Additionally, our findings suggest that within anterior cingulate cortex connectivity and the connectivity between the precuneus and posterior cingulate cortex are the potential biomarkers for the future intervention of MDD.

Causal Interactions Between the Default Mode Network and Central Executive Network in Patients with Major Depression

Two different but interacting neural systems exist in the human brain: the task positive networks and task negative networks. One of the most important task positive networks is the central executive network (CEN), while the task negative network generally refers to the default mode network (DMN), which usually demonstrates task-induced deactivation. Although previous studies have clearly shown the association of both the CEN and DMN with major depressive disorder (MDD), how the causal interactions between these two networks change in depressed patients remains unclear. In the current study, 99 subjects (43 patients with MDD and 56 healthy controls) were recruited with their resting-state fMRI data collected. After data preprocessing, spectral dynamic causal modeling (spDCM) was used to investigate the causal interactions within and between the DMN and CEN. Group commonalities and differences in causal interaction patterns within and between the CEN and DMN in patients and controls were assessed by a parametric empirical Bayes (PEB) model. Both subject groups demonstrated significant effective connectivity between regions of the CEN and DMN. In particular, we detected inhibitory influences from the CEN to the DMN with node-level PEB analyses, which may help to explain the anticorrelations between these two networks consistently reported in previous studies. Compared with healthy controls, patients with MDD showed increased effective connectivity within the CEN and decreased connectivity from regions of the CEN to DMN, suggesting impaired control of the DMN by the CEN in these patients. These findings might provide new insights into the neural substrates of MDD.

Prospective study on resting state functional connectivity in adolescents with major depressive disorder after antidepressant treatment

Recent advances in functional magnetic resonance imaging (fMRI) have resulted in many studies on resting-state functional connectivity (rsFC) in depressed patients. Previous studies have shown alterations between multiple brain areas, such as the prefrontal cortex, anterior cingulate cortex, and basal ganglia, but there are very few prospective studies with a longitudinal design on adolescent depression patients. We therefore investigated the change in positive rsFC in a homogeneous drug-naïve adolescent group after 12 weeks of antidepressant treatment. Functional neuroimaging data were collected and analyzed from 32 patients and 27 healthy controls. Based on previous literature, the amygdala, anterior cingulate cortex (ACC), insula, hippocampus, and dorsolateral prefrontal cortex (DLPFC) were selected as seed regions. Seed-to-voxel analyses were performed between pre- and post-treatment states as well as between the patients and controls at baseline. The positive rsFC between the right DLPFC and the left putamen/right frontal operculum were shown to be higher in patients than in the controls. The positive rsFC between the left DLPFC and left putamen/left lingual gyrus was also higher in the patients than in the controls. The positive rsFC between the right dorsal ACC and the left precentral gyrus had reduced after the 12-week antidepressant treatment. Regions involved in the frontolimbic circuit showed changes in the positive rsFC in the depressed adolescents as compared to in the healthy controls. There were also significant changes in the positive rsFC after 12-weeks of antidepressant treatment. The involved regions were associated with emotional regulation, cognitive functioning, impulse control, and visual processing.

Baseline Functional Connectivity in Resting State Networks Associated with Depression and Remission Status after 16 Weeks of Pharmacotherapy: A CAN-BIND Report

Understanding the neural underpinnings of major depressive disorder (MDD) and its treatment could improve treatment outcomes. So far, findings are variable and large sample replications scarce. We aimed to replicate and extend altered functional connectivity associated with MDD and pharmacotherapy outcomes in a large, multisite sample. Resting-state fMRI data were collected from 129 patients and 99 controls through the Canadian Biomarker Integration Network in Depression. Symptoms were assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS). Connectivity was measured as correlations between four seeds (anterior and posterior cingulate cortex, insula and dorsolateral prefrontal cortex) and all other brain voxels. Partial least squares was used to compare connectivity prior to treatment between patients and controls, and between patients reaching remission (MADRS ≤ 10) early (within 8 weeks), late (within 16 weeks), or not at all. We replicated previous findings of altered connectivity in patients. In addition, baseline connectivity of the anterior/posterior cingulate and insula seeds differentiated patients with different treatment outcomes. The stability of these differences was established in the largest single-site subsample. Our replication and extension of altered connectivity highlighted previously reported and new differences between patients and controls, and revealed features that might predict remission prior to pharmacotherapy. Trial registration:  ClinicalTrials.gov: NCT01655706.

Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification

If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.

Resting state functional connectivity abnormalities and delayed recall performance in patients with amnestic mild cognitive impairment

Amnestic Mild Cognitive Impairment (aMCI) represents the transition between healthy aging and Alzheimer's dementia (AD) wherein gradual impairment of cognitive abilities, especially memory sets in. Impairment in episodic memory, especially delayed recall, is a hallmark of AD and therefore, patients with aMCI with more severe impairment in episodic memory are considered to be at greater risk of imminent conversion to AD. Brain structural and functional abnormalities were examined by comparing gray matter volumes, white matter micro-structural integrity and resting state functional connectivity (rsFC), between patients with aMCI (n = 46) having lower vs. higher episodic memory delayed recall (EM-DR) performance scores, correcting the influences of age, sex, number of years of formal education and total brain volumes using voxel-based morphometry, whole-brain tract based spatial statistics and dual regression analysis respectively. 'Low' performers (n = 27) when compared to 'high' performers (n = 19) showed significantly increased rsFC in the dorsal attention network (DAN) and central executive network (CEN) in the absence of demonstrable gray matter volumetric or white matter micro-structural integrity differences at family-wise error (FWE) corrected (p < 0.05) significance threshold. Follow-up data available for 38 (low performers = 22; high performers = 16) of the above 46 subjects (82.60% follow-up rate) over a median follow-up period of 24.5 months revealed that 7 subjects (18.42%) had converted to dementia. These converted subjects included 5 of the 22 low performers (22.72%) and 2 of the 16 high performers (12.5%) within the follow-up sample (n = 38). The results of the study indicate that imminent conversion of aMCI to dementia is higher in low performers in comparison to high performers, which may be characterized by increased rsFC in task positive networks, viz., DAN and CEN, as opposed to gray or white matter structural changes. This finding, therefore, might be considered as a prognostic indicator of progression from aMCI to dementia.

Subgenual cingulate connectivity and hippocampal activation are related to MST therapeutic and adverse effects

Aberrant connectivity between the dorsolateral prefrontal cortex (DLPFC) and the subgenual cingulate cortex (SGC) has been linked to the pathophysiology of depression. Indirect evidence also links hippocampal activation to the cognitive side effects of seizure treatments. Magnetic seizure therapy (MST) is a novel treatment for patients with treatment resistant depression (TRD). Here we combine transcranial magnetic stimulation with electroencephalography (TMS-EEG) to evaluate the effects of MST on connectivity and activation between the DLPFC, the SGC and hippocampus (Hipp) in patients with TRD. The TMS-EEG was collected from 31 TRD patients prior to and after an MST treatment trial. Through TMS-EEG methodology we evaluated significant current scattering (SCS) as an index of effective connectivity between the SGC and left DLPFC. Significant current density (SCD) was used to assess activity at the level of the Hipp. The SCS between the SGC and DLPFC was reduced after the course of MST (p < 0.036). The DLPFC-SGC effective connectivity reduction correlated with the changes in Hamilton depression score pre-to-post treatment (R = 0.46; p < 0.031). The SCD localized to the Hipp was reduced after the course of MST (p < 0.015), and the SCD change was correlated with montreal cognitive assessment (MOCA) scores pre-post the course of MST (R = -0.59; p < 0.026). Our findings suggest that MST treatment is associated with SGC-DLPFC connectivity reduction and that changes to cognition are associated with Hipp activation reduction. These findings demonstrate two distinct processes which drive efficacy and side effects separately, and might eventually aid in delineating physiological TRD targets in clinical settings.

Diagnosis of major depressive disorder using whole-brain effective connectivity networks derived from resting-state functional MRI

OBJECTIVE

It is important to improve identification accuracy for possible early intervention of major depressive disorder (MDD). Recently, effective connectivity (EC), defined as the directed influence of spatially distant brain regions on each other, has been used to find the dysfunctional organization of brain networks in MDD. However, little is known about the ability of whole-brain resting-state EC features in identification of MDD. Here, we employed EC by whole-brain analysis to perform MDD diagnosis.

