Distinctive pretreatment features of bilateral nucleus accumbens networks predict early response to antidepressants in major depressive disorder

Increased brain nucleus accumbens functional connectivity in melancholic depression

BACKGROUND

Melancholic depression, marked by typical symptoms of anhedonia, is regarded as a homogeneous subtype of major depressive disorder (MDD). However, little attention was paid to underlying mechanisms of melancholic depression. This study aims to examine functional connectivity of the reward circuit associated with anhedonia symptoms in melancholic depression.

METHODS

Fifty-nine patients with first-episode drug- naive MDD, including 31 melancholic patients and 28 non-melancholic patients, were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Thirty-two healthy volunteers were recruited as controls. Bilateral nucleus accumbens (NAc) were selected as seed points to form functional NAc network. Then support vector machine (SVM) was used to distinguish melancholic patients from non-melancholic patients.

RESULTS

Relative to non-melancholic patients, melancholic patients displayed increased functional connectivity (FC) between bilateral NAc and right middle frontal gyrus (MFG) and between right NAc and left cerebellum lobule VIII. Compared to healthy controls, melancholic patients showed increased FC between right NAc and right lingual gyrus and between left NAc and left postcentral gyrus; non-melancholic patients had increased FC between bilateral NAc and right lingual gyrus. No significant correlations were observed between altered FC and clinical variables in melancholic patients. SVM results showed that FC between left NAc and right MFG could accurately distinguish melancholic patients from non-melancholic patients.

CONCLUSION

Melancholic depression exhibited different patterns of functional connectivity of the reward circuit relative to non-melancholic patients. This study highlights the significance of the reward circuit in the neuropathology of melancholic depression.

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).

Differences in gray matter volumes of subcortical nuclei between major depressive disorder with and without persistent depressive disorder

OBJECTIVE

This study aimed to compare the differences in gray matter volumes (GMVs) of subcortical nuclei between major depressive disorder (MDD) patients with and without persistent depressive disorder (PDD) at long-term follow-up.

METHODS

114 and 94 subjects with MDD, including 48 and 41 with comorbid PDD, were enrolled to undergo high-resolution T1-weighted imaging at first (FIP) and second (three years later, SIP) investigation points, respectively. FreeSurfer was used to extract the GMVs of seven subcortical nuclei, and Generalized Estimating Equation models were employed to estimate the differences in GMVs of subcortical nuclei between the two subgroups.

RESULTS

The PDD subgroup had a significantly greater depressive severity and a higher percentage of patients undergoing pharmacotherapy at the FIP as compared with the non-PDD subgroup. These differences became insignificant at the SIP. The PDD subgroup had a significantly (p < 0.003) smaller GMV in the right putamen at the SIP and in the right nucleus accumbens (NAc) at the FIP and SIP as compared with the non-PDD subgroup. After controlling for clinical variables, PDD was independently associated with smaller GMVs in the right putamen and NAc.

LIMITATIONS

Imaging was not performed at baseline and pharmacotherapy was not controlled at the FIP and SIP.

CONCLUSIONS

MDD with PDD was associated with smaller GMVs in the right putamen and NAc as compared with MDD without PDD. Whether the two regions are biomarkers related to a poor prognosis and the chronicity of depression requires further study.

Classification of primary dysmenorrhea by brain effective connectivity of the amygdala: a machine learning study

BACKGROUND

The amygdala plays a crucial role in the central pathogenesis mechanism of primary dysmenorrhea (PDM). However, the detailed pain modulation principles of the amygdala in PDM remain unclear. Here, we applied the Granger causality analysis (GCA) to investigate the directional effective connectivity (EC) alterations in the amygdala network of PDM patients.

METHODS

Thirty-seven patients with PDM and 38 healthy controls were enrolled in this study and underwent resting-state functional magnetic resonance imaging scans during the pain-free stage. GCA was employed to explore the amygdala-based EC network alteration in PDM. A multivariate pattern analysis (MVPA)-based machine learning approach was used to explore whether the altered amygdala EC could serve as an fMRI-based marker for classifying PDM and HC participants.

