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

Functional connectivity of the default mode network in first-episode drug-naïve patients with major depressive disorder

BACKGROUND

Alterations in the default mode network (DMN) have been reported in major depressive disorder (MDD), well-replicated robust alterations of functional connectivity (FC) of DMN remain to be established. Investigating the functional connections of DMN at the overall and subsystem level in early MDD patients has the potential to advance our understanding of the physiopathology of this disorder.

METHODS

We recruited 115 first-episode drug-naïve patients with MDD and 137 demographic-matched healthy controls (HCs). We first compared FC within the DMN, within/between the DMN subsystems, and from DMN subsystems to the whole brain between groups. Subsequently, we explored correlations between clinical features and identified alterations in FC.

RESULTS

First-episode drug-naïve patients with MDD showed significantly increased FC within the DMN, dorsal DMN and medial DMN. Each subsystem showed a distinct FC pattern with other brain networks. Increased FC between the subsystems (core DMN, dorsal DMN) and other networks was associated with more severe depressive symptoms, while medial DMN-related connectivity correlated with memory performance.

LIMITATIONS

The relatively large "pure" MDD sample could only be generalized to a limited population. And, atypical asymmetric FCs in the DMN related to MDD might be missed for only left-lateralized ROIs were used to avoid strong correlations between mirrored (right/left) seed regions.

CONCLUSION

These findings suggest patients with early MDD showed distinct patterns of FC alterations throughout DMN and its subsystems, which were related to illness severity and illness-associated cognitive impairment, highlighting their clinical significance.

Neural signatures of default mode network subsystems in first-episode, drug-naive patients with major depressive disorder after 6-week thought induction psychotherapy treatment

Evidence indicates that the default mode network (DMN) plays a crucial role in the neuropathology of major depressive disorder (MDD). However, the neural signatures of DMN subsystems in MDD after low resistance Thought Induction Psychotherapy (TIP) remain incompletely understood. We collected functional magnetic resonance imaging data from 20 first-episode, drug-naive MDD and 20 healthy controls (HCs). The DMN was segmented into three subsystems and seed-based functional connectivity (FC) was computed. After 6-week treatment, the significantly reduced FCs with the medial temporal lobe memory subsystem in MDD at baseline were enhanced and were comparable to that in HCs. Changed Hamilton Depression Rating Scale scores were significantly related with changed FC between the posterior cingulate cortex (PCC) and the right precuneus (PCUN). Further, changed serotonin 5-hydroxytryptamine levels were significantly correlated with changed FCs between the PCC and the left PCUN, between the posterior inferior parietal lobule and the left inferior temporal gyrus, and between the retrosplenial cortex and the right inferior frontal gyrus, opercular part. Finally, the support vector machine obtained an accuracy of 67.5% to distinguish between MDD at baseline and HCs. These findings may deepen our understanding of the neural basis of the effects of TIP on DMN subsystems in MDD.

Exploring the mediating role of the ventral attention network and somatosensory motor network in the association between childhood trauma and depressive symptoms in major depressive disorders

BACKGROUND

Childhood trauma is closely tied to adult depression, but the neurobiological mechanisms remain unclear. Previous studies suggested associations between depression and large-scale brain networks such as the Ventral Attention Network (VAN) and Somatosensory Motor Network (SMN). This study hypothesized that functional connectivity (FC) within and between these networks mediates the link between childhood trauma and adult depression.

METHODS

The Childhood Trauma Questionnaire (CTQ) assessed developmental experiences, and the Hamilton Rating Scale for Depression (HAMD-17) gauged depressive symptoms. Resting-state functional magnetic resonance imaging (fMRI) analyzed FC within and between the VAN and SMN.

RESULTS

Depression group exhibited significantly higher HAMD and CTQ scores, as well as elevated FC within the VAN and between the VAN and SMN (P < 0.05). Positive correlations were found between HAMD total score and FC within the VAN (P < 0.05, r = 0.35) and between the VAN and SMN (P < 0.05, r = 0.34), as well as with CTQ total score (P < 0.05, r = 0.27). Positive correlations were also observed between CTQ total score and FC within the VAN (P < 0.05, r = 0.31) and between the VAN and SMN (P < 0.05, r = 0.29). In the mediation model, FC within and between the VAN and SMN significantly mediated childhood trauma and depression.