APPROACH

In this study, we proposed a high-order EC network capturing high-level relationship among multiple brain regions to discriminate 57 patients with MDD from 60 normal controls (NC). In high-order EC networks and traditional low-order EC networks, we utilized the network properties and connection strength for classification. Meanwhile, the support vector machine (SVM) was employed for model training. Generalization of the results was supported by 10-fold cross-validation.

MAIN RESULTS

The classification results showed that the high-order EC network performed better than the low-order EC network in diagnosing MDD, and the integration of these two networks yielded the best classification precision with 95% accuracy, 98.83% sensitivity, and 91% specificity. Furthermore, we found that the abnormal connections of high-order EC in MDD patients involved multiple widely concerned functional subnets, particularly the default mode network and the cerebellar network.

SIGNIFICANCE

The current study indicates whole-brain EC networks, measured by our high-order method, may be promising biomarkers for clinical diagnosis of MDD, and the complementary between high-order and low-order EC will better guide patients to get early interventions as well as treatments.

A bayesian approach to examining default mode network functional connectivity and cognitive performance in major depressive disorder

To reconcile the inconsistency of the association between the resting-state functional connectivity (RSFC) and cognitive performance in healthy and depressed groups due to high variance of both measures, we proposed a Bayesian spatio-temporal model to precisely and accurately estimate the RSFC in depressed and nondepressed participants. This model was employed to estimate spatially-adjusted functional connectivity (saFC) in the extended default mode network (DMN) that was hypothesized to correlate with cognitive performance in both depressed and nondepressed. Multiple linear regression models were used to study the relationship between DMN saFC and cognitive performance scores measured in the following four cognitive domains while adjusting for age, sex, and education. In ROI pairs including the posterior cingulate (PCC) and anterior cingulate (ACC) cortex regions, the relationship between connectivity and cognition was found only with the Bayesian approach. Moreover, only the Bayesian approach was able to detect a significant diagnostic difference in the association in ROI pairs, including both PCC and ACC regions, due to smaller variance for the saFC estimator. The results confirm that a reliable and precise saFC estimator, based on the Bayesian model, can foster scientific discovery that may not be feasible with the conventional ROI-based FC estimator (denoted as 'AVG-FC').

Meditation and Endocrine Health and Wellbeing

Meditation is a popular practice for reducing stress and improving mental health and wellbeing. Its effects are mediated largely by the endocrine system, including the hypothalamic-pituitary-adrenal axis, the hypothalamic-pituitary-thyroid axis, and the renin-angiotensin-aldosterone system, and energy homeostasis. The limited evidence available indicates that changes associated with endocrine function following meditation correspond with improvements in mental health. However, this field of study is hampered by a lack of consensus as to definition and types of meditation and the mixed quality of reported studies. Moreover, the exact mechanisms by which meditation operates remain unclear and more robust studies are required to explore this by delineating the target populations, forms, dosages, and modes of delivery of meditation, comparison groups, and health experiences and outcomes used.

Neuroticism modulates neural activities of posterior cingulate cortex and thalamus during psychosocial stress processing

OBJECTIVE

Individuals with higher neuroticism are vulnerable to stress and are prone to develop depression, however, the neural mechanisms underlying it have not been clarified clearly.

METHOD

The Montreal Imaging Stress Task (MIST) was administered to 148 healthy adults during functional magnetic resonance imaging (fMRI). Whole-brain voxel-wise regression analyses were used to detect associations of neuroticism with neural activity involved in perceiving and processing psychosocial stress. In addition, two-sample t-tests were conducted between the high-neurotic and low-neurotic group in order to supplement the results found in regression analyses.

RESULTS

Higher neuroticism scores were associated with higher activities in the posterior cingulate cortex (PCC)/precuneus and thalamus (p < 0.05, false discovery rate correction). Moreover, two sample t-tests also revealed that the high-neurotic group had higher neural stress responses in precuneus and bilateral thalamus in comparison to the low-neurotic group (p < 0.05, false discovery rate correction).

LIMITATIONS

Our study mainly recruited young adults, which may limit the generalizability of our findings.

CONCLUSIONS

Our findings highlight the crucial role of PCC/precuneus and thalamus in the association between neuroticism and stress and may provide insight into the cognitive model of depression.

Topological reorganization of the default mode network in patients with poststroke depressive symptoms: A resting-state fMRI study

OBJECTIVE

This study mapped the topological configuration of the default mode network (DMN) in patients with depressive symptoms after acute ischemic stroke.