RESULTS

Compared to the healthy control group, patients with PDM showed significantly decreased EC from the amygdala to the right superior frontal gyrus (SFG), right superior parietal lobe/middle occipital gyrus, and left middle cingulate cortex, whereas increased EC was found from the amygdala to the bilateral medial orbitofrontal cortex. In addition, increased EC was found from the bilateral SFG to the amygdala, and decreased EC was found from the medial orbitofrontal cortex, caudate nucleus to the amygdala. The increased EC from the right SFG to the amygdala was associated with a plasma prostaglandin E2 level in PDM. The MVPA based on an altered amygdala EC pattern yielded a total accuracy of 86.84% for classifying the patients with PDM and HC.

CONCLUSION

Our study is the first to combine MVPA and EC to explore brain function alteration in PDM. The results could advance understanding of the neural theory of PDM in specifying the pain-free period.

Baseline patterns of resting functional connectivity within posterior default-mode intranetwork associated with remission to antidepressants in major depressive disorder

BACKGROUND

The default mode network (DMN) is implicated in the pathophysiology of major depressive disorder (MDD), and functional connectivity (FC) involved in DMN is suggested to be associated with antidepressant remission. The goal of this study is to recognize relationships between FC within DMN and early amelioration in MDD patients and to further test the capacity of FC to predict early efficacy.

METHODS

In total 66 MDD patients and 57 healthy controls were recruited for resting-state functional magnetic resonance imaging scans at baseline. After four weeks of treatment with Escitalopram or Venlafaxine, patients were divided into subgroups with remitters (R, n = 31) and non-remitters (NR, n = 35). Independent component analysis (ICA) was used to compare intranetwork functional connectivity (intra-FC) in DMN between the three groups.

RESULTS

Relative to NR-MDD group and HCs, the R-MDD group showed significantly higher intra-FC in the right angular gyrus of DMN, and the intra-FC was positively correlated with the reduction ratio of the depressive symptom scores. The ROC curve analysis revealed that intra-FC exhibited a high diagnostic value for remission.

CONCLUSION

These findings indicated that intra-FC related to the DMN is a prognostic marker that can potentially predict early remission of symptoms after antidepressant treatment.

Abnormal Dynamic Functional Connectivity of the Left Rostral Hippocampus in Predicting Antidepressant Efficacy in Major Depressive Disorder

OBJECTIVE

Some pharmacological treatments are ineffective in parts of patients with major depressive disorder (MDD), hence this needs prediction of effective treatment responses. The study aims to examine the relationship between dynamic functional connectivity (dFC) of the hippocampal subregion and antidepressant improvement of MDD patients and to estimate the capability of dFC to predict antidepressant efficacy.

METHODS

The data were from 70 MDD patients and 43 healthy controls (HC); the dFC of hippocampal subregions was estimated by sliding-window approach based on resting-state functional magnetic resonance imaging (R-fMRI). After 3 months treatment, 36 patients underwent second R-fMRI scan and were then divided into the response group and non-response group according to clinical responses.

RESULTS

The result manifested that MDD patients exhibited lower mean dFC of the left rostral hippocampus (rHipp.l) compared with HC. After 3 months therapy, the response group showed lower dFC of rHipp.l compared with the non-response group. The dFC of rHipp.l was also negatively correlated with the reduction rate of Hamilton Depression Rating Scale.

CONCLUSION

These findings highlighted the importance of rHipp in MDD from the dFC perspective. Detection and estimation of these changes might demonstrate helpful for comprehending the pathophysiological mechanism and for assessment of treatment reaction of MDD.

