Brain structural effects of treatments for depression and biomarkers of response: a systematic review of neuroimaging studies

Multimodal Neuroimaging in the Prediction of Deep TMS Response in OCD

Backgrounds: Brain morphological biomarkers could contribute to understanding the treatment response in patients with obsessive-compulsive disorder (OCD). Multimodal neuroimaging addresses this issue by providing more comprehensive information regarding neural processes and structures. Objectives. The present study aims to investigate whether patients responsive to deep Transcranial Magnetic Stimulation (TMS) differ from non-responsive individuals in terms of electrophysiology and brain morphology. Secondly, to test whether multimodal neuroimaging is superior to unimodal neuroimaging in predicting response to deep TMS. Methods. Thirty-two OCD patients who underwent thirty sessions of deep TMS treatment were included in the study. Based on a minimum 50% reduction in Yale-Brown Obsessive Compulsive Scale (Y-BOCS) scores after treatment, patients were grouped as responders (n = 25) and non-responders (n = 7). The baseline resting state qEEG and magnetic resonance imaging (MRI) records of patients were recorded. Independent sample t-test is used to compare the groups. Then, three logistic regression model were calculated for only QEEG markers, only MRI markers, and both QEEG/MRI markers. The predictive values of the three models were compared. Results. OCD patients who responded to deep TMS treatment had increased Alpha-2 power in the left temporal area and increased volume in the left temporal pole, entorhinal area, and parahippocampal gyrus compared to non-responders. The logistic regression model showed better prediction performance when both QEEG and MRI markers were included. Conclusions. This study addresses the gap in the literature regarding new functional and structural neuroimaging markers and highlights the superiority of multimodal neuroimaging to unimodal neuroimaging techniques in predicting treatment response.

Alterations in gray matter volume and associated transcriptomics after electroconvulsive therapy in major depressive disorder

BACKGROUND

The antidepressant mechanism of electroconvulsive therapy (ECT) remains not clearly understood. This study aimed to detect the changes in gray matter volume (GMV) in patients with major depressive disorder (MDD) caused by ECT and exploratorily analyzed the potential functional mechanisms.

METHODS

A total of 24 patients with MDD who underwent eight ECT sessions were included in the study. Clinical symptom assessments and MRI scans were conducted and compared. Using whole-brain micro-array measurements provided by the Allen Human Brain Atlas (AHBA), regional gene expression profiles were calculated. The differential gene PLS1 was obtained through Partial Least Squares (PLS) regression analysis, and PLS1 was divided into positive contribution (PLS1+) and negative contribution (PLS1-) genes. Through gene function enrichment analysis, the functional pathways and cell types of PLS1 enrichment were identified.

RESULTS

Gray matter volume (GMV) in the somatosensory and motor cortices, occipital cortex, prefrontal cortex, and insula showed an increasing trend after ECT, while GMV in the temporal cortex, posterior cingulate cortex, and orbitofrontal cortex decreased. PLS1 genes were enriched in synapse- and cell-related biological processes and cellular components (such as 'pre- and post-synapse', 'synapse organization' etc.). A large number of genes in the PLS1+ list were involved in neurons (inhibitory and excitatory), whereas PLS1- genes were significantly involved in Astrocytes (Astro) and Microglia (Micro).

CONCLUSIONS

This study established a link between treatment-induced GMV changes and specific functional pathways and cell types, which suggests that ECT may exert its effects through synapse-associated functional and affect neurons and glial cells.

Neuroplastic changes in patients with functional seizures following neurobehavioral therapy

Given the high prevalence of functional neurological symptom disorder and its negative effects on the individual, family, and society, the development of interventions to treat it-including the subtype of functional seizures (FS)-is critical.Although we have limited understanding of the neurobiological effects of neurobehavioral therapy (NBT), studies indicate that NBT reduces seizures and improves psychological comorbidities in FS. In this study, healthy adults (N = 33) and patients with a history of TBI with (TBI-FS; N = 50) and without FS (TBI-only; N = 50) underwent magnetic resonance imaging (MRI) at 3 T approximately 12 weeks apart. TBI-FS participants underwent up to 12 sessions of NBT between scans. Structural MRI data were analyzed using voxel-based morphometry. A voxelwise repeated measures ANOVA tested changes in grey matter volume (GMV) between groups over time. Following treatment with NBT, TBI-FS participants showed a 1.23 % GMV increase in the left inferior and middle temporal gyri (pFWE < 0.05) along with a 35.78 % reduction in seizure events and decrease in depressive (p < 0.001) and anxiety (p = 0.01) symptoms. Left temporal GMV increases were directly associated (p = 0.04, r = 0.26) with improvements in overall psychological, social, and occupational functioning (p < 0.001). We observed structural brain changes within the left inferior temporal gyrus following NBT that correspond to functional and psychological improvements in patients with TBI-FS. This work highlights the need for further research into the neurobiological effects of NBT, building on the relationship between NBT and brain plasticity and demonstrating putative targets for interventions.

Melatonin Alleviates Behavioral and Neurodevelopmental Abnormalities in Offspring Caused by Prenatal Stress

BACKGROUND

Prenatal stress (PNS) is a significant risk factor impacting the lifelong health of offspring, and it has been widely recognized as being closely linked to the increased prevalence of neurodevelopmental disorders and psychiatric illnesses. However, effective pharmacological interventions to mitigate its detrimental effects remain limited. Melatonin (Mel), an endogenous hormone, has demonstrated considerable potential in treating neurological diseases due to its anti-inflammatory, antioxidant, and neuroprotective properties, as well as its favorable safety profile and broad clinical applicability.

OBJECTIVE

This study aims to investigate the protective effects and mechanisms of melatonin on neurodevelopmental and behavioral abnormalities in offspring induced by prenatal stress.

METHODS

Using a prenatal stress mouse model, we evaluated the effects of melatonin on emotional and cognitive deficits in offspring. Neurogenesis and synaptic development were assessed, and RNA sequencing was performed to analyze microglial gene enrichment and immune-related pathways. Both in vivo and in vitro experiments were conducted to validate the findings, focusing on the PI3K/AKT/NF-κB signaling pathway in microglia.

RESULTS

Melatonin administration alleviated emotional and cognitive deficits in offspring mice exposed to prenatal stress, addressing abnormalities in neurogenesis and synaptic development. Additionally, RNA sequencing revealed that melatonin suppresses microglial gene enrichment and the upregulation of immune-related pathways. Both in vivo and in vitro validation indicated that melatonin modulates the PI3K/AKT/NF-κB signaling pathway in microglia, reducing the elevated expression of CXCL10 in the dentate gyrus, thereby restoring normal neuro-supportive functions and optimizing the neurodevelopmental environment.

CONCLUSION

These findings suggest that melatonin significantly improves neurodevelopmental disorders and behavioral abnormalities caused by prenatal stress by inhibiting pathological microglial activation and promoting hippocampal neurogenesis and synaptic plasticity. This provides new insights into melatonin's potential as a neuroprotective agent for treating prenatal stress-related disorders.

Perspectives in treatment-resistant depression: esketamine and electroconvulsive therapy

Modern electroconvulsive therapy (ECT) and the approval of nasal esketamine for clinical use have significantly improved the approach to treatment-resistant depression (TRD), which is defined as non-response to at least two different courses of antidepressants with verified adherence to treatment, adequate dosage, and duration of treatment. The goal of this literature review is to present the newest evidence regarding efficacy and safety. Furthermore, we aim to provide an overview of future perspectives in this field of research, for example, regarding structural and molecular effects. Both treatment methods will be critically evaluated for their individual advantages, disadvantages, and response rates. Firstly, we will discuss the well-established method of ECT and its different treatment modalities. Secondly, we will discuss the properties of ketamine, the discovery of its antidepressive effects and the route to clinical approval of the esketamine nasal spray. We will comment on research settings which have evaluated intravenous ketamine against ECT. The decision-making process between esketamine nasal spray or ECT should include the assessment of contraindications, age, severity of disease, presence of psychotic symptoms, patient preference and treatment accessibility. We conclude that both treatment options are highly effective in TRD. If both are indicated, pragmatically esketamine will be chosen before ECT; however, ECT studies in ketamine non-responders are missing.