METHODS

The study sample comprised 63 patients: 36 with poststroke depressive symptoms (PSD) and 37 without PSD matched according to age, gender and the severity of stroke. PSD was defined by a cutoff of ≥ 7 on the 15-item Geriatric Depression Scale (GDS). Resting-state functional magnetic resonance imaging (fMRI) was used to examine functional connectivity (FC) to reconstruct the DMN. Network based statistics estimated the FC differences of the DMN between the PSD and non-PSD groups. Graph theoretical approaches were used to characterize the topological properties of this network.

RESULTS

The study sample mainly comprised patients with mild to moderate stroke. A widespread hyper-connected configuration of the functional DMN was characterized in PSD group. The orbital frontal, dorsolateral prefrontal, dorsal medial prefrontal and, ventromedial prefrontal corticis, the middle temporal gyrus and the inferior parietal lobule were the functional hubs related to PSD. The nodal topology in inferior parietal lobule and superior frontal gyrus, overlapping with dorsal medial prefrontal and, ventromedial prefrontal cortices, tended to be functionally integrated in patients with PSD. After False Discovery Rate correction, no significant difference between the PSD and non-PSD groups was found with respect to the global and nodal metrics of the DMN. However, the correlations between these altered network metrics and severity of PSD were lacking.

LIMITATIONS

The diagnosis of PSD was based on the GDS score rather than established with a structured clinical interview.

CONCLUSIONS

The DMN in PSD was functionally integrated and more specialized in some core hubs such as the inferior parietal lobule and dorsal prefrontal cortex. The configuration of the subnetwork like DMN may be more essential in the pathogenesis of PSD than single stroke lesions.

In vivo metabotropic glutamate receptor 5 availability-associated functional connectivity alterations in drug-naïve young adults with major depression

There has been increasing interest in glutamatergic neurotransmission as a putative underlying mechanism of depressive disorders. We performed [¹¹C]ABP688 positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI) in drug-naïve young adult patients with major depression to examine alterations in metabotropic glutamate receptor-5 (mGluR5) availability, and to investigate their functional significance relating to neural systems-level changes in major depression. Sixteen psychotropic drug-naïve patients with major depression without comorbidity (median age: 22.8 years) and fifteen matched healthy controls underwent [¹¹C]ABP688 PET imaging and 3-T MRI. For mGluR5 availability, we quantified [¹¹C]ABP688 binding potential (BP_ND) using the simplified reference tissue model. Seed-based functional connectivity analysis was performed using rs-fMRI data with regions derived from quantitative [¹¹C]ABP688 PET analysis as seeds. In region-of-interest (ROI)-based and voxel-based analyses, the [¹¹C]ABP688 BP_ND was significantly lower in patients than in controls in the prefrontal cortex ROI and in voxel clusters within the prefrontal, temporal, and parietal cortices, and supramarginal gyrus. The [¹¹C]ABP688 BP_ND seed-based functional connectivity analysis showed significantly less negative connectivity from the inferior parietal cortex seed to the fusiform gyrus and inferior occipital cortex in patients than in controls. The correlation patterns between [¹¹C]ABP688 BP_ND and functional connectivity strength (β) for the superior prefrontal cortex seed were opposite in the depression and control groups. In conclusion, using a novel approach combining [¹¹C]ABP688 PET and rs-fMRI analyses, our study provides a first evidence of lower mGluR5 availability and related functional connectivity alterations in drug-naïve young adults with major depression without comorbidity.

Tracking mood fluctuations with functional network patterns

Subjective mood is a psychophysiological property that depends on complex interactions among the central and peripheral nervous systems. How network interactions in the brain drive temporal fluctuations in mood is unknown. Here we investigate how functional network configuration relates to mood profiles in a single individual over the course of 1 year. Using data from the 'MyConnectome Project', we construct a comprehensive mapping between resting-state functional connectivity (FC) patterns and subjective mood scales using an associative multivariate technique (partial least squares). We report three principal findings. First, FC patterns reliably tracked daily fluctuations in mood. Second, positive mood was marked by an integrated architecture, with prominent interactions between canonical resting-state networks. Finally, one of the top-ranked nodes in mood-related network reconfiguration was the subgenual anterior cingulate cortex, an area commonly associated with mood regulation and dysregulation. Altogether, these results showcase the utility of highly sampled individual-focused data sets for affective neuroscience.