Reduced nucleus accumbens functional connectivity in reward network and default mode network in patients with recurrent major depressive disorder

The nucleus accumbens (NAc) is considered a hub of reward processing and a growing body of evidence has suggested its crucial role in the pathophysiology of major depressive disorder (MDD). However, inconsistent results have been reported by studies on reward network-focused resting-state functional MRI (rs-fMRI). In this study, we examined functional alterations of the NAc-based reward circuits in patients with MDD via meta- and mega-analysis. First, we performed a coordinated-based meta-analysis with a new SDM-PSI method for all up-to-date rs-fMRI studies that focused on the reward circuits of patients with MDD. Then, we tested the meta-analysis results in the REST-meta-MDD database which provided anonymous rs-fMRI data from 186 recurrent MDDs and 465 healthy controls. Decreased functional connectivity (FC) within the reward system in patients with recurrent MDD was the most robust finding in this study. We also found disrupted NAc FCs in the DMN in patients with recurrent MDD compared with healthy controls. Specifically, the combination of disrupted NAc FCs within the reward network could discriminate patients with recurrent MDD from healthy controls with an optimal accuracy of 74.7%. This study confirmed the critical role of decreased FC in the reward network in the neuropathology of MDD. Disrupted inter-network connectivity between the reward network and DMN may also have contributed to the neural mechanisms of MDD. These abnormalities have potential to serve as brain-based biomarkers for individual diagnosis to differentiate patients with recurrent MDD from healthy controls.

Functional connectivity of the hippocampus in predicting early antidepressant efficacy in patients with major depressive disorder

BAKGROUD

The hippocampus is involved in the pathophysiology of major depressive disorder (MDD), and its structure and function have been reported to be related to the antidepressant response. This study aimed to identify relationships between hippocampal functional connectivity (FC) and early improvement in patients with MDD and to further explore the ability of hippocampal FC to predict early efficacy.

METHODS

Thirty-six patients with nonpsychotic MDD were recruited and underwent resting-state functional magnetic resonance imaging scans at baseline. After two weeks of treatment with escitalopram, patients were divided into subgroups with early improved depression (EID, n= 19) and nonimproved depression (NID, n=17) . A voxelwise FC analysis was performed with the bilateral hippocampus as seeds, two-sample t-tests were used to compare hippocampal FC between groups. Receiver operating characteristic (ROC) curves were constructed to determine the best FC measures and optimal threshold for differentiating EID from END.

RESULTS

The EID group showed significantly higher FC between the left hippocampus and left inferior frontal gyrus and precuneus than the END group. And the left hippocampal FC of these two regions were positively correlated with the reduction ratio of the depressive symptom scores. The ROC curve analysis revealed that summed FC scores for these two regions exhibited the highest area under the curve, with a sensitivity of 0.947 and specificity of 0.882 at a summed score of 0.14.

LIMITATIONS

The sample used in this study was relatively small.

CONCLUSIONS

These findings demonstrated that FC of the left hippocampus can predict early efficacy of antidepressant.

Major Depressive Disorder: Advances in Neuroscience Research and Translational Applications

Major depressive disorder (MDD), also referred to as depression, is one of the most common psychiatric disorders with a high economic burden. The etiology of depression is still not clear, but it is generally believed that MDD is a multifactorial disease caused by the interaction of social, psychological, and biological aspects. Therefore, there is no exact pathological theory that can independently explain its pathogenesis, involving genetics, neurobiology, and neuroimaging. At present, there are many treatment measures for patients with depression, including drug therapy, psychotherapy, and neuromodulation technology. In recent years, great progress has been made in the development of new antidepressants, some of which have been applied in the clinic. This article mainly reviews the research progress, pathogenesis, and treatment of MDD.