LIMITATIONS

The cross-sectional design limits causal inference. The sample size for different trauma types is relatively small, urging caution in generalizing findings.

CONCLUSIONS

The study underscores the association between depression severity, VAN dysfunction, abnormal VAN-SMN FC, and childhood trauma. These findings contribute to understanding the neurobiological mechanisms underlying childhood trauma and depression.

Altered static and dynamic functional network connectivity in individuals with subthreshold depression: a large-scale resting-state fMRI study

Dynamic functional network connectivity (dFNC) is an expansion of static FNC (sFNC) that reflects connectivity variations among brain networks. This study aimed to investigate changes in sFNC and dFNC strength and temporal properties in individuals with subthreshold depression (StD). Forty-two individuals with subthreshold depression and 38 healthy controls (HCs) were included in this study. Group independent component analysis (GICA) was used to determine target resting-state networks, namely, executive control network (ECN), default mode network (DMN), sensorimotor network (SMN) and dorsal attentional network (DAN). Sliding window and k-means clustering analyses were used to identify dFNC patterns and temporal properties in each subject. We compared sFNC and dFNC differences between the StD and HCs groups. Relationships between changes in FNC strength, temporal properties, and neurophysiological score were evaluated by Spearman's correlation analysis. The sFNC analysis revealed decreased FNC strength in StD individuals, including the DMN-CEN, DMN-SMN, SMN-CEN, and SMN-DAN. In the dFNC analysis, 4 reoccurring FNC patterns were identified. Compared to HCs, individuals with StD had increased mean dwell time and fraction time in a weakly connected state (state 4), which is associated with self-focused thinking status. In addition, the StD group demonstrated decreased dFNC strength between the DMN-DAN in state 2. sFNC strength (DMN-ECN) and temporal properties were correlated with HAMD-17 score in StD individuals (all p < 0.01). Our study provides new evidence on aberrant time-varying brain activity and large-scale network interaction disruptions in StD individuals, which may provide novel insight to better understand the underlying neuropathological mechanisms.

Neuroimaging for precision medicine in psychiatry

Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. We lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation.

Shared and unique alterations of large-scale network connectivity in drug-free adolescent-onset and adult-onset major depressive disorder

Differences in clinical manifestations and biological underpinnings between Major Depressive Disorder (MDD) onset during adolescence and adulthood have been posited in previous studies, implying an influential role of age of onset (AOO) in the clinical subtyping and therapeutic approaches to MDD. However, direct comparisons between the two cohorts and their age-matched controls have been lacking in extant investigations. In this investigation, 156 volunteers participated, comprising 46 adolescents with MDD (adolescent-onset group), 35 adults with MDD (adult-onset group), 19 healthy adolescents, and 56 healthy adults. Resting-state functional MRI scans were undergone by all participants. Large-scale network analyses were applied. Subsequently, a 2 × 2 ANOVA was employed to analyze the main effects of diagnosis, age, and their interaction effect on functional connectivity (FC). Furthermore, regression analysis was employed to scrutinize the association between anomalous FC and HAMD sub-scores. Increased FC in visual network (VN), limbic network (LN), VN-dorsal attention network (DAN), VN-LN, and LN-Default Mode (DMN) was found in both adolescent-onset and adult-onset MDD; however, the increased FC in DAN and LN were only found in adult-onset MDD and the decreased FC in DAN was only found in adolescent-onset MDD. Additionally, the relationship between HAMD factor 1 anxiety somatization and altered FC of DAN, VN, and VN-DAN was moderated by AOO. In conclusion, shared and distinctive large-scale network alterations in adolescent-onset and adult-onset MDD patients were suggested by our findings, providing valuable contributions towards refining clinical subtyping and treatment approaches for MDD.