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.

Abnormal resting-state cerebral-limbic functional connectivity in bipolar depression and unipolar depression

BACKGROUND

Distinctive patterns of functional connectivity (FC) abnormalities in neural circuitry has been reported in patients with bipolar depression (BD) and unipolar depression (UD). However, it is unclear that whether this distinct functional connectivity patterns are diagnosis specific between BD and UD. This study aimed to compare patterns of functional connectivity among BD, UD and healthy controls (HC) and determine the distinct functional connectivity patterns which can differentiate BD from UD.

METHOD

Totally 23 BD, 22 UD, and 24 HC were recruited to undergo resting-state fMRI scanning. FC between each pair of brain regions was calculated and compared among the three groups, the associations of FC with depressive symptom were also analyzed.

RESULTS

Both patient groups showed significantly decreased cerebral-limbic FC located between the default mode network [posterior cingulated gyrus (PCG) and precuneus] and limbic regions (hippocampus, amygdala and thalamus) than HC. Moreover, the BD group exhibited more decreased FC mainly in the cortical regions (middle temporal gyrus, PCG, medial superior frontal gyrus, inferior occipital gyrus and superior temporal gyrus), but the UD group is more associated with limbic alterations. These decreased FCs were negatively correlated with HAMD scores in both BD and UD patients.

CONCLUSIONS

BD and UD patients demonstrate different patterns of abnormal cerebral-limbic FC, reflected by decreased FC within cerebral cortex and limbic regions in BD and UD, respectively. The distinct FC abnormal pattern of the cerebral-limbic circuit might be applied as biomarkers to differentiate these two depressive patient groups.

Resting-state functional connectivity and inflexibility of daily emotions in major depression

BACKGROUND

Major Depressive Disorder (MDD) is characterized by aberrant resting-state functional connectivity (FC) in anterior cingulate regions (e.g., subgenual anterior cingulate [sgACC]) and by negative emotional functioning that is inflexible or resistant to change.

METHODS

MDD (N = 33) and control (CTL; N = 31) adults completed a resting-state scan, followed by a smartphone-based Experience Sampling Methodology (ESM) protocol surveying 10 positive and negative emotions 5 times per day for 21 days. We used multilevel modeling to assess moment-to-moment emotional inflexibility (i.e., strong temporal connections between emotions). We examined group differences in whole-brain FC analysis of bilateral sgACC, and then examined associations between emotional experiences and the extracted FC values within each group.

RESULTS

As predicted, MDDs had inflexibility in sadness and avoidance (p < .001, FDR-corrected p < .05), indicating that these emotional experiences persist in depression. MDDs showed weaker FC between the right sgACC and pregenual/dorsal anterior cingulate (pg/dACC) than did CTLs (FWE-corrected, voxelwise p = .01). Importantly, sgACC-pg/dACC FC predicted sadness inflexibility in both MDDs (p = .046) and CTLs (p = .033), suggesting that sgACC FC is associated with day-to-day negative emotions.

LIMITATIONS

Other maladaptive behaviors likely also affect the flexibility of negative emotions. We cannot generalize our finding of a positive relation between sgACC FC and inflexibility of sadness to individuals with more chronic depression or who have recovered from depression.

CONCLUSIONS

Our preliminary findings suggest that connections between portions of the ACC contribute to the persistence of negative emotions and are important in identifying a brain mechanism that may underlie the maintenance of sadness in daily life.

Brain network functional connectivity and cognitive performance in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is one of the most prevalent and debilitating psychiatric disorders. Cognitive complaints are commonly reported in MDD and cognitive impairment is a criterion item for MDD diagnosis. As cognitive processes are increasingly understood as the consequences of distributed interactions between brain regions, a network-based approach may provide novel information about the neurobiological basis of cognitive deficits in MDD.

METHODS

51 Depressed (MDD, n = 23) and non-depressed (control, n = 28) adult participants completed neuropsychological testing and resting-state fMRI (rsfMRI). Cognitive domain scores (processing speed, working memory, episodic memory, and executive function) were calculated. Anatomical regions of interests were entered as seeds for functional connectivity analyses in: default mode (DMN), salience, and executive control (ECN) networks. Partial correlations controlling for age and sex were conducted for cognitive domain scores and functional connectivity in clusters with significant differences between groups.

