Antidepressants normalize brain flexibility associated with multi-dimensional symptoms in major depressive patients

Affective dysfunction mediates the link between neuroimmune markers and the default mode network functional connectivity, and the somatic symptoms in somatic symptom disorder

OBJECTIVE

Somatic symptom disorder (SSD) is characterized by physical symptoms and associated functional impairments that are often comorbid with depression and anxiety disorders. In this study, we explored relationships between affective symptoms and the functional connectivity of the default mode network (DMN) in SSD patients, as well as the impact of peripheral inflammation. We employed mediation analyses to investigate the potential pathways between these factors.

METHODS

We recruited a total of 119 individuals (74 unmedicated SSD patients and 45 healthy controls), who were subjected to comprehensive psychiatric and clinical evaluations, blood tests, and resting-state functional magnetic resonance imaging scanning. We assessed neuroimmune markers (interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), tumor necrosis factor-α (TNF-α), tryptophan, serotonin, and 5-hydroxyindoleacetic acid (5-HIAA)), clinical indicators of somatic symptoms, depression, anxiety, anger, alexithymia, and functional connectivity (FC) within the DMN regions. Data were analyzed using correlation and mediation analysis, with a focus on exploring potential relations between clinical symptoms, blood indices, and DMN FCs.

RESULTS

Patients with SSD had higher clinical scores as well as IL-6 and TNF-α levels compared with those in the control group (P < 0.05). The SSD group exhibited lower FC strength between the left inferior parietal lobule and left prefrontal cortex (Pfalse discovery rate (FDR) < 0.05). Exploratory correlation analysis revealed that somatic symptom scores were positively correlated with affective symptom scores, negatively correlated with the FC strength between the intra prefrontal cortex regions, and correlated with levels of IL-6, TNF- α, and tryptophan (uncorrected P < 0.01). Mediation analysis showed that levels of anxiety and trait anger significantly mediated the relations between DMN FC strength and somatic symptoms. In addition, the DMN FC mediated the level of trait anger with respect to somatic symptoms (all PFDR < 0.05). The levels of depression and trait anger exhibited significant mediating effects as suppressors of the relations between the level of 5-HIAA and somatic symptom score (all PFDR < 0.05). Further, the level of 5-HIAA had a mediating effect as a suppressor on the relation between DMN FC and state anger. Meanwhile, the levels of hs-CRP and IL-6 had full mediating effects as suppressors when explaining the relations of DMN FC strengths with the level of depression (all PFDR < 0.05). The patterns of valid mediation pathways were different in the control group.

CONCLUSIONS

Affective symptoms may indirectly mediate the associations between DMN connectivity, somatic symptoms, and neuroimmune markers. Inflammatory markers may also mediate the impact of DMN connectivity on affective symptoms. These results emphasize the importance of affective dysregulation in understanding the mechanisms of SSD and have potential implications for the development of tailored therapeutic approaches for SSD patients with affective symptoms. Furthermore, in SSD research using DMN FC or neuroimmune markers, considering and incorporating such mediating effects of affective symptoms suggests the possibility of more accurate prediction and explanation.

Dynamic reconfigurations of brain networks in depressive and anxiety disorders: The influence of antidepressants

Major Depressive Disorder (MDD) and anxiety disorders are highly comorbid recurrent psychiatric disorders. Reduced dynamic reconfiguration of brain regions across subnetworks may play a critical role underlying these deficits, with indications of normalization after treatment with antidepressants. This study investigated dynamic reconfigurations in controls and individuals with a current MDD and/or anxiety disorder including antidepressant users and non-users in a large sample (N = 207) of adults. We quantified the number of subnetworks a region switched to (promiscuity) as well as the total number of switches (flexibility). Average whole-brain (i.e., global) values and subnetwork-specific values were compared between diagnosis and antidepressant groups. No differences in reconfiguration dynamics were found between individuals with a current MDD (N = 49), anxiety disorder (N = 46), comorbid MDD and anxiety disorder (N = 55), or controls (N = 57). Global and sensorimotor network (SMN) promiscuity and flexibility were higher in antidepressant users (N = 49, regardless of diagnosis) compared to non-users (N = 101) and controls. Dynamic reconfigurations were considerably higher in antidepressant users relative to non-users and controls, but not significantly altered in individuals with a MDD and/or anxiety disorder. The increase in antidepressant users was apparent across the whole brain and in the SMN when investigating subnetworks. These findings help disentangle how antidepressants improve symptoms.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features

BACKGROUND

Previous studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers.

PURPOSE

To evaluate machine-learning models combined with multisequence MRI neuroimaging features to diagnose patients with MDD.

STUDY TYPE

Prospective.

SUBJECTS

A training cohort including 111 patients and 90 healthy controls (HCs) and a test cohort including 28 patients and 22 HCs.