Identification of first-episode unmedicated major depressive disorder using pretreatment features of dominant coactivation patterns

Identifying neuroimaging features to diagnose major depressive disorder (MDD) and predict treatment response remains challenging. Using the pretreatment dominant coactivation pattern (dCAP) analysis approach, we aimed to identify patients with MDD and predict antidepressant efficacy. Seventy-seven first-episode unmedicated MDD patients and forty-two age- and sex-matched healthy controls (HCs) were recruited in the study. The dCAP analysis was performed for the reward and default mode network (DMN) to identify the MDD patients from the HCs. The dCAP1 of the left posterior DMN and bilateral anterior DMN were significantly higher in the MDD group than in the HC group (P < .001), and the dCAP1 in the left posterior DMN was positively correlated with the baseline severity of depression (rho = 0.248, P = .030). Besides, the MDD group exhibited significantly higher dCAP1 in the right reward network than the HC group. Further correlation analyses revealed that the transfer probability in the right reward network was positively correlated with the treatment responsivity (r = 0.247, P = .030). Importantly, integrating the dCAPs of the above four subnetworks can effectively identify the patients with MDD (AUC = 0.920, P < .001). The distinct pretreatment features of the dCAP in the subnetwork of the DMN and reward network may serve as potential indicators for individual diagnosis and prediction of antidepressant response in the early stage.

A Treatment-Response Comparison Study of Resting-State Functional Magnetic Resonance Imaging Between Standard Treatment of SSRI and Standard Treatment of SSRI Plus Non-dominant Hand-Writing Task in Patients With Major Depressive Disorder

Background: Researches have recently shifted from functional/structural imaging studies to functional connectivity (FC) studies in major depressive disorder (MDD). We aimed to compare treatment response of two treatment groups before and after treatment, in terms of both with psychiatric evaluation scales and resting-state functional connectivity (RSFC) changes in order to objectively demonstrate the possible contribution of the non-dominant hand-writing exercise (NHE) effect on depression treatment. Methods: A total of 26 patients who were right-handed women with similar sociodemographic characteristics were enrolled. Their pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychiatric tests were recorded, and then, patients were divided into two groups randomly. A standard treatment (ST) (fix sertraline 50 mg/day) was given to both groups. One randomly selected group was given the NHE in addition to the ST. After 8 weeks of treatment, all patients were reevaluated with rs-fMRI and neuropsychiatric tests. Pre- and post-treatment FC changes within the groups and post-treatment connectivity changes between groups were evaluated. Results: Post-treatment neuropsychiatric tests were significantly different in both groups. Post-treatment, two brain regions' connectivity changed in the ST group, whereas 10 brain regions' connectivity changed significantly in the ST + NHE group. When treatment groups were compared with each other after the treatment, the FC of 13 regions changed in the ST + NHE group compared to the ST group (p-unc/p-PFD <0.05). The density of connectivity changes in the frontal and limbic regions, especially connectivities shown to change in depression treatment, in the ST + NHE group indicates a positive contribution to depression treatment, which is also supported by neuropsychiatric scale changes. Conclusion: NHE, which we developed with inspiration from the Eye Movement Desensitization and Reprocessing (EMDR) method, showed significantly more connecitivity changes related with MDD treatment. Beyond offering a new additional treatment method, our study will also contribute to the current literature with our efforts to evaluate all brain regions and networks that may be related to MDD and its treatment together, without being limited to a few regions. Trial Registration: The rs-fMRI and treatment registers were recorded in the BizMed system, which is the patient registration system of Bezmialem Vakif University Medicine Faculty, under the BAP support project approval code and the registration number 3.2018/8.

Distinct Features of Cerebral Blood Flow and Spontaneous Neural Activity as Integrated Predictors of Early Response to Antidepressants

AIMS

The purpose of this study is to explore whether pre-treatment features of brain function can discriminate non-responders to antidepressant medication in the early phase.

METHODS

Forty-four treatment-responsive depressed (RD) patients, 36 non-responsive depressed (NRD) patients, and 42 healthy controls (HCs) were recruited. Regional cerebral blood flow (CBF) and amplitude of low-frequency fluctuation (ALFF) values were calculated for all subjects. Correlation analyses were used to explore the relationship between symptom improvement and CBF/ALFF. Receiver operating characteristics (ROC) and the 10-fold cross-validation support vector machine (SVM) classifier were applied for the discrimination of treatment response.