RESULTS

Significant rsfMRI differences between groups were identified in multiple clusters in the DMN and ECN. Greater positive connectivity within the ECN and between ECN and DMN regions was associated with poorer episodic memory performance in the Non-Depressed group but better performance in the MDD group. Greater connectivity within the DMN was associated with better episodic and working memory performance in the Non-Depressed group but worse performance in the MDD group.

CONCLUSIONS

These results provide evidence that cognitive performance in MDD may be associated with aberrant functional connectivity in cognitive networks and suggest patterns of alternate brain function that may support cognitive processes in MDD.

Altered hippocampal function with preserved cognitive performance in treatment-naive major depressive disorder

The hippocampus is implicated in the pathophysiology of major depressive disorder (MDD), with evidence that morphological changes occur with disease progression. It was hypothesized that treatment-naive patients with depression would show performance deficits in hippocampus-dependent memory trials, with concurrent hippocampal activation deficits on functional magnetic resonance imaging, compared with control participants. Thirteen treatment-naive patients with MDD and 13 control participants completed a hippocampus-dependent memory functional magnetic resonance imaging process-dissociation task. On behavioural measures of habit memory and guessing, there were no significant differences between groups. Functional magnetic resonance imaging analysis indicated that compared with the control group, the MDD group showed increased activation in the parahippocampal gyrus and hippocampus on habit memory and nonitem trials. These alterations in hippocampal functioning with preserved cognitive performance on a test of hippocampus-dependent memory in MDD may be indicative of a compensatory mechanism.

State-Related Alterations of Spontaneous Neural Activity in Current and Remitted Depression Revealed by Resting-State fMRI

Purpose: Although efforts have been made to identify neurobiological characteristic of major depressive disorder (MDD) in recent years, trait- and state-related biological characteristics of MDD still remains unclear. Using functional magnetic resonance imaging (fMRI), the aim of this study was to explore whether altered spontaneous neural activities in MDD are trait- or state- related.

Materials and Methods: Resting-state fMRI data were analyzed for 72 current MDD (cMDD) patients (first-episode, medication-naïve), 49 remitted MDD (rMDD) patients, and 78 age- and sex- matched healthy control (HC) subjects. The values of amplitude of low-frequency fluctuation (ALFF) were compared between groups.

Results: Compared with the cMDD group, the rMDD group had increased ALFF values in the left middle occipital gyrus, left middle temporal gyrus and right cerebellum anterior lobe. Besides, compared with the HC group, the cMDD group had decreased ALFF values in the left middle occipital gyrus. Further analysis explored that the mean ALFF values in the left middle occipital gyrus, left middle temporal gyrus and right cerebellum anterior lobe were correlated positively with BDI scores in rMDD patients.

Conclusion: Abnormal activity in the left middle occipital gyrus, left middle temporal gyrus and right cerebellum anterior lobe may be state-specific in current (first-episode, medication-naïve) and remitted (medication-naïve) depression patients. Furthermore, the state-related compensatory effect was found in these brain areas.

Alterations of amplitude of low-frequency fluctuation in treatment-resistant versus non-treatment-resistant depression patients

PURPOSE

We used parcellation based on 264 putative functional areas to explore the difference of amplitude of low-frequency fluctuation (ALFF) between refractory depression and non-refractory depression patients.

PATIENTS AND METHODS

Sixty first episode drug-naive patients with major depressive disorder (MDD) and 20 healthy controls (HCs) were recruited in this study; the MDD group was divided into a refractory depression (TRD) group (n=15) and a non-refractory depression (non-TRD) group (n=18) according to the treatment effect following up for 2 years. All the subjects underwent magnetic resonance imaging scanning and performed the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and all the patients with MDD finished the 17-item Hamilton Depression Rating Scale (HAMD17). We used a parcellation based on 264 putative functional areas to explore the difference of ALFF measures in the three groups. The correlation between the abnormal ALFF value and characteristics of MDD was examined.

RESULTS

RBANS total scores and index scores in the HCs were significantly different from that of the MDD group. HAMD-17 in the TRD group was significantly higher than that of non-TRD group. Relative to HCs, MDD groups showed significantly lower ALFF within the right default mode network, which was positively correlated with the immediate memory and language in the MDD group. Compared with the non-TRD group, the TRD group showed higher ALFF in the right sensory/somatomotor hand, right auditory and left default mode network.