Middle frontal gyrus volume mediates the relationship between interleukin-1β and antidepressant response in major depressive disorder

Inflammation is a leading biological risk factor contributing to unfavorable outcomes of major depressive disorder (MDD). Both inflammation and depression are associated with similar alterations in brain structure, indicating that brain structural alterations could serve as a mediating factor in the adverse influence of inflammation on clinical outcomes in MDD. Nonetheless, longitudinal research has yet to confirm this hypothesis. Therefore, this study aimed at elucidating the relationships between peripheral inflammatory cytokines, gray matter volume (GMV) alterations, and antidepressant response in MDD. We studied 104 MDD patients treated with selective serotonin reuptake inhibitors and 85 healthy controls (HCs). Antidepressant response was assessed after 8-week antidepressant treatment by changes in 17-item Hamilton Depression Rating Scale (HAMD-17) scores. The GMV alterations were investigated using a voxel-based morphometry analysis. Inflammatory cytokines were measured using flow cytometry. Partial correlations were used to explore the relationships between inflammatory cytokines, GMV alterations, and antidepressant response. Compared to HCs, MDD patients showed reduced GMVs primarily in the frontal-limbic area, right insula, and right superior temporal gyrus. Furthermore, the alterations in GMVs, particularly in the right middle frontal gyrus and the left anterior cingulate gyrus, were associated with ΔHAMD-17 and inflammatory cytokines. Additionally, GMV alterations in the right middle frontal gyrus mediated the negative relationship between interleukin -1β and ΔHAMD-17. This study contributes to understanding the effect of inflammation on the brain and their relationships with antidepressant response, offering a potential explanation for the connection between inflammatory status and treatment efficacy.

Cardiovascular Risk Predicts White Matter Hyperintensities, Brain Atrophy and Treatment Resistance in Major Depressive Disorder: Role of Genetic Liability

INTRODUCTION

Depressive disorders are a leading cause of global disease burden, particularly with the challenge of treatment-resistant depression (TRD). Research points to a complex bidirectional relationship between cardiovascular (CV) risk factors and TRD, with CV risk negatively impacting brain structure and potentially influencing antidepressant resistance. Moreover, the association between depression and the genetic vulnerability to cardiovascular disease suggests a shared pathophysiological process between the two. This study investigates the mediating role of brain structural alterations in the relationship between CV and cerebrovascular (CeV) risk and treatment resistance in depression.

METHODS

We assessed 165 inpatients with Major depressive disorder. Each patient's CV risk was assessed via the QRISK 3 calculator. For a subset of patients, CV and CeV disease polygenic risk scores (PRS) were obtained. All patients underwent a 3 T MRI scan, and white matter hyperintensities estimates and indicators of brain trophic state were obtained.

RESULTS

Both CV risk and CV disease PRSs are associated with treatment resistance status, white matter hyperintensities, and indicators of brain atrophy. Mediation analyses suggested that CV-induced brain alterations might underlie the relation between CV genetic and phenotypic risk and antidepressant treatment resistance.

CONCLUSION

These results underscore the need to explore cardiovascular risk management as part of treatment strategies for depression, pointing toward a shared pathophysiological process linking heart and brain health in treatment-resistant depression.

A systematic review of structural neuroimaging markers of psychotherapeutic and pharmacological treatment for obsessive-compulsive disorder

Identifying individual difference factors associated with treatment response and putative mechanisms of therapeutic change may improve treatment for Obsessive Compulsive Disorder (OCD). Our systematic review of structural neuroimaging markers (i.e., morphometry, structural connectivity) of psychotherapy and medication treatment response for OCD identified 26 eligible publications from 20 studies (average study total n=54 ± 41.6 [range: 11-175]; OCD group n=29 ± 19) in child, adolescent, and adult samples evaluating baseline brain structure correlates of treatment response as well as treatment-related changes in brain structure. Findings were inconsistent across studies; significant associations within the anterior cingulate cortex (3/5 regional, 2/8 whole brain studies) and orbitofrontal cortex (5/10 regional, 2/7 whole brain studies) were most common, but laterality and directionality were not always consistent. Structural neuroimaging markers of treatment response do not currently hold clinical utility. Given increasing evidence that associations between complex behavior and brain structure are characterized by small, but potentially meaningful, effects, much larger samples are likely needed. Multivariate approaches (e.g., machine learning) may also improve the clinical predictive utility of neuroimaging data.

Abnormal choroid plexus, hippocampus, and lateral ventricles volumes as markers of treatment-resistant major depressive disorder

AIM

One-third of patients with major depressive disorder (MDD) do not achieve full remission and have high relapse rates even after treatment, leading to increased medical costs and reduced quality of life and health status. The possible specificity of treatment-resistant depression (TRD) neurobiology is still under investigation, with risk factors such as higher inflammatory markers being identified. Given recent findings on the role of choroid plexus (ChP) in neuroinflammation and hippocampus in treatment response, the aim of the present study was to evaluate inflammatory- and trophic-related differences in these regions along with ventricular volumes among patients with treatment-sensitive depression (TSD), TRD, and healthy controls (HCs).

METHODS

ChP, hippocampal, and ventricular volumes were assessed in 197 patients with MDD and 58 age- and sex-matched HCs. Volumes were estimated using FreeSurfer 7.2. Treatment resistance status was defined as failure to respond to at least two separate antidepressant treatments. Region of interest volumes were then compared among groups.

RESULTS

We found higher ChP volumes in patients with TRD compared with patients with TSD and HCs. Our results also showed lower hippocampal volumes and higher lateral ventricular volumes in TRD compared with both patients without TRD and HCs.

CONCLUSIONS

These findings corroborate the link between TRD and neuroinflammation, as ChP volume could be considered a putative marker of central immune activity. The lack of significant differences in all of the region of interest volumes between patients with TSD and HCs may highlight the specificity of these features to TRD, possibly providing new insights into the specific neurobiological underpinnings of this condition.

Investigating amygdala nuclei volumes in military personnel with post-traumatic stress disorder, major depressive disorder, and adjustment disorder: A retrospective cross-sectional study using clinical routine data

BACKGROUND

Post-traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD), and Adjustment Disorder (AdjD) are highly prevalent among military personnel, often presenting diagnostic challenges due to overlapping symptoms and reliance on self-reporting. The amygdala, particularly the basolateral complex involved in fear-related memory formation and extinction recall, plays a crucial role in emotional processing. Abnormalities in these amygdala nuclei are implicated in PTSD and may distinguish it from other disorders like MDD and AdjD, where these mechanisms are less central. Investigating structural differences in specific amygdala nuclei could enhance diagnostic precision and inform targeted interventions.

GOAL

This study aimed to explore volumetric differences in amygdala nuclei among patients with PTSD, MDD, comorbid PTSD and MDD (PTSD+MDD), and AdjD using routine clinical MRI data. We hypothesized that patients with PTSD would exhibit distinct amygdala nuclei volumes compared to those with MDD or AdjD. Additionally, we examined the influence of symptom duration, prior medication, and psychotherapeutic experience on amygdala volumes.

METHODS

We conducted a retrospective cross-sectional study with 185 military personnel (162 men, 23 women) diagnosed with PTSD (n = 50), MDD (n = 70), PTSD+MDD (n = 38), and AdjD (n = 27). High-resolution T1-weighted MRI scans were obtained using a 3T Siemens Skyra scanner. Amygdala subfields were automatically segmented and volumetrized using FreeSurfer software. Analysis of covariance (ANCOVA) models compared amygdala nuclei volumes across diagnostic groups, controlling for estimated total intracranial volume (eTIV), age, and gender. Exploratory analyses included symptom duration, medication use, and prior psychotherapy as additional covariates. Sensitivity analyses further examined the impact of depressive episode type (first vs. recurrent), severity (mild, moderate, severe), and AdjD symptom duration.

RESULTS

The main analyses revealed no significant differences in the volumes of the basolateral and medial amygdala nuclei among the PTSD, MDD, PTSD+MDD, and AdjD groups. Exploratory analyses did not identify significant associations between amygdala volumes and symptom duration, medication use, or prior psychotherapy. Sensitivity analyses also showed no significant volumetric differences related to depressive episode type, severity, or AdjD symptom duration.

CONCLUSION

Our findings suggest that, within this military population, amygdala nuclei volumes measured using routine clinical MRI data do not significantly differ among patients with PTSD, MDD, PTSD+MDD, and AdjD. This indicates that structural amygdala volumetry alone may not suffice to distinguish between these stress-related disorders in clinical settings. The study highlights the complexity of diagnosing overlapping mental health conditions and underscores the need for comprehensive approaches that integrate neuroimaging with clinical assessments. Future research should include healthy control groups, consider additional brain regions and functional connectivity, and employ longitudinal designs to better understand the temporal dynamics of amygdala changes and their relation to symptomatology.

Depression, brain structure and socioeconomic status: A UK Biobank study

BACKGROUND

Depression results from interactions between biological, social, and psychological factors. Literature shows that depression is associated with abnormal brain structure, and that socioeconomic status (SES) is associated with depression and brain structure. However, limited research considers the interaction between each of these factors.

METHODS

Multivariate regression analysis was conducted using UK Biobank data on 39,995 participants to examine the relationship between depression and brain volume in 23 cortical regions for the whole sample and then separated by sex. It then examined whether SES affected this relationship.