FIELD STRENGTH/SEQUENCE

A 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and single-shot echo-planar diffusion tensor imaging.

ASSESSMENT

Recruitment and integration were used to reflect the dynamic changes of functional networks, while gray matter volume and fractional anisotropy were used to reflect the changes in the morphological and anatomical network. We then fused features with significant differences in functional, morphological, and anatomical networks to evaluate a random forest (RF) classifier to diagnose patients with MDD. Furthermore, a support vector machine (SVM) classifier was used to verify the stability of neuroimaging features. Linear regression analyses were conducted to investigate the relationships among multisequence neuroimaging features and the suicide risk of patients.

STATISTICAL TESTS

The comparison of functional network attributes between patients and controls by two-sample t-test. Network-based statistical analysis was used to identify structural and anatomical connectivity changes between MDD and HCs. The performance of the model was evaluated by receiver operating characteristic (ROC) curves.

RESULTS

The performance of the RF model integrating multisequence neuroimaging features in the diagnosis of depression was significantly improved, with an AUC of 93.6%. In addition, we found that multisequence neuroimaging features could accurately predict suicide risk in patients with MDD (r = 0.691).

DATA CONCLUSION

The RF model fusing functional, morphological, and anatomical network features performed well in diagnosing patients with MDD and provided important insights into the pathological mechanisms of MDD.

EVIDENCE LEVEL

1.

TECHNICAL EFFICACY

Stage 2.

Functional connectomics in depression: insights into therapies

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

Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives

Based on functional magnetic resonance imaging and multilayer dynamic network model, the brain network’s quantified temporal stability has shown potential in predicting altered brain functions. This manuscript aims to summarize current knowledge, clinical research progress, and future perspectives on brain network’s temporal stability. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date which measure is the best. The temporal stability of brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, circadian rhythm, and data preprocessing/analyzing strategies, which should be considered in clinical studies. Multiple common psychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disorder-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric disorders partly because of the divergent results. Further studies with larger samples and in transdiagnostic (including schizoaffective disorder) subjects are warranted.

Transcutaneous Electrical Cranial-Auricular Acupoint Stimulation Modulating the Brain Functional Connectivity of Mild-to-Moderate Major Depressive Disorder: An fMRI Study Based on Independent Component Analysis

Evidence has shown the roles of taVNS and TECS in improving depression but few studies have explored their synergistic effects on MDD. Therefore, the treatment responsivity and neurological effects of TECAS were investigated and compared to escitalopram, a commonly used medication for depression. Fifty patients with mild-to-moderate MDD (29 in the TECAS group and 21 in another) and 49 demographically matched healthy controls were recruited. After an eight-week treatment, the outcomes of TECAS and escitalopram were evaluated by the effective rate and reduction rate based on the Montgomery-Asberg Depression Rating Scale, Hamilton Depression Rating Scale, and Hamilton Anxiety Rating Scale. Altered brain networks were analyzed between pre- and post-treatment using independent component analysis. There was no significant difference in clinical scales between TECAS and escitalopram but these were significantly decreased after each treatment. Both treatments modulated connectivity of the default mode network (DMN), dorsal attention network (DAN), right frontoparietal network (RFPN), and primary visual network (PVN), and the decreased PVN-RFPN connectivity might be the common brain mechanism. However, there was increased DMN-RFPN and DMN-DAN connectivity after TECAS, while it decreased in escitalopram. In conclusion, TECAS could relieve symptoms of depression similarly to escitalopram but induces different changes in brain networks.

Sustained attention alterations in major depressive disorder: A review of fMRI studies employing Go/No-Go and CPT tasks

BACKGROUND

Major depressive disorder (MDD) is a severe psychiatric condition characterized by selective cognitive dysfunctions. In this regard, functional Magnetic Resonance Imaging (fMRI) studies showed, both at resting state and during tasks, alterations in the brain functional networks involved in cognitive processes in MDD patients compared to controls. Among those, it seems that the attention network may have a role in the disease pathophysiology. Therefore, in this review we aim at summarizing the current fMRI evidence investigating sustained attention in MDD patients.

METHODS

We conducted a search on PubMed on case-control studies on MDD employing fMRI acquisitions during Go/No-Go and continuous performance tasks. A total of 12 studies have been included in the review.

RESULTS

Overall, the majority of fMRI studies reported quantitative alterations in the response to attentive tasks in selective brain regions, including the prefrontal cortex, the cingulate cortex, the temporal and parietal lobes, the insula and the precuneus, which are key nodes of the attention, the executive, and the default mode networks.

LIMITATIONS

The heterogeneity in the study designs, fMRI acquisition techniques and processing methods have limited the generalizability of the results.