RESULTS

Compared with the HCs, the RD and NRD groups exhibited lower CBF and ALFF in the right posterior lobe of the cerebellum. Compared with the NRD group, the RD group showed distinct CBF patterns in the left frontal striatal regions and right frontal cerebellar regions, as well as distinct ALFF features in the left frontoparietal striatum and right frontotemporal striatal cerebellar regions. The ROC and SVM classifier revealed the optimal power to distinguish the RD and NRD groups based on the combined measures (i.e., CBF and ALFF).

CONCLUSION

Distinct features of CBF and ALFF in the frontal striatal network may serve as promising neuroimaging predictors for identifying patients with blunted responsiveness, which may facilitate personalized antidepressant treatment.

Desynchronized Functional Activities Between Brain White and Gray Matter in Major Depression Disorder

BACKGROUND

Alterations in gray matter (GM) have been recognized as playing an important role in the neurobiological mechanism underlying major depressive disorder (MDD) and antidepressant responses. However, little is known about white matter (WM) connectivity in MDD, leaving an incomplete understanding of the pathophysiology of the disorder.

PURPOSE

To examine the functional connectivity (FC) of WM, GM, and WM-GM in MDD patients and explore the relationship between FC and antidepressant response.

STUDY TYPE

Longitudinal study.

SUBJECTS

In all, 129 MDD patients and 89 healthy controls (HC).

FIELD STRENGTH/SEQUENCE

Whole-brain blood oxygen level-dependent (BOLD) single-shot echo planar imaging was acquired at 3.0T.

ASSESSMENT

At baseline, all participants received Hamilton depression rating scale (HAMD) assessment and an fMRI scan. After 2- and 8-week antidepressant treatment, patients completed the HAMD again. The HAMD reductive rate of 2- and 8-weeks were calculated.

STATISTICAL TESTS

The comparisons of age, education, HAMD scores, and FC values (false discovery rate correction) between patients and controls were calculated with a two-sample t-test. The chi-square test was employed to compare the differences of gender between these two groups. Correlations between FC and HAMD, as well as the reductive rate of HAMD, were analyzed with Pearson or Spearman correlation. Receiver operator curve analysis was performed to predict the antidepressant response.

RESULTS

Compared to HC, MDD patients exhibited widespread decreases in FC of WM-GM. Furthermore, 28 GM regions and 11 WM bundles had lower connectivity in MDD patients. At baseline, four FC of WM-GM showed negative correlations with the HAMD scores. Six FC of WM-GM correlated with the 2-week reductive rate of HAMD. Moreover, FC in GM, WM, and WM-GM also exhibited significantly positive correlations with an 8-week reductive rate of HAMD.

DATA CONCLUSION

The FC of WM-GM was decreased in MDD and may play a role in its pathophysiology and antidepressant responses.

LEVEL OF EVIDENCE

2.

TECHNICAL EFFICACY STAGE

2.

Research Review: Brain network connectivity and the heterogeneity of depression in adolescence - a precision mental health perspective

BACKGROUND

Adolescence is a period of high risk for the onset of depression, characterized by variability in symptoms, severity, and course. During adolescence, the neurocircuitry implicated in depression continues to mature, suggesting that it is an important period for intervention. Reflecting the recent emergence of 'precision mental health' - a person-centered approach to identifying, preventing, and treating psychopathology - researchers have begun to document associations between heterogeneity in features of depression and individual differences in brain circuitry, most frequently in resting-state functional connectivity (RSFC).

METHODS

In this review, we present emerging work examining pre- and post-treatment measures of network connectivity in depressed adolescents; these studies reveal potential intervention-specific neural markers of treatment efficacy. We also review findings from studies examining associations between network connectivity and both types of depressive symptoms and response to treatment in adults, and indicate how this work can be extended to depressed adolescents. Finally, we offer recommendations for research that we believe will advance the science of precision mental health of adolescence.