Brain Circuit-Derived Biotypes for Treatment Selection in Mood Disorders: A Critical Review and Illustration of a Functional Neuroimaging Tool for Clinical Translation

Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry. We begin by summarizing the current landscape of how functional neuroimaging-derived circuit predictors can forecast treatment outcomes in depression. Then, we outline the opportunities and challenges in integrating circuit predictors into clinical practice. We highlight one standardized and reproducible approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation. We conclude by evaluating the prospects and practicality of employing neuroimaging tools, such as the one that we propose, in routine clinical practice.

Compressed primary-to-transmodal gradient is accompanied with subcortical alterations and linked to neurotransmitters and cellular signatures in major depressive disorder

Major depressive disorder (MDD) has been shown to involve widespread changes in low-level sensorimotor and higher-level cognitive functions. Recent research found that a primary-to-transmodal gradient could capture a cortical hierarchical organization ranging from perception and action to cognition in healthy subjects, but a prominent gradient dysfunction in MDD patients. However, whether and how this cortical gradient is linked to subcortical impairments and whether it is reflected in the microscale neurotransmitter systems and cell type-specific transcriptional signatures remain largely unknown. Data were acquired from 323 MDD patients and 328 sex- and age-matched healthy controls derived from the REST-meta-MDD project, and the human brain neurotransmitter systems density maps and gene expression data were drawn from two publicly available datasets. We investigated alterations of the primary-to-transmodal gradient in MDD patients and their correlations with clinical symptoms of depression and anxiety, as well as  their paralleled subcortical impairments. The correlations between MDD-related gradient alterations and densities of the neurotransmitter systems and gene expression information were assessed, respectively. The results demonstrated that MDD patients had a compressed primary-to-transmodal gradient accompanied by paralleled alterations in subcortical regions including the caudate, amygdala, and thalamus. The case-control gradient differences were spatially correlated with the densities of the neurotransmitter systems including the serotonin and dopamine receptors, and meanwhile with gene expression enriched in astrocytes, excitatory and inhibitory neuronal cells. These findings mapped the paralleled subcortical impairments in cortical hierarchical organization and also helped us understand the possible molecular and cellular substrates of the co-occurrence of high-level cognitive impairments with low-level sensorimotor abnormalities in MDD.

Lower Dorsal Lateral Prefrontal Cortex Functional Connectivity in Late-Life Depression With Suicidal Ideation

OBJECTIVE

The dorsal lateral prefrontal cortex (DLPFC) has been identified as a neuromodulation target for alleviating suicidal ideation. Dysfunctional DLPFC has been implicated in suicidality in depression. This study aimed to investigate the functional connectivity (FC) of the DLPFC in late-life depression (LLD) with suicidal ideation.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) data from 32 LLD patients with suicidal ideation (LLD-S), 41 LLD patients without suicidal ideation (LLD-NS), and 54 healthy older adults (HOA) were analyzed using DLPFC seed-based FC analyses. Group differences in FC were examined, and machine learning was applied to explore the potential of DLPFC-FC for classifying LLD-S from LLD-NS.

RESULTS

Abnormal DLPFC-FC patterns were observed in LLD-S, characterized by lower connectivity with the angular gyrus, precuneus, and superior frontal gyrus compared to LLD-NS and healthy controls. A classification model based on the identified DLPFC-FC achieved an accuracy of 75%.

CONCLUSION

The lower FC of DLPFC networks may contribute to the neurobiological mechanism of suicidal ideation in late-life depression. These findings may facilitate suicide prevention for LLD by providing potential neuroimaging markers and network-based neuromodulation targets. However, further confirmation with larger sample sizes and experimental designs is warranted.

Measures of Connectivity and Dorsolateral Prefrontal Cortex Volumes and Depressive Symptoms Following Treatment With Selective Serotonin Reuptake Inhibitors in Adolescents

Importance

Selective serotonin reuptake inhibitors (SSRIs) are considered a first-line pharmacological treatment for adolescent depression with moderate or higher levels of symptom severity. Thus, it is important to understand neurobiological changes related to SSRIs during the course of treatment for adolescents with depression.