RESULTS

Eight out of 23 brain areas had significant negative associations with depression in the whole population. However, these relationships were abolished in seven areas when SES was included in the analysis. For females, three regions had significant negative associations with depression when SES was not included, but only one when it was. For males, lower volume in six regions was significantly associated with higher depression without SES, but this relationship was abolished in four regions when SES was included. The precentral gyrus was robustly associated with depression across all analyses.

LIMITATIONS

Participants with conditions that could affect the brain were not excluded. UK Biobank is not representative of the general population which may limit generalisability. SES was made up of education and income which were not considered separately.

CONCLUSIONS

SES affects the relationship between depression and cortical brain volume. Health practitioners and researchers should consider this when working with imaging data in these populations.

Changes in hippocampal volume, 5-HT4 receptor binding, and verbal memory over the course of antidepressant treatment in major depressive disorder

Serotonin reuptake inhibitors have been reported to increase hippocampal volume and improve memory function in patients with Major depressive disorder (MDD). The postsynaptic 5-HT4 receptor (5-HT4R) is involved in hippocampal development, familial risk for depression and depressive pathology. In an open-label trial with 91 patients (72% female, mean 27.2 years) with MDD, we investigated the relation between changes in hippocampal volume, 5-HT4R, and verbal memory during 12 weeks treatment with 10-20 mg escitalopram. Depression severity, verbal memory, MRI-determined hippocampus volume and PET-determined 5-HT4R were measured pretreatment. Forty-three patients were rescanned at week 8. HAMD17 was reassessed at week 8 and together with verbal memory at week 12. We used mixed-effects models and linear regressions. We estimated a 27 mm3 (p = 0.086) reduction in mean hippocampus volume over the course of eight weeks. In patients clinically responding to treatment, we estimated a 45 mm3 reduction (p = 0.019), 8 mm3 increase in non-responders (p = 0.78), and a 52 mm3 group difference (p = 0.12). Hippocampal 5-HT4 receptor binding before treatment and at week eight was negatively associated with hippocampal volume in females, regardless of treatment response (p-values≤0.006). However, no clear evidence for an association in males or sex interaction could be established (p-values≥0.16). Although the hippocampus volume did not increase with treatment, we found a decrease in clinically responsive patients. Our findings suggest an association between 5-HT4R signalling and changes in hippocampal volume in females with MDD during antidepressant treatment, highlighting the need for further investigation into the role of serotonergic mechanisms in hippocampal plasticity.

Effects of Electroconvulsive Therapy on Brain Structure: A Neuroradiological Investigation Into White Matter Hyperintensities, Atrophy, and Microbleeds

BACKGROUND

Electroconvulsive therapy (ECT) is a well-established treatment for severe depression, but it remains stigmatized due to public perceptions linking it with brain injury. Despite extensive research, the neurobiological mechanisms underlying ECT have not been fully elucidated. Recent findings suggest that ECT may work through disrupting depression circuitry. However, whether ECT is associated with neuroradiological correlates of brain injury, including white matter changes, atrophy, and microbleeds, remains largely unexplored.

METHODS

We performed magnetic resonance imaging (MRI) scans on 36 ECT patients (19 female), 19 healthy control participants (11 female), and 18 patients with atrial fibrillation (1 female) who were treated with electrical cardioversion while receiving an equivalent anesthetic as the ECT group. Scans were conducted at 4 time points: at baseline, after the first ECT treatment, after the ECT series, and at 6-month follow-up. We evaluated white matter changes using the Fazekas and the age-related white matter changes scales, atrophy using the global cortical atrophy and medial temporal lobe atrophy scales, and cerebral microbleeds using the Microbleed Anatomical Rating Scale. Data were analyzed using nonparametric statistical methods.

RESULTS

Patients did not show any changes in radiological scores after ECT (all ps > .1), except for a decrease in microbleeds (p = .05).

CONCLUSIONS

Utilizing state-of-the-art MRI techniques, we found no significant evidence that ECT induces white matter changes, atrophy, or microbleeds. Thus, although ECT may work through disrupting depression circuitry, the treatment is not associated with neuroradiological signs of brain injury.

Optimizing Antidepressant Efficacy: Generalizable Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response

Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R2 = 0.31; placebo: R2 = 0.22). Remarkably, the sertraline response biomarker is further tested on an independent escitalopram-medicated cohort of MDD patients, validating its generalizability (p = 0.01) and suggesting an overlap of psychopharmacological mechanisms across selective serotonin reuptake inhibitors. Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel and reliable insights into the intricate neuropsychopharmacology of antidepressant treatment, paving the way for advances in precision medicine and development of more targeted antidepressant therapeutics.

Habenular volume changes after venlafaxine treatment in patients with major depression

BACKGROUND

Habenula, a hub brain region controlling monoaminergic brain center, has been implicated in major depressive disorder (MDD) and as a possible target of antidepressant response. Nevertheless, the effect of antidepressant drug treatment on habenular volumes remains unknown. The objective of the present research was to study habenular volume change after antidepressant treatment in patients with MDD, and assess whether it is associated with clinical improvement.

METHODS

Fifty patients with a current major depressive episode (MDE) in the context of MDD, and antidepressant-free for at least 1 month, were assessed for habenula volume (3T MRI with manual segmentation) before and after a 3 months sequence of venlafaxine antidepressant treatment.

RESULTS

A 2.3% significant increase in total habenular volume (absolute volume: P = 0.0013; relative volume: P = 0.0055) and a 3.3% significant increase in left habenular volume (absolute volume: P = 0.00080; relative volume: P = 0.0028) were observed. A significant greater variation was observed in male patients (4.8%) compared to female patients. No association was observed between habenular volume changes and response and remission. Some habenula volume changes were associated with improvement of olfactory pleasantness.

CONCLUSION

Habenular volumes increased after 3 months of venlafaxine treatment in depressed patients. Further studies should assess whether cell proliferation and density or dendritic structure variations are implied in these volume changes.

Molecular mechanisms underlying structural plasticity of electroconvulsive therapy in major depressive disorder

Although previous studies reported structural changes associated with electroconvulsive therapy (ECT) in major depressive disorder (MDD), the underlying molecular basis of ECT remains largely unknown. Here, we combined two independent structural MRI datasets of MDD patients receiving ECT and transcriptomic gene expression data from Allen Human Brain Atlas to reveal the molecular basis of ECT for MDD. We performed partial least square regression to explore whether/how gray matter volume (GMV) alterations were associated with gene expression level. Functional enrichment analysis was conducted using Metascape to explore ontological pathways of the associated genes. Finally, these genes were further assigned to seven cell types to determine which cell types contribute most to the structural changes in MDD patients after ECT. We found significantly increased GMV in bilateral hippocampus in MDD patients after ECT. Transcriptome-neuroimaging association analyses showed that expression levels of 726 genes were positively correlated with the increased GMV in MDD after ECT. These genes were mainly involved in synaptic signaling, calcium ion binding and cell-cell signaling, and mostly belonged to excitatory and inhibitory neurons. Moreover, we found that the MDD risk genes of CNR1, HTR1A, MAOA, PDE1A, and SST as well as ECT related genes of BDNF, DRD2, APOE, P2RX7, and TBC1D14 showed significantly positive associations with increased GMV. Overall, our findings provide biological and molecular mechanisms underlying structural plasticity induced by ECT in MDD and the identified genes may facilitate future therapy for MDD.

Low-dose interleukin 2 antidepressant potentiation in unipolar and bipolar depression: Safety, efficacy, and immunological biomarkers

Immune-inflammatory mechanisms are promising targets for antidepressant pharmacology. Immune cell abnormalities have been reported in mood disorders showing a partial T cell defect. Following this line of reasoning we defined an antidepressant potentiation treatment with add-on low-dose interleukin 2 (IL-2). IL-2 is a T-cell growth factor which has proven anti-inflammatory efficacy in autoimmune conditions, increasing thymic production of naïve CD4 + T cells, and possibly correcting the partial T cell defect observed in mood disorders. We performed a single-center, randomised, double-blind, placebo-controlled phase II trial evaluating the safety, clinical efficacy and biological responses of low-dose IL-2 in depressed patients with major depressive (MDD) or bipolar disorder (BD). 36 consecutively recruited inpatients at the Mood Disorder Unit were randomised in a 2:1 ratio to receive either aldesleukin (12 MDD and 12 BD) or placebo (6 MDD and 6 BD). Active treatment significantly potentiated antidepressant response to ongoing SSRI/SNRI treatment in both diagnostic groups, and expanded the population of T regulatory, T helper 2, and percentage of Naive CD4+/CD8 + immune cells. Changes in cell frequences were rapidly induced in the first five days of treatment, and predicted the later improvement of depression severity. No serious adverse effect was observed. This is the first randomised control trial (RCT) evidence supporting the hypothesis that treatment to strengthen the T cell system could be a successful way to correct the immuno-inflammatory abnormalities associated with mood disorders, and potentiate antidepressant response.