CONCLUSIONS

The results from the included studies showed the presence of alterations in the activation patterns of regions involved in sustained attention in MDD, which are in line with current evidence and seemed to explain some of the key symptoms of depression. However, given the paucity and heterogeneity of studies available, it may be worthwhile to continue investigating the attentional domain in MDD with ad-hoc study designs to retrieve more robust evidence.

Childhood trauma is linked to decreased temporal stability of functional brain networks in young adults

BACKGROUND

Both childhood trauma and disruptions in brain functional networks are implicated in the development of psychiatric disorders in early adulthood. However, the relationships between these two factors remain unclear. This study aimed to investigate whether and how childhood trauma would relate to changes of functional network dynamics in young adults.

METHODS

Resting-state functional magnetic resonance imaging data were collected from 53 young healthy adults, whose childhood trauma histories were assessed by the Childhood Trauma Questionnaire (CTQ). Network switching rate, a measure of stability of dynamic brain networks over time, was calculated at both global and local levels for each participant. Switching rates at both levels were compared between participants with and without childhood trauma, and further correlated with CTQ total score.

RESULTS

In the current sample, 19 (35.8%) participants reported a history of childhood trauma. At the global level, participants with childhood trauma showed significantly higher network switching rates than those without trauma (F = 10.021, p = 0.003). A significant positive correlation was found between network switching rates and CTQ scores in the entire sample (r = 0.378, p = 0.007). At the local level, these effects were mainly observed in the default-mode, fronto-parietal, cingulo-opercular, and occipital subnetworks.

CONCLUSIONS

Our study provides preliminary evidence for a possible long-term effect of childhood trauma on brain functional dynamism. These findings may have potential contributions to psychiatric disorders during adulthood.

Eight-week antidepressant treatment reduces functional connectivity in first-episode drug-naïve patients with major depressive disorder

Previous neuroimaging studies have revealed abnormal functional connectivity of brain networks in patients with major depressive disorder (MDD), but findings have been inconsistent. A recent big‐data study found abnormal intrinsic functional connectivity within the default mode network in patients with recurrent MDD but not in first‐episode drug‐naïve patients with MDD. This study also provided evidence for reduced default mode network functional connectivity in medicated MDD patients, raising the question of whether previously observed abnormalities may be attributable to antidepressant effects. The present study (ClinicalTrials.gov identifier: NCT03294525) aimed to disentangle the effects of antidepressant treatment from the pathophysiology of MDD and test the medication normalization hypothesis. Forty‐one first‐episode drug‐naïve MDD patients were administrated antidepressant medication (escitalopram or duloxetine) for 8 weeks, with resting‐state functional connectivity compared between posttreatment and baseline. To assess the replicability of the big‐data finding, we also conducted a cross‐sectional comparison of resting‐state functional connectivity between the MDD patients and 92 matched healthy controls. Both Network‐Based Statistic analyses and large‐scale network analyses revealed intrinsic functional connectivity decreases in extensive brain networks after treatment, indicating considerable antidepressant effects. Neither Network‐Based Statistic analyses nor large‐scale network analyses detected significant functional connectivity differences between treatment‐naïve patients and healthy controls. In short, antidepressant effects are widespread across most brain networks and need to be accounted for when considering functional connectivity abnormalities in MDD.

Brain Activation during Memory Retrieval is Associated with Depression Severity in Women

Major depressive disorder (MDD) is a debilitating disorder that interferes with daily functioning, and that occurs at higher rates in women than in men. Structural and functional alterations in hippocampus and frontal lobe have been reported in MDD, which likely contribute to the multifaceted nature of MDD. One area impacted by MDD is hippocampal-mediated memory, which can be probed using a spatial virtual Morris water task (MWT). Women (n=24) across a spectrum of depression severity underwent functional magnetic resonance imaging (fMRI) during MWT. Depression severity, assessed via Beck Depression Inventory (BDI), was examined relative to brain activation during task performance. Significant brain activation was evident in areas traditionally implicated in spatial memory processing, including right hippocampus and frontal lobe regions, for retrieval > motor contrast. When BDI was included as a regressor, significantly less functional activation was evident in left hippocampus, and other non-frontal, task relevant regions for retrieval > rest contrast. Consistent with previous studies, depression severity was associated with functional alterations observed during spatial memory performance. These findings may contribute to understanding neurobiological underpinnings of depression severity and associated memory impairments, which may have implications for treatment approaches aimed at alleviating effects of depression in women.