CONCLUSION

Dysfunction of the somatosensory areas, right auditory and left default mode network may be a marker for specific psychopathology symptoms of TRD.

Anhedonia and depression severity dissociated by dmPFC resting-state functional connectivity in adolescents

INTRODUCTION

Given the heterogeneity within depression, in this study we aim to examine how resting-state functional connectivity (RSFC) in adolescents is related to anhedonia and depression severity on a continuum in line with the research domain criteria (RDoC) approach.

METHODS

We examined how RSFC in the dorsal medial prefrontal cortex (dmPFC), nucleus accumbens (NAcc) and pregenual anterior cingulate cortex (pgACC) was related to anhedonia and depression severity in 86 adolescents (13-21 years).

RESULTS

We found both anhedonia and depression severity related to decreased dmPFC RSFC with the precuneus, a part of the default mode network. However we also found that increased dmPFC connectivity with the ACC/paracingulate gyrus related to anhedonia whereas increased RSFC with the frontal pole related to depression severity.

DISCUSSION

This work extends the view that the dmPFC is a potential therapeutic target for depression in two ways: 1. We report dmPFC connectivity in adolescents; and 2. We show different dmPFC RSFC specific to anhedonia and depression severity, providing neural targets for intervention in young people at risk of depression.

Abnormal early dynamic individual patterns of functional networks in low gamma band for depression recognition

BACKGROUND

The functional networks are associated with emotional processing in depression. The mapping of dynamic spatio-temporal brain networks is used to explore individual performance during early negative emotional processing. However, the dysfunctions of functional networks in low gamma band and their discriminative potentialities during early period of emotional face processing remain to be explored.

METHODS

Functional brain networks were constructed from the MEG recordings of 54 depressed patients and 54 controls in low gamma band (30-48 Hz). Dynamic connectivity regression (DCR) algorithm analyzed the individual change points of time series in response to emotional stimuli and constructed individualized spatio-temporal patterns. The nodal characteristics of patterns were calculated and fed into support vector machine (SVM). Performance of the classification algorithm in low gamma band was validated by dynamic topological characteristics of individual patterns in comparison to alpha and beta band.

RESULTS

The best discrimination accuracy of individual spatio-temporal patterns was 91.01% in low gamma band. Individual temporal patterns had better results compared to group-averaged temporal patterns in all bands. The most important discriminative networks included affective network (AN) and fronto-parietal network (FPN) in low gamma band.

LIMITATIONS

The sample size is relatively small. High gamma band was not considered.

CONCLUSIONS

The abnormal dynamic functional networks in low gamma band during early emotion processing enabled depression recognition. The individual information processing is crucial in the discovery of abnormal spatio-temporal patterns in depression during early negative emotional processing. Individual spatio-temporal patterns may reflect the real dynamic function of subjects while group-averaged data may neglect some individual information.

Risk factors associated with cognitions for late-onset depression based on anterior and posterior default mode sub-networks

BACKGROUND

Abnormal functional connectivity (FC) in the default mode network (DMN) plays an important role in late-onset depression (LOD) patients. In this study, the risk predictors of LOD based on anterior and posterior DMN are explored.

METHODS

A total of 27 LOD patients and 40 healthy controls (HC) underwent resting-state functional magnetic resonance imaging and cognitive assessments. Firstly, FCs within DMN sub-networks were determined by placing seeds in the ventral medial prefrontal cortex (vmPFC) and posterior cingulate cortex (PCC). Secondly, multivariable logistic regression was used to identify risk factors for LOD patients. Finally, correlation analysis was performed to investigate the relationship between risk factors and the cognitive value.

RESULTS

Multivariable logistic regression showed that the FCs between the vmPFC and right middle temporal gyrus (MTG) (vmPFC-MTG_R), FCs between the vmPFC and left precuneus (PCu), and FCs between the PCC and left PCu (PCC-PCu_L) were the risk factors for LOD. Furthermore, FCs of the vmPFC-MTG_R and PCC-PCu_L correlated with processing speed (R = 0.35, P = 0.002; R = 0.32, P = 0.009), and FCs of the vmPFC-MTG_R correlated with semantic memory (R = 0.41, P = 0.001).