Multi-modal morphometric association study of subclinical depressive symptoms using voxel-based morphometry, cortical thickness, and diffusion tensor imaging (DTI)

BACKGROUND

Case-control studies in major depression have established numerous regional grey and white matter effects in fronto-limbic brain regions. Yet, brain structural studies of dimensional depressive psychopathology within the subclinical spectrum are still limited, in particular for multi-modal imaging approaches.

METHODS

Using voxel-based and surface-based morphometry (cortical thickness) in combination with diffusion tensor imaging (DTI) in a large non-clinical sample (N = 300), we correlated grey and white matter structural variation with subclinical depressive symptoms assessed with Beck's Depression inventory (BDI).

RESULTS

We found a significant decrease of axial diffusivity associated with higher BDI scores in the left hippocampal part of the cingulum bundle (p < 0.05, threshold free cluster enhanced [TFCE] p-value) and some grey matter trend results e.g., a non-linear negative correlation of cortical thickness with depressive symptom load in the right pre/postcentral cortex (pFWE = 0.054, family wise error [FWE] peak level corrected) and a trend in grey matter volume decrease in women in the inferior frontal gyrus (pFWE = 0.054).

LIMITATIONS

Since all grey matter effects disappear after FWE correction, we assume more stable effects in a larger, less homogenous sample enriched by help-seeking subjects covering a wider range of subclinical psychopathology.

CONCLUSION

Our study adds correlations between single depressive symptoms and brain structure to a growing literature. Since subclinical depression is increasingly recognised to be relevant in our understanding of manifest depression, early detection and identification of potential brain correlates of minor depressive symptoms has the potential to expand and reveal possible biomarkers and early psychological treatment.

Long-term effects of electroconvulsive therapy on brain structure in major depression

BACKGROUND

Magnetic resonance imaging (MRI) studies on major depressive disorder (MDD) have predominantly found short-term electroconvulsive therapy (ECT)-related gray matter volume (GMV) increases, but research on the long-term stability of such changes is missing. Our aim was to investigate long-term GMV changes over a 2-year period after ECT administration and their associations with clinical outcome.

METHODS

In this nonrandomized longitudinal study, patients with MDD undergoing ECT (n = 17) are assessed three times by structural MRI: Before ECT (t0), after ECT (t1) and 2 years later (t2). A healthy (n = 21) and MDD non-ECT (n = 33) control group are also measured three times within an equivalent time interval. A 3(group) × 3(time) ANOVA on whole-brain level and correlation analyses with clinical outcome variables is performed.

RESULTS

Analyses yield a significant group × time interaction (pFWE < 0.001) resulting from significant volume increases from t0 to t1 and decreases from t1 to t2 in the ECT group, e.g., in limbic areas. There are no effects of time in both control groups. Volume increases from t0 to t1 correlate with immediate and delayed symptom increase, while volume decreases from t1 to t2 correlate with long-term depressive outcome (all p ⩽ 0.049).

CONCLUSIONS

Volume increases induced by ECT appear to be a transient phenomenon as volume strongly decreased 2 years after ECT. Short-term volume increases are associated with less symptom improvement suggesting that the antidepressant effect of ECT is not due to volume changes. Larger volume decreases are associated with poorer long-term outcome highlighting the interplay between disease progression and structural changes.

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

BACKGROUND

Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting.

METHODS

Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier.

RESULTS

Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC).

CONCLUSIONS

These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.

Electroconvulsive Therapy (ECT) in Major Depression: Oldies but Goodies

Electroconvulsive therapy is one of the useful treatment methods for symptom improvement and remission in patients with treatment-resistant depression. Considering the various clinical characteristics of patients experiencing depression, key indicators are extracted from structural brain magnetic resonance imaging, functional brain magnetic resonance imaging, and electroencephalography (EEG) data taken before treatment, and applied as explanatory variables in machine learning and network analysis. Studies that attempt to make reliable predictions about the degree of response to electroconvulsive treatment and the possibility of remission in patients with treatment-resistant depression are continuously being published. In addition, studies are being conducted to identify the correlation with clinical improvement by taking structural-functional brain magnetic resonance imaging after electroconvulsive therapy in depressed patients. By reviewing and integrating the results of the latest studies on the above matters, we aim to present the usefulness of electroconvulsive therapy for improving the personalized prognosis of patients with treatment-resistant depression.

Relation of hippocampal volume and SGK1 gene expression to treatment remission in major depression is moderated by childhood maltreatment: A CAN-BIND-1 report

Preclinical research implicates stress-induced upregulation of the enzyme, serum- and glucocorticoid-regulated kinase 1 (SGK1), in reduced hippocampal volume. In the current study, we tested the hypothesis that greater SGK1 mRNA expression in humans would be associated with lower hippocampal volume, but only among those with a history of prolonged stress exposure, operationalized as childhood maltreatment (physical, sexual, and/or emotional abuse). Further, we examined whether baseline levels of SGK1 and hippocampal volume, or changes in these markers over the course of antidepressant treatment, would predict treatment outcomes in adults with major depression [MDD]. We assessed SGK1 mRNA expression from peripheral blood, and left and right hippocampal volume at baseline, as well as change in these markers over the first 8 weeks of a 16-week open-label trial of escitalopram as part of the Canadian Biomarker Integration Network in Depression program (MDD [n = 161] and healthy comparison participants [n = 91]). Childhood maltreatment was assessed via contextual interview with standardized ratings. In the full sample at baseline, greater SGK1 expression was associated with lower hippocampal volume, but only among those with more severe childhood maltreatment. In individuals with MDD, decreases in SGK1 expression predicted lower remission rates at week 16, again only among those with more severe maltreatment. Decreases in hippocampal volume predicted lower week 16 remission for those with low childhood maltreatment. These results suggest that both glucocorticoid-related neurobiological mechanisms of the stress response and history of childhood stress exposure may be critical to understanding differential treatment outcomes in MDD. ClinicalTrials.gov: NCT01655706 Canadian Biomarker Integration Network for Depression Study.

Sex Determines Anterior Cingulate Cortex Cortical Thickness in the Course of Depression

BACKGROUND

Major depressive disorder (MDD) is a severe psychiatric disorder affecting women more than men. Changes in anterior cingulate cortex cortical thickness (ACC CT) may be crucial to understanding sex influences in MDD onset and recurrency.

METHODS

Taken from the large open-source REST-meta-MDD database, we contrasted 499 patients with MDD (381 first-episode MDD, 118 recurrent MDD) and 524 healthy control participants using linear mixed-effects models and normative modeling and investigated whether sex differences affected ACC CT and its subregions differently during the course of depressive illness.

RESULTS

Overall, females showed thinner ACC CT compared with males. Female patients with a first depressive episode showed significantly thinner ACC CT compared with male patients with first-episode MDD (Cohen's d = -0.65), including in the perigenual ACC and the subgenual ACC, but not in the dorsal ACC. Moreover, male patients with first-episode depression showed thicker ACC CT (including subgenual ACC and pregenual ACC) compared to the male patients with recurrent MDD (Cohen's d = 1.24), but they also showed significantly thicker cortices in the same subregions in comparison to never-depressed males (Cohen's d = 0.85). No lateralization differences were observed in ACC CT or its subdivisions.

CONCLUSIONS

Sex determined ACC CT changes over the course of depressive illness. Because the ACC subdivisions in question are associated with dysregulation of emotions, our observations substantiate the need for early and prolonged sex-specific clinical interventions.

Association between NTRK2 Polymorphisms, Hippocampal Volumes and Treatment Resistance in Major Depressive Disorder

Despite the increasing availability of antidepressant drugs, a high rate of patients with major depression (MDD) does not respond to pharmacological treatments. Brain-derived neurotrophic factor (BDNF)-tyrosine receptor kinase B (TrkB) signaling is thought to influence antidepressant efficacy and hippocampal volumes, robust predictors of treatment resistance. We therefore hypothesized the possible role of BDNF and neurotrophic receptor tyrosine kinase 2 (NTRK2)-related polymorphisms in affecting both hippocampal volumes and treatment resistance in MDD. A total of 121 MDD inpatients underwent 3T structural MRI scanning and blood sampling to obtain genotype information. General linear models and binary logistic regressions were employed to test the effect of genetic variations related to BDNF and NTRK2 on bilateral hippocampal volumes and treatment resistance, respectively. Finally, the possible mediating role of hippocampal volumes on the relationship between genetic markers and treatment response was investigated. A significant association between one NTRK2 polymorphism with hippocampal volumes and antidepressant response was found, with significant indirect effects. Our results highlight a possible mechanistic explanation of antidepressant action, possibly contributing to the understanding of MDD pathophysiology.