Structural-functional decoupling predicts suicide attempts in bipolar disorder patients with a current major depressive episode

Bipolar disorder (BD) is associated with a high risk of suicidality, and it is challenging to predict suicide attempts in clinical practice to date. Although structural and functional connectivity alterations from neuroimaging studies have been previously reported in BD with suicide attempts, little is known about how abnormal structural and functional connectivity relates to each other. Here, we hypothesize that structure connectivity constrains functional connectivity, and structural-functional coupling is a more sensitive biomarker to detect subtle brain abnormalities than any single modality in BD patients with a current major depressive episode who had attempted suicide. By investigating structural and resting-state fMRI connectivity, as well as their coupling among 191 BD depression patients with or without a history of suicide attempts and 113 healthy controls, we found that suicide attempters in BD depression patients showed significantly decreased central-temporal structural connectivity, increased frontal-temporal functional connectivity, along with decreased structural-functional coupling compared with non-suicide attempters. Crucially, the altered structural connectivity network predicted the abnormal functional connectivity network profile, and the structural-functional coupling was significantly correlated with suicide risk but not with depression or anxiety severity. Our findings suggest that the structural connectome is the key determinant of brain dysfunction, and structural-functional coupling could serve as a valuable trait-like biomarker for BD suicidal predication over and above the intramodality network connectivity. Such a measure can have clinical implications for early identification of suicide attempters with BD depression and inform strategies for prevention.

Covariation between Childhood-Trauma Related Resting-State Functional Connectivity and Affective Temperaments is Impaired in Individuals with Major Depressive Disorder

Affective temperaments and childhood-trauma experiences are associated with major depressive disorder (MDD). So far, how the covariation between distinct affective temperaments and childhood-trauma insulted brain functional connectivities (FCs) contribute to MDD remains unclear. Here, we aimed to investigate whether certain brain FC patterns are related to certain affective temperaments and whether the FCs contribute to depressive symptom dimensions of MDD patients. Twenty-nine medication-free MDD patients and 58 healthy controls underwent magnetic resonance imaging scanning and completed the Hamilton Depression Rating Scale (HDRS), the Hamilton Anxiety Rating Scale (HARS), the Childhood Trauma Questionnaire-Short Form (CTQ-SF), and the Temperament Evaluation of Memphis, Pisa, Paris and San Diego (TEMPS). Two multivariate analyses of partial least squares (PLS) regression were used to explore the associations among childhood-trauma related resting-state FCs, affective temperaments and depressive symptom dimensions. In all participants, a linear combination of 81 FCs (involving parahippocampus, amygdala, cingulate cortex, insula, frontal-temporal-parietal-occipital cortex, pallidum, and cerebellum) were associated with a linear combination of increased depressive, irritable, anxious, and cyclothymic temperaments. Moreover, the covariation between the PLS FC profile and the PLS affective-temperament profile were enhanced in the MDD patients compared to healthy controls. In MDD participants alone, the affective-temperament modulated FC profile (mainly of the lingual and temporal cortex) was associated with the somatization symptom dimension when age, sex, ill-duration, age-of-onset, and HARS scores were adjusted. The findings imply possible neural correlates of affective temperaments and may find applications in intervention of the somatization-depression symptoms by stimulation of the related neural correlates.

Frequency-Specific Changes in the Fractional Amplitude of the Low-Frequency Fluctuations in the Default Mode Network in Medication-Free Patients With Bipolar II Depression: A Longitudinal Functional MRI Study

Objective: This study aimed to examine the treatment-related changes of the fractional amplitude of low-frequency fluctuations (fALFF) in the default mode network (DMN) across different bands after the medication-free patients with bipolar II depression received a 16-week treatment of escitalopram and lithium. Methods: A total of 23 medication-free patients with bipolar II depression and 29 healthy controls (HCs) were recruited. We evaluated the fALFF values of slow 4 (0.027-0.073 Hz) band and slow 5 (0.01-0.027 Hz) band of the patients and compared the results with those of the 29 HCs at baseline. After 16-week treatment of escitalopram with lithium, the slow 4 and slow 5 fALFF values of the patients were assessed and compared with the baselines of patients and HCs. The depressive symptoms of bipolar II depression in patients were assessed with a 17-item Hamilton Depression Rating Scale (HDRS) before and after treatment. Results: Treatment-related effects showed increased slow 5 fALFF in cluster D (bilateral medial superior frontal gyrus, bilateral superior frontal gyrus, right middle frontal gyrus, and bilateral anterior cingulate), cluster E (bilateral precuneus/posterior cingulate, left cuneus), and cluster F (left angular, left middle temporal gyrus, left superior temporal gyrus, and left supramarginal gyrus) in comparison with the baseline of the patients. Moreover, a positive association was found between the increase in slow 5 fALFF values (follow-up value minus the baseline values) in cluster D and the decrease in HDRS scores (baseline HDRS scores minus follow-up HDRS scores) at follow-up, and the same association between the increase in slow 5 fALFF values and the decrease in HDRS scores was found in cluster E. Conclusions: The study reveals that the hypoactivity of slow 5 fALFF in the DMN is related to depression symptoms and might be corrected by the administration of escitalopram with lithium, implying that slow 5 fALFF of the DMN plays a key role in bipolar depression.