RESULTS

Nascent studies suggest that linking RSFC-based pathophysiological variation with effects of different types of treatment and changes in mood following specific interventions will strengthen predictions of prognosis and treatment response. Studies with larger sample sizes and direct comparisons of treatments are required to determine whether RSFC patterns are reliable neuromarkers of treatment response for depressed adolescents. Although we are not yet at the point of using RSFC to guide clinical decision-making, findings from research examining the stability and reliability of RSFC point to a favorable future for network-based clinical phenotyping.

CONCLUSIONS

Delineating the correspondence between specific clinical characteristics of depression (e.g., symptoms, severity, and treatment response) and patterns of network-based connectivity will facilitate the development of more tailored and effective approaches to the assessment, prevention, and treatment of depression in adolescents.

Predicting Antidepressant Citalopram Treatment Response via Changes in Brain Functional Connectivity After Acute Intravenous Challenge

Introduction: The early and therapy-specific prediction of treatment success in major depressive disorder is of paramount importance due to high lifetime prevalence, and heterogeneity of response to standard medication and symptom expression. Hence, this study assessed the predictability of long-term antidepressant effects of escitalopram based on the short-term influence of citalopram on functional connectivity. Methods: Twenty nine subjects suffering from major depression were scanned twice with resting-state functional magnetic resonance imaging under the influence of intravenous citalopram and placebo in a randomized, double-blinded cross-over fashion. Symptom factors were identified for the Hamilton depression rating scale (HAM-D) and Beck's depression inventory (BDI) taken before and after a median of seven weeks of escitalopram therapy. Predictors were calculated from whole-brain functional connectivity, fed into robust regression models, and cross-validated. Results: Significant predictive power could be demonstrated for one HAM-D factor describing insomnia and the total score (r = 0.45-0.55). Remission and response could furthermore be predicted with an area under the receiver operating characteristic curve of 0.73 and 0.68, respectively. Functional regions with high influence on the predictor were located especially in the ventral attention, fronto-parietal, and default mode networks. Conclusion: It was shown that medication-specific antidepressant symptom improvements can be predicted using functional connectivity measured during acute pharmacological challenge as an easily assessable imaging marker. The regions with high influence have previously been related to major depression as well as the response to selective serotonin reuptake inhibitors, corroborating the advantages of the current approach of focusing on treatment-specific symptom improvements.

Limbic Intrinsic Connectivity in Depressed and High-Risk Youth

OBJECTIVE

Depression runs in families and has been associated with dysfunctional limbic connectivity. Whether aberrant limbic connectivity is a risk factor for or a consequence of depression is unclear. To examine this question, we compared resting state functional connectivity (RSFC) in youth with depressive disorders (DEP), healthy offspring of parents with depression (DEP-risk), and healthy comparison (HC) youth.

METHOD

Magnetic resonance imaging at rest was acquired from 119 youth, aged 8 to 17 years (DEP, n = 41, DEP-risk, n = 39, and HC, n = 39) and analyzed using seed-based RSFC in bilateral amygdala and nucleus accumbens (NAcc), covarying for age, IQ, and sex.

RESULTS

We found distinct risk- and disorder-specific patterns of RSFC across groups. DEP-risk and DEP youth shared reduced negative amygdala-right frontal cortex RSFC and reduced positive amygdala-lingual gyrus RSFC compared to HC youth (p < .001). DEP-risk youth had weaker negative amygdala-precuneus RSFC compared to DEP and HC youth (p < .001), suggesting a resilience marker for depression. In contrast, DEP youth had increased positive NAcc-left frontal cortex RSFC and reduced positive NAcc-insula RSFC compared to DEP-risk and HC youth (p < .001), suggestive of disorder-specific features of depression. Greater depression severity was correlated with disorder-specific amygdala and NAcc RSFC (p < .05).

CONCLUSION

RSFC in the amygdala and NAcc may represent selective disorder- and risk-specific markers in youth with, and at familial risk for, depression. Longitudinal studies are needed to determine whether these patterns predict long-term clinical outcomes.