Hippocampal and parahippocampal volume and function predict antidepressant response in patients with major depression: A multimodal neuroimaging study

BACKGROUND

For many patients with major depressive disorder (MDD) adequate treatment remains elusive. Neuroimaging techniques received attention for their potential use in guiding and predicting response, but were rarely investigated in real-world psychiatric settings.

AIMS

To identify structural and functional Magnetic Resonance Imaging (MRI) biomarkers associated with antidepressant response in a real-world clinical sample.

METHODS

We studied 100 MDD inpatients admitted to our psychiatric ward, treated with various antidepressants upon clinical need. Hamilton Depression Rating Scale percentage decrease from admission to discharge was used as a measure of response. All patients underwent 3.0 T MRI scanning. Grey matter (GM) volumes were investigated both in a voxel-based morphometry (VBM), and in a regions of interest (ROI) analysis. In a subsample of patients, functional resting-state connectivity patterns were also explored.

RESULTS

In the VBM analysis, worse response was associated to lower GM volumes in two clusters, encompassing the left hippocampus and parahippocampal gyrus, and the right superior and middle temporal gyrus. Investigating ROIs, lower bilateral hippocampi and amygdalae volumes predicted worse treatment outcomes. Functional connectivity in the right temporal and parahippocampal gyrus was also associated to response.

CONCLUSION

Our results expand existing literature on the relationship between the structure and function of several brain regions and treatment response in MDD. While we are still far from routine use of MRI biomarkers in clinical practice, we confirm a possible role of these techniques in guiding treatment choices and predicting their efficacy.

Normative modeling analysis reveals corpus callosum volume changes in early and mid-to-late first episode major depression

BACKGROUND

It has been widely accepted that major depressive disorder (MDD) impacts brain structures including the Corpus Callosum (CC). However, this assumption is based on scarce literature data involving small sample sizes. Furthermore, it is still unclear whether such CC volume changes may already be present at a first depressive episode.

METHODS

To further investigate this question, we compared 369 first-episode MDD patients (mean age = 35 years (sd = 12), 249 females; 283 early onset, 86 mid-to-late onset) from the open-source REST meta-MDD database closely matched for age and gender to 490 never-depressed individuals (mean age = 37 years (sd = 14); 309 females) using Z-scores obtained from normative neuroanatomical modeling to assess individual variability in CC (sub)volumes.

RESULTS

Relative to the norms established by the healthy controls, first-episode MDD patients displayed CC volume (z-score) reductions in the entire CC (including the body), as did mid-to-late-onset first-episode MDD patients (age ≥ 45 y). In early-onset first-episode MDD patients (age ≤ 44 y), depression severity symptoms were related to volume increases in the entire CC, as well as the body and splenium.

LIMITATIONS

No data on depressive episode duration. Relatively small sample size for mid-to-late first-episode MDD patients.

CONCLUSIONS

Our data revealed CC (sub)volume differences in early versus mid-to-late onset first episode MDD. Especially at early onset, depression severity may result in neural white matter activity as potential reaction to stress influences. Our results underline the importance of prompt clinical interventions at early onset MDD.

Neutrophil to lymphocyte ratio and antidepressant treatment response in patients with major depressive disorder: Effect of sex and hippocampal volume

Several factors may affect response to treatment in Major Depressive Disorder (MDD) including immune/inflammatory alterations and regional brain volumes, particularly in hippocampal regions which have shown to be influenced by inflammatory status. Neutrophil-to-lymphocyte ratio (NLR) is an inflammatory marker found to be elevated in depressed women in large population studies. Here we investigate the effect of NLR on treatment response in MDD patients, and the role of sex and hippocampal volume on influencing this relationship. A sample of 124 MDD depressed inpatients (F = 80) underwent MRI acquisition, admission NLR was calculated by dividing absolute neutrophil by absolute lymphocyte counts and depression severity was assessed at admission and discharge via the Hamilton Depression Rating Scale (HDRS). As a measure of treatment response, delta HDRS was calculated. We found a significant moderation effect of sex on the relationship between NLR and Delta HDRS: a negative relation was found in females and a positive one in males. NLR was found to negatively affect hippocampal volumes in females. Both left and right hippocampal volume positively associated with Delta HDRS. Finally, left hippocampal volume mediated the effect of NLR on Delta HDRS in females. Sex moderated the relation between inflammation and treatment response in line with previous reports linking inflammation to hampered antidepressant effect in females. Further, this effect is partially mediated by hippocampal volume, suggesting that antidepressant response may be hampered by the detrimental effect of inflammation on the brain.

Altered topology of individual brain structural covariance networks in major depressive disorder

BACKGROUND

The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks.

METHODS

We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics.

RESULTS

Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex.

CONCLUSIONS

The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.

Reduced cerebral cortex thickness is related to overexpression of exosomal miR-146a-5p in medication-free patients with major depressive disorder

BACKGROUND

Previous studies have confirmed that miR-146a-5p overexpression suppresses neurogenesis, thereby enhancing depression-like behaviors. However, it remains unclear how miR-146a-5p dysregulation produces in vivo brain structural abnormalities in patients with major depressive disorder (MDD).

METHODS

In this case-control study, we combined cortical morphology analysis of magnetic resonance imaging (MRI) and miR-146a-5p quantification to investigate the neuropathological effect of miR-146a-5p on cortical thickness in MDD patients. Serum-derived exosomes that were considered to readily cross the blood-brain barrier and contain miR-146a-5p were isolated for miRNA quantification. Moreover, follow-up MRI scans were performed in the MDD patients after 6 weeks of antidepressant treatment to further validate the clinical relevance of the relationship between miR-146a-5p and brain structural abnormalities.

RESULTS

In total, 113 medication-free MDD patients and 107 matched healthy controls were included. Vertex-vise general linear model revealed miR-146a-5p-dependent cortical thinning in MDD patients compared with healthy individuals, i.e., overexpression of miR-146a-5p was associated with reduced cortical thickness in the left orbitofrontal cortex (OFC), anterior cingulate cortex, bilateral lateral occipital cortices (LOCs), etc. Moreover, this relationship between baseline miR-146a-5p and cortical thinning was nonsignificant for all regions in the patients who had received antidepressant treatment, and higher baseline miR-146a-5p expression was found to be related to greater longitudinal cortical thickening in the left OFC and right LOC.

CONCLUSIONS

The findings of this study reveal a relationship between miR-146a-5p overexpression and cortical atrophy and thus may help specify the in vivo mediating effect of miR-146a-5p dysregulation on brain structural abnormalities in patients with MDD.

Machine learning prediction will be part of future treatment of depression

Machine learning (ML) is changing the way that medicine is practiced. While already clinically utilised in diagnostic radiology and outcome prediction in intensive care unit, ML approaches in psychiatry remain nascent. Implementing ML algorithms in psychiatry, particularly in the treatment of depression, is significantly more challenging than other areas of medicine in part because of the less demarcated disease nosology and greater variability in practice. Given the current exiguous capacity of clinicians to predict patient and treatment outcomes in depression, there is a significantly greater need for better predictive capability. Early studies have shown promising results. ML predictions were significantly better than chance within the sequenced treatment alternatives to relieve depression (STAR*D) trial (accuracy 64.6%, p < 0.0001) and combining medications to enhance depression outcomes (COMED) randomised Controlled Trial (RCT) (accuracy 59.6%, p = 0.043), with similar results found in larger scale, retrospective studies. The greater flexibility and dimensionality of ML approaches has been demonstrated in studies incorporating diverse input variables including electroencephalography scans, achieving 88% accuracy for treatment response, and cognitive test scores, achieving up to 72% accuracy for treatment response. The predicting response to depression treatment (PReDicT) trial tested ML informed prescribing of antidepressants against standard therapy and found there was both better outcomes for anxiety and functional endpoints despite the algorithm only having a balanced accuracy of 57.5%. Impeding the progress of ML algorithms in psychiatry are pragmatic hurdles, including accuracy, expense, acceptability and comprehensibility, and ethical hurdles, including medicolegal liability, clinical autonomy and data privacy. Notwithstanding impediments, it is clear that ML prediction algorithms will be part of depression treatment in the future and clinicians should be prepared for their arrival.

Progressive grey matter alterations in bipolar disorder across the life span - A systematic review

OBJECTIVES

To elucidate the relationship between the course of bipolar disorder (BD) and structural brain changes across the life span, we conducted a systematic review of longitudinal imaging studies in adolescent and adult BD patients.

METHODS

Eleven studies with 329 BD patients and 277 controls met our PICOS criteria (participants, intervention, comparison, outcome and study design): BD diagnosis based on DSM criteria, natural course of disease, comparison of grey matter changes in BD individuals over ≥1-year interval between scans.