LIMITATIONS

The study was a cross-sectional study. The results may be potentially biased because of a small sample.

CONCLUSIONS

In this study, we confirmed that LOD patients mainly present cognitive deficits in processing speed and semantic memory. Moreover, our findings further suggested that FCs within DMN sub-networks associated with cognitions were risk factors, which may be used for the prediction of LOD.

Voxel-wise brain-wide functional connectivity abnormalities in first-episode, drug-naive patients with major depressive disorder

Due to different foci and single sample across studies, abnormal functional connectivity (FC) has been implicated in the pathophysiology of major depressive disorder (MDD) with inconsistent results. The inconsistency may reflect a combination of clinical and methodological variability, which leads to limited reproducibility of these findings. The samples included 59 patients with MDD and 31 controls from Sample 1, 29 patients with MDD and 24 controls from Sample 2, and 31 patients with schizophrenia and 37 controls from Sample 3. Global-brain FC (GFC) and an overlapping technique were applied to analyze the imaging data. Compared with healthy controls, patients with MDD in Samples 1 and 2 showed increased GFC in the overlapped brain areas, including the bilateral insula, right inferior parietal lobule (IPL), and right supramarginal gyrus/IPL. By contrast, decreased GFC in the overlapped brain areas, including the bilateral posterior cingulate cortex/presuneus and left calcarine cortex, was found in patients with MDD. In addition, patients with schizophrenia in Sample 3 did not show any GFC abnormalities in the overlapped areas from the results of Samples 1 and 2. The present study is the first to examine voxel-wise brain-wide FC in MDD with two independent samples by using an overlapping technique. The results indicate that aberrant FC patterns of insula-centered sensorimotor circuit may account for the pathophysiology of MDD.

Lateral habenula perturbation reduces default-mode network connectivity in a rat model of depression

Hyperconnectivity of the default-mode network (DMN) is one of the most widely replicated neuroimaging findings in major depressive disorder (MDD). Further, there is growing evidence for a central role of the lateral habenula (LHb) in the pathophysiology of MDD. There is preliminary neuroimaging evidence linking LHb and the DMN, but no causal relationship has been shown to date. We combined optogenetics and functional magnetic resonance imaging (fMRI), to establish a causal relationship, using an animal model of treatment-resistant depression, namely Negative Cognitive State rats. First, an inhibitory light-sensitive ion channel was introduced into the LHb by viral transduction. Subsequently, laser stimulation was performed during fMRI acquisition on a 9.4 Tesla animal scanner. Neural activity and connectivity were assessed, before, during and after laser stimulation. We observed a connectivity decrease in the DMN following laser-induced LHb perturbation. Our data indicate a causal link between LHb downregulation and reduction in DMN connectivity. These findings may advance our mechanistic understanding of LHb inhibition, which had previously been identified as a promising therapeutic principle, especially for treatment-resistant depression.

Functional brain abnormalities in major depressive disorder using the Hilbert-Huang transform

Major depressive disorder is a common disease worldwide, which is characterized by significant and persistent depression. Non-invasive accessory diagnosis of depression can be performed by resting-state functional magnetic resonance imaging (rs-fMRI). However, the fMRI signal may not satisfy linearity and stationarity. The Hilbert-Huang transform (HHT) is an adaptive time-frequency localization analysis method suitable for nonlinear and non-stationary signals. The objective of this study was to apply the HHT to rs-fMRI to find the abnormal brain areas of patients with depression. A total of 35 patients with depression and 37 healthy controls were subjected to rs-fMRI. The HHT was performed to extract the Hilbert-weighted mean frequency of the rs-fMRI signals, and multivariate receiver operating characteristic analysis was applied to find the abnormal brain regions with high sensitivity and specificity. We observed differences in Hilbert-weighted mean frequency between the patients and healthy controls mainly in the right hippocampus, right parahippocampal gyrus, left amygdala, and left and right caudate nucleus. Subsequently, the above-mentioned regions were included in the results obtained from the compared region homogeneity and the fractional amplitude of low frequency fluctuation method. We found brain regions with differences in the Hilbert-weighted mean frequency, and examined their sensitivity and specificity, which suggested a potential neuroimaging biomarker to distinguish between patients with depression and healthy controls. We further clarified the pathophysiological abnormality of these regions for the population with major depressive disorder.