RESULTS

The selected studies yielded heterogeneous findings, partly due to varying patient characteristics, data acquisition and statistical models. Mood episodes were associated with greater grey matter loss in frontal brain regions over time. Brain volume decreased or remained stable in adolescent patients, whereas it increased in healthy adolescents. Adult BD patients showed increased cortical thinning and brain structural decline. In particular, disease onset in adolescence was associated with amygdala volume reduction, which was not reported in adult BD.

CONCLUSIONS

The evidence collected suggests that the progression of BD impairs adolescent brain development and accelerates structural brain decline across the lifespan. Age-specific changes in amygdala volume in adolescent BD suggest that reduced amygdala volume is a correlate of early onset BD. Clarifying the role of BD in brain development across the lifespan promises a deeper understanding of the progression of BD patients through different developmental episodes.

Psychophysiological responses to psychological stress exposure and neural correlates in adults with mental disorders: a scoping review

Introduction

The dysregulation of psychophysiological responses to mental stressors is a common issue addressed in individuals with psychiatric conditions, while brain circuit abnormalities are often associated with psychiatric conditions and their manifestations. However, to our knowledge, there is no systematic overview that would comprehensively synthesize the literature on psychophysiological responses during laboratory-induced psychosocial stressor and neural correlates in people with mental disorders. Thus, we aimed to systematically review the existing research on psychophysiological response during laboratory-induced stress and its relationship with neural correlates as measured by magnetic resonance imaging techniques in mental disorders.

Methods

The systematic search was performed on PubMed/Medline, EBSCOhost/PsycArticles, Web of Science, and The Cochrane Library databases during November 2021 following the PRISMA guidelines. Risk of bias was evaluated by employing the checklists for cross-sectional and case-control studies from Joanna Briggs Institute (JBI) Reviewers Manual.

Results

Out of 353 de-duplicated publications identified, six studies were included in this review. These studies were identified as representing two research themes: (1) brain anatomy and psychophysiological response to mental stress in individuals with mental disorders, and (2) brain activity and psychophysiological response to mental stress in individuals with mental disorders.

Conclusions

Overall, the evidence from studies exploring the interplay between stress psychophysiology and neural correlates in mental disorders is limited and heterogeneous. Further studies are warranted to better understand the mechanisms of how psychophysiological stress markers interplay with neural correlates in manifestation and progression of psychiatric illnesses.

Volume of hippocampus-amygdala transition area predicts outcomes of electroconvulsive therapy in major depressive disorder: high accuracy validated in two independent cohorts

BACKGROUND

Although many previous studies reported structural plasticity of the hippocampus and amygdala induced by electroconvulsive therapy (ECT) in major depressive disorder (MDD), yet the exact roles of both areas for antidepressant effects are still controversial.

METHODS

In the current study, segmentation of amygdala and hippocampal sub-regions was used to investigate the longitudinal changes of volume, the relationship between volume and antidepressant effects, and prediction performances for ECT in MDD patients before and after ECT using two independent datasets.

RESULTS

As a result, MDD patients showed selectively and consistently increased volume in the left lateral nucleus, right accessory basal nucleus, bilateral basal nucleus, bilateral corticoamygdaloid transition (CAT), bilateral paralaminar nucleus of the amygdala, and bilateral hippocampus-amygdala transition area (HATA) after ECT in both datasets, whereas marginally significant increase of volume in bilateral granule cell molecular layer of the head of dentate gyrus, the bilateral head of cornu ammonis (CA) 4, and left head of CA 3. Correlation analyses revealed that increased volume of left HATA was significantly associated with antidepressant effects after ECT. Moreover, volumes of HATA in the MDD patients before ECT could be served as potential biomarkers to predict ECT remission with the highest accuracy of 86.95% and 82.92% in two datasets (The predictive models were trained on Dataset 2 and the sensitivity, specificity and accuracy of Dataset 2 were obtained from leave-one-out-cross-validation. Thus, they were not independent and very likely to be inflated).

CONCLUSIONS

These results not only suggested that ECT could selectively induce structural plasticity of the amygdala and hippocampal sub-regions associated with antidepressant effects of ECT in MDD patients, but also provided potential biomarkers (especially HATA) for effectively and timely interventions for ECT in clinical applications.

Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium

BACKGROUND

Neuroimaging studies on major depressive disorder (MDD) have identified an extensive range of brain structural abnormalities, but the exact neural mechanisms associated with MDD remain elusive. Most previous studies were performed with voxel- or surface-based morphometry which were univariate methods without considering spatial information across voxels/vertices.

METHODS

Brain morphology was investigated using voxel-based morphometry (VBM) and source-based morphometry (SBM) in 1082 MDD patients and 990 healthy controls (HCs) from the REST-meta-MDD Consortium. We first examined group differences in regional grey matter (GM) volumes and structural covariance networks between patients and HCs. We then compared first-episode, drug-naïve (FEDN) patients, and recurrent patients. Additionally, we assessed the effects of symptom severity and illness duration on brain alterations.

RESULTS

VBM showed decreased GM volume in various regions in MDD patients including the superior temporal cortex, anterior and middle cingulate cortex, inferior frontal cortex, and precuneus. SBM returned differences only in the prefrontal network. Comparisons between FEDN and recurrent MDD patients showed no significant differences by VBM, but SBM showed greater decreases in prefrontal, basal ganglia, visual, and cerebellar networks in the recurrent group. Moreover, depression severity was associated with volumes in the inferior frontal gyrus and precuneus, as well as the prefrontal network.

CONCLUSIONS

Simultaneous application of VBM and SBM methods revealed brain alterations in MDD patients and specified differences between recurrent and FEDN patients, which tentatively provide an effective multivariate method to identify potential neurobiological markers for depression.

Effects of 12-week escitalopram treatment on resting-state functional connectivity of large-scale brain networks in major depressive disorder

In this study, the effects of antidepressants on large-scale brain networks and the neural basis of individual differences in response were explored. A total of 41 patients with major depressive disorder (MDD) and 42 matched healthy controls (HCs) were scanned by resting-state functional magnetic resonance imaging separately at baseline and after a 12-week follow-up. The patients with MDD received escitalopram for 12 weeks. After treatment, patients were classified into those with MDD in remission [MDDr, endpoint 17-item Hamilton Depression Rating Scale (HAMD) total score ≤7] and those in nonremission (MDDnr). The human Brainnetome Atlas was used to define large-scale networks and compute within- and between-network resting-state functional connectivity (rsFC). Results showed the decreased subcortical network (SCN)-ventral attention network (VAN) connectivity at baseline increased in patients with MDD after 12-week treatment, and it was comparable with that of HCs. This change was only observed in patients with MDDr. However, the decreased within-network rsFC in SCN and default mode network (DMN) persisted in all patients with MDD, including those with MDDr and MDDnr, after treatment. The strength of SCN-VAN connectivity at baseline was significantly negatively correlated with the reduction rate of HAMD score in all patients with MDD. Thus, SCN-VAN connectivity may be an antidepressant target associated with depressive state changes and a predictor of treatment response to serotonin reuptake inhibitors. The within-network rsFC in SCN and DMN may reflect a trait-like abnormality in MDD. These findings provide further insights into the mechanism of antidepressants and their individual differences in response. The trial name is "Appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement" (URL: http://www.chictr.org.cn/showproj.aspx?proj=21377; registration number: ChiCTR-OOC-17012566).

Neuroimaging-Derived Biomarkers of the Antidepressant Effects of Ketamine

Major depressive disorder is a highly prevalent psychiatric disorder. Despite an extensive range of treatment options, about a third of patients still struggle to respond to available therapies. In the last 20 years, ketamine has gained considerable attention in the psychiatric field as a promising treatment of depression, particularly in patients who are treatment resistant or at high risk for suicide. At a subanesthetic dose, ketamine produces a rapid and pronounced reduction in depressive symptoms and suicidal ideation, and serial treatment appears to produce a greater and more sustained therapeutic response. However, the mechanism driving ketamine's antidepressant effects is not yet well understood. Biomarker discovery may advance knowledge of ketamine's antidepressant action, which could in turn translate to more personalized and effective treatment strategies. At the brain systems level, neuroimaging can be used to identify functional pathways and networks contributing to ketamine's therapeutic effects by studying how it alters brain structure, function, connectivity, and metabolism. In this review, we summarize and appraise recent work in this area, including 51 articles that use resting-state and task-based functional magnetic resonance imaging, arterial spin labeling, positron emission tomography, structural magnetic resonance imaging, diffusion magnetic resonance imaging, or magnetic resonance spectroscopy to study brain and clinical changes 24 hours or longer after ketamine treatment in populations with unipolar or bipolar depression. Though individual studies have included relatively small samples, used different methodological approaches, and reported disparate regional findings, converging evidence supports that ketamine leads to neuroplasticity in structural and functional brain networks that contribute to or are relevant to its antidepressant effects.

Deep phenotyping towards precision psychiatry of first-episode depression - the Brain Drugs-Depression cohort

BACKGROUND

Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging.

METHODS

All patients are examined before receiving a standardised treatment package for adults aged 18-65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [¹¹C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome.

DISCUSSION

The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients.

TRIAL REGISTRATION

Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).

Predicting treatment outcome in depression: an introduction into current concepts and challenges

Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.

A systematic meta-review of patient-level predictors of psychological therapy outcome in major depressive disorder

BACKGROUND

Psychological therapies are effective for treating major depressive disorder, but current clinical guidelines do not provide guidance on the personalization of treatment choice. Established predictors of psychotherapy treatment response could help inform machine learning models aimed at predicting individual patient responses to different therapy options. Here we sought to comprehensively identify known predictors.

METHODS

EMBASE, Medline, PubMed, PsycINFO were searched for systematic reviews with or without meta-analysis published until June 2020 to identify individual patient-level predictors of response to psychological treatments. 3113 abstracts were identified and 300 articles assessed. We qualitatively synthesized our findings by predictor category (sociodemographic; symptom profile; social support; personality features; affective, cognitive, and behavioural; comorbidities; neuroimaging; genetics) and treatment type. We used the AMSTAR 2 to evaluate the quality of included reviews.

RESULTS

Following screening and full-text assessment, 27 systematic reviews including 12 meta-analyses were eligible for inclusion. 74 predictors emerged for various psychological treatments, primarily cognitive behavioural therapy, interpersonal therapy, and mindfulness-based cognitive therapy.

LIMITATIONS

A paucity of studies examining predictors of psychological treatment outcome, as well as methodological heterogeneities and publication biases limit the strength of the identified predictors.

CONCLUSIONS

The synthesized predictors could be used to supplement clinical decision-making in selecting psychological therapies based on individual patient characteristics. These predictors could also be used as a priori input features for machine learning models aimed at predicting a given patient's likelihood of response to different treatment options for depression, and may contribute toward the development of patient-specific treatment recommendations in clinical guidelines.

Generalizability of treatment outcome prediction in major depressive disorder using structural MRI: A NeuroPharm study

Brain morphology has been suggested to be predictive of drug treatment outcome in major depressive disorders (MDD). The current study aims at evaluating the performance of pretreatment structural brain magnetic resonance imaging (MRI) measures in predicting the outcome of a drug treatment of MDD in a large single-site cohort, and, importantly, to assess the generalizability of these findings in an independent cohort. The random forest, boosted trees, support vector machines and elastic net classifiers were evaluated in predicting treatment response and remission following an eight week drug treatment of MDD using structural brain measures derived with FastSurfer (FreeSurfer). Models were trained and tested within a nested cross-validation framework using the NeuroPharm dataset (n = 79, treatment: escitalopram); their generalizability was assessed using an independent clinical dataset, EMBARC (n = 64, treatment: sertraline). Prediction of antidepressant treatment response in the Neuropharm cohort was statistically significant for the random forest (p = 0.048), whereas none of the models could significantly predict remission. Furthermore, none of the models trained using the entire NeuroPharm dataset could significantly predict treatment outcome in the EMBARC dataset. Although our primary findings in the NeuroPharm cohort support some, but limited value in using pretreatment structural brain MRI to predict drug treatment outcome in MDD, the models did not generalize to an independent cohort suggesting limited clinical applicability. This study emphasizes the importance of assessing model generalizability for establishing clinical utility.

Electroconvulsive therapy changes temporal dynamics of intrinsic brain activity in depressed patients

Electroconvulsive therapy (ECT) has been demonstrated to be effective in treating depressed patients. Previous neuroimaging studies have focused mainly on alterations in static brain activity and connectivity to study the effects of ECT in depressed patients. However, it remains unclear whether the temporal dynamics of brain activity are associated with mechanisms of ECT in depressed patients. We measured the dynamics of spontaneous brain activity using dynamic amplitude of low-frequency fluctuation (dALFF) in healthy controls (n = 40) and patients diagnosed with unipolar depression (UD, n = 36) or bipolar disorder (BD, n = 9) before and after ECT. Furthermore, the temporal variability of intrinsic brain activity (iBA) was quantified as the variance of dALFF across sliding window. In addition, correlation analysis was performed to investigate the relationships among dALFF, depressive symptoms, and cognitive function in depressed patients. We lack second resting-state functional magnetic resonance imaging (rs-fMRI) data for healthy controls. After ECT, patients showed decreased brain dynamics (less temporal variability) in the right dorsal anterior cingulate cortex (dACC) and the right precuneus, whereas they showed increased brain dynamics in the bilateral superior medial frontal cortex (mSFC). No significant correlation was found between the dALFF and clinical variables in depressed patients. Our findings suggest that right dACC, right precuneus, and bilateral mSFC play an important role in response to ECT depressed patients from the perspective of dynamic local brain activity, indicating that the dALFF variability may be useful in further understanding the mechanisms of ECT's antidepressant effects.

Structural MRI-Based Measures of Accelerated Brain Aging do not Moderate the Acute Antidepressant Response in Late-Life Depression

OBJECTIVE

Late-life depression (LLD) is characterized by accelerated biological aging. Accelerated brain aging, estimated from structural magnetic resonance imaging (sMRI) data by a machine learning algorithm, is associated with LLD diagnosis, poorer cognitive performance, and disability. We hypothesized that accelerated brain aging moderates the antidepressant response.

DESIGN AND INTERVENTIONS

Following MRI, participants entered an 8-week randomized, controlled trial of escitalopram. Nonremitting participants then entered an open-label 8-week trial of bupropion.

PARTICIPANTS

Ninety-five individuals with LLD.

MEASUREMENTS

A machine learning algorithm estimated each participant's brain age from sMRI data. This was used to calculate the brain-age gap (BAG), or how estimated age differed from chronological age. Secondary sMRI measures of aging pathology included white matter hyperintensity (WMH) volumes and hippocampal volumes. Mixed models examined the relationship between sMRI measures and change in depression severity. Initial analyses tested for a moderating effect of MRI measures on change in depression severity with escitalopram. Subsequent analyses tested for the effect of MRI measures on change in depression severity over time across trials.

RESULTS

In the blinded initial phase, BAG was not significantly associated with a differential response to escitalopram over time. BAG was also not associated with a change in depression severity over time across both arms in the blinded phase or in the subsequent open-label bupropion phase. We similarly did not observe effects of WMH volume or hippocampal volume on change in depression severity over time.

CONCLUSION

sMRI markers of accelerated brain aging were not associated with treatment response in this sequential antidepressant trial.

Cortical thickness and related depressive symptoms in early abstinence from chronic methamphetamine use

Methamphetamine use is surging globally as a cause of morbidity and mortality. Treatment is typically sought in early abstinence, when craving and depressive symptoms are intense, contributing to relapse and poor outcomes. To advance an understanding of this problem and identify therapeutic targets, we conducted a retrospective analysis of brain structure in 89 adults with Methamphetamine Use Disorder who were in early abstinence and 89 healthy controls. Unlike most prior research, the participants did not significantly differ in age, sex and recent use of alcohol and tobacco (p-values ≥ 0.400). We analysed thickness across the entire cerebral cortex by fitting a general linear model to identify differences between groups. Follow-up regressions were performed to determine whether cortical thickness in regions showing group differences was related to craving, measured on a visual analogue scale, or to the Beck Depression Inventory score. Participants in early methamphetamine abstinence (M ± SD = 22.1 ± 25.6 days) exhibited thinner cortex in clusters within bilateral frontal, parietal, temporal, insular, and right cingulate cortices relative to controls (p-values < 0.001, corrected for multiple comparisons). Unlike craving (β = 0.007, p = 0.947), depressive symptoms were positively correlated with cortical thickness across clusters (β = 0.239, p = 0.030) and with thickness in the anterior cingulate cluster (β = 0.246, p = 0.027) in the methamphetamine-dependent group. Inasmuch as anterior cingulate pathology predicts response to antidepressants for Major Depressive Disorder, cingulate structure may also identify patients with Methamphetamine Use Disorder who can benefit from antidepressant medication.

Hippocampal volume changes in a pharmacological sex-hormone manipulation risk model for depression in women

Hormone transition phases may trigger depression in some women, yet the underlying mechanisms remain elusive. In a pharmacological sex-hormone manipulation model, we previously reported that estradiol reductions, induced with a gonadotropin-releasing hormone agonist (GnRHa), provoked subclinical depressive symptoms in healthy women, especially if neocortical serotonin transporter (SERT) binding also increased. Within this model, we here evaluated if GnRHa, compared to placebo, reduced hippocampal volume, in a manner that depended on the magnitude of the estradiol decrease and SERT binding, and if this decrease translated to the emergence of subclinical depressive symptoms. Sixty-three healthy, naturally cycling women were included in a randomized, double-blind, placebo-controlled GnRHa-intervention study. We quantified the change from baseline to follow-up (n = 60) in serum estradiol (ΔEstradiol), neocortical SERT binding ([¹¹C] DASB positron emission tomography; ΔSERT), subclinical depressive symptoms (Hamilton depression rating scale; ΔHAMD-17), and hippocampal volume (magnetic resonance imaging data analyzed in Freesurfer 7.1, ΔHippocampus). Group differences in ΔHippocampus were evaluated in a t-test. Within the GnRHa group, associations between ΔEstradiol, ΔHippocampus, and ΔHAMD-17, in addition to ΔSERT-by-ΔEstradiol interaction effects on ΔHippocampus, were evaluated with linear regression models. Mean ΔHippocampus was not significantly different between the GnRHa and placebo group. Within the GnRHa group, hippocampal volume reductions were associated with the magnitude of estradiol decrease (p = 0.04, Cohen's f² = 0.18), controlled for baseline SERT binding, but not subclinical depressive symptoms. There was no ΔSERT-by-ΔEstradiol interaction effects on ΔHippocampus. If replicated, our data highlight a possible association between estradiol fluctuations and hippocampal plasticity, adjusted for serotonergic contributions.

Depression screening using a non-verbal self-association task: A machine-learning based pilot study

BACKGROUND

Effective screening is important to combat the raising burden of depression and opens a critical time window for early intervention. Clinical use of non-verbal depression screening is nascent, yet a promising and viable candidate to supplement verbal screening. Differential self- and emotion-processing in depression patients were previously reported by non-verbal behavioural assessments, corroborated by neuroimaging findings of distinct neuroanatomical markers. Thus non-verbal validated brain-behaviour based self-emotion-related assessment data reflect physiological differences and may support individual level screening of depression.

METHODS

In this pilot study (n = 84) we collected two longitudinal sessions of behavioural assessment data in a laboratory setting. Depression was assessed using Beck Depression Inventory II (BDI-II), to explore optimal screening methods with machine-learning, and to establish the validity of adapting a novel behavioural assessment focusing on self and emotions for depression screening.

RESULTS

The best machine-learning model achieved high performance in depression screening, 10-Fold cross-validation (CV) Area Under the receiver operating characteristic Curve (AUC) of 0.90 and balanced accuracy of 0.81, using a Gradient Boosting algorithm. Prospective prediction using a model trained with session 1 data to predict session 2 depression status achieved a 10-Fold CV AUC of 0.77 and balanced accuracy of 0.66. We also identified interpretable behavioural signatures for depression patients based on the best model.

CONCLUSION

The study supports the utility of using behavioural data as a viable and cost-effective solution for depression screening, with a potential wide range of applications in clinical settings.

Parsing the Network Mechanisms of Electroconvulsive Therapy

Electroconvulsive therapy (ECT) is one of the oldest and most effective forms of neurostimulation, wherein electrical current is used to elicit brief, generalized seizures under general anesthesia. When electrodes are positioned to target frontotemporal cortex, ECT is arguably the most effective treatment for severe major depression, with response rates and times superior to other available antidepressant therapies. Neuroimaging research has been pivotal in improving the field's mechanistic understanding of ECT, with a growing number of magnetic resonance imaging studies demonstrating hippocampal plasticity after ECT, in line with evidence of upregulated neurotrophic processes in the hippocampus in animal models. However, the precise roles of the hippocampus and other brain regions in antidepressant response to ECT remain unclear. Seizure physiology may also play a role in antidepressant response to ECT, as indicated by early positron emission tomography, single-photon emission computed tomography, and electroencephalography research and corroborated by recent magnetic resonance imaging studies. In this review, we discuss the evidence supporting neuroplasticity in the hippocampus and other brain regions during and after ECT, and their associations with antidepressant response. We also offer a mechanistic, circuit-level model that proposes that core mechanisms of antidepressant response to ECT involve thalamocortical and cerebellar networks that are active during seizure generalization and termination over repeated ECT sessions, and their interactions with corticolimbic circuits that are dysfunctional prior to treatment and targeted with the electrical stimulus.

Neural correlates of affective control regions induced by common therapeutic strategies in major depressive disorders: An activation likelihood estimation meta-analysis study

In major depressive disorder (MDD), not only the pathophysiology of this disease is unknown but also the mechanisms of clinical efficacy across its therapeutic strategies are unclear. Although neuroimaging studies adopted activation likelihood estimation (ALE) approach to identify the convergent abnormalities of human brain in the MDD patients, the common alterations after antidepressant therapies were not summarized. Thus, we extracted the coordinates of brain regions in the MDD patients that showed differences in resting-state function, gray matter morphometry, and task-evoked neuronal responses after therapies. The ALE algorithm (GingerALE2.0.3) was employed in all 53 studies (64 experiments with 1406 MDD patients). Consistent results across treatment therapies were reported in the affective control network, including the bilateral thalamus, bilateral amygdala/parahippocampal gyrus, right anterior cingulate cortex/middle frontal gyrus, and right insular cortex/claustrum. Only electroconvulsive therapy partially replicated above findings. Our results indicate the antidepressant therapies efficiently influence core structures of the affective control network, which might be the underlying mechanism of remission in depression and provides potential targets for further treatment strategies.

Electroconvulsive Therapy for Neuropsychiatric Symptoms due to Major Neurocognitive Disorder: A Prospective, Observational Study

OBJECTIVES

Neuropsychiatric symptoms (NPSs) in those with major neurocognitive disorder (MNCD) include the responsive behaviors of agitation and aggression. Electroconvulsive therapy (ECT) has shown some effectiveness based on retrospective studies and one open label prospective study. We hypothesized that ECT will reduce NPSs between baseline and after treatment in those with medication-refractory behaviors.

METHOD/DESIGN

This Canadian prospective multicenter study included MNCD patients admitted to geriatric psychiatry units for the management of refractory NPSs. All treatment-refractory participants suffered from advanced MNCD. We conducted the Neuropsychiatric Inventory-Clinician version and the Pittsburgh Agitation Scale at baseline, and during and after the ECT course. A bitemporal or bifrontal ECT series based on dose titration to 1.5 to 2.5 times seizure threshold was administered.

RESULTS

Data were collected for 33 patients with a mean age of 73 and categorized with severe MNCD using the Functional Assessment Staging of Alzheimer's Disease scale (stages 6 and 7). The data showed a drop in mean Neuropsychiatric Inventory-Clinician version from 58.36 to 24.58 (P < 0.0001). Mean Neuropsychiatric Inventory agitation subscale dropped from 7.12 to 3.09 (P = 0.007). Mean Neuropsychiatric Inventory aggression subscale dropped from 6.94 to 0.97 (P < 0.0001). There was a concomitant significant decline in Pittsburgh Agitation Scale scores. No participants dropped out because of intolerance of ECT. One participant died from pneumonia, which did not appear related to ECT.

CONCLUSIONS

In this naturalistic study, ECT was found to be a safe and effective treatment for certain NPSs in people with MNCD. This can translate into improving quality of life.

Non-invasive brain stimulation combined with psychosocial intervention for depression: a systematic review and meta-analysis

OBJECTIVES

This review investigates the efficacy and safety of non-invasive brain stimulation (NIBS) combined with psychosocial intervention on depressive symptoms.

MATERIALS AND METHODS

We systematically searched five electronic databases from their inception to June 2021: PubMed, Embase, PsycINFO, Web of Science, and Medline. Randomized or non-randomized clinical trials in which NIBS plus psychosocial intervention was compared to control conditions in people with depressive symptoms were included.

RESULTS

A total of 17 eligible studies with 660 participants were included. The meta-analysis results showed that NIBS combined with psychosocial therapy had a positive effect on moderate to severe depression ([SMD = - 0.46, 95%CI (- 0.90, - 0.02), I2 = 73%, p < .01]), but did not significantly improve minimal to mild depression ([SMD = - 0.12, 95%CI (- 0.42, 0.18), I2 = 0%, p = .63]). Compared with NIBS alone, the combination treatment had a significantly greater effect in alleviating depressive symptoms ([SMD = - 0.84, 95%CI (- 1.25, - 0.42), I2 = 0%, p = .93]). However, our results suggested that the pooled effect size of ameliorating depression of NIBS plus psychosocial intervention had no significant difference compared with the combination of sham NIBS [SMD = - 0.12, 95%CI (- 0.31, 0.07), I2 = 0%, p = .60] and psychosocial intervention alone [SMD = - 0.97, 95%CI (- 2.32, 0.38), I2 = 72%, p = .01].

CONCLUSION

NIBS when combined with psychosocial intervention has a significant positive effect in alleviating moderately to severely depressive symptoms. Further well-designed studies of NIBS combined with psychosocial intervention on depression should be carried out to consolidate the conclusions and explore the in-depth underlying mechanism.