Objective

To examine neurobiological changes associated with SSRI treatment in adolescents with major depressive disorder (MDD) by measuring longitudinal changes in volume and resting-state functional connectivity (rsFC) in the dorsolateral prefrontal cortex (DLPFC), a core region of cognitive control.

Design, Setting, and Participants

This cohort study was conducted with an open-label design. Adolescents with MDD and healthy controls were recruited at the Seoul National University Hospital (Seoul, South Korea). Adolescents with MDD were treated with escitalopram for 8 weeks. Data analysis was conducted between April 2021 and February 2022.

Main Outcomes and Measures

Depressive symptoms were assessed using the Children's Depression Rating Scale-Revised. The outcome measure was defined as the change in Children's Depression Rating Scale-Revised scores from week 0 (before treatment) to week 8 (after treatment) or upon termination. Participants completed structural and resting-state functional magnetic resonance imaging (rsfMRI) assessments before (week 0) and after (week 8) SSRI treatment. Repeated measures analysis of variance and liner mixed model analyses were used to examine the longitudinal associations of SSRI treatment with DLPFC volume and rsFC between responders who showed at least a 40% decrease in depressive symptoms and nonresponders who did not.

Results

Ninety-five adolescents with MDD and 57 healthy controls were initially recruited. The final analyses of volume included 36 responders (mean [SD] age, 15.0 [1.6] years; 25 girls [69.4%]) and 26 nonresponders (mean [SD] age, 15.3 [1.5] years; 19 girls [73.1%]). Analyses of rsFC included 33 responders (mean [SD] age, 15.2 [1.5] years; 21 girls [63.6%]) and 26 nonresponders (mean [SD] age, 15.3 [1.5] years; 19 girls [73.1%]). The longitudinal associations of SSRI treatment were more evident in responders than in nonresponders. Responders showed significantly increased right DLPFC volume, decreased bilateral DLPFC rsFC with the superior frontal gyri, and decreased left DLPFC rsFC with the ventromedial PFC after treatment compared with before treatment. Furthermore, increased right DLPFC volume was correlated with decreased rsFC between the right DLPFC and superior frontal gyri after SSRI treatment.

Conclusions and Relevance

The preliminary results of this cohort study suggest that the DLPFC volumetric and rsFC changes may serve as potential neurobiological treatment markers that are associated with symptom improvement in adolescents with MDD.

Brain connectivity in major depressive disorder: a precision component of treatment modalities?

Major depressive disorder (MDD) is a very prevalent mental disorder that imposes an enormous burden on individuals, society, and health care systems. Most patients benefit from commonly used treatment methods such as pharmacotherapy, psychotherapy, electroconvulsive therapy (ECT), and repetitive transcranial magnetic stimulation (rTMS). However, the clinical decision on which treatment method to use remains generally informed and the individual clinical response is difficult to predict. Most likely, a combination of neural variability and heterogeneity in MDD still impedes a full understanding of the disorder, as well as influences treatment success in many cases. With the help of neuroimaging methods like functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), the brain can be understood as a modular set of functional and structural networks. In recent years, many studies have investigated baseline connectivity biomarkers of treatment response and the connectivity changes after successful treatment. Here, we systematically review the literature and summarize findings from longitudinal interventional studies investigating the functional and structural connectivity in MDD. By compiling and discussing these findings, we recommend the scientific and clinical community to deepen the systematization of findings to pave the way for future systems neuroscience roadmaps that include brain connectivity parameters as a possible precision component of the clinical evaluation and therapeutic decision.

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).

Childhood sexual abuse related to brain activity abnormalities in right inferior temporal gyrus among major depressive disorder

Regional brain activity abnormalities have been reported in major depressive disorder (MDD) with childhood trauma (CT). However, these findings were variable and equivocal. The present study aims to explore further the correlation between CT and the amplitude of low-frequency fluctuations (ALFF) alteration in MDD. In this study, we recruited 60 healthy controls (HCs) and 66 MDD patients to complete resting-state fMRI scans. All MDD patients were evaluated clinical symptoms and childhood trauma experience using the Hamilton Depression Rating Scale-17 (HDRS-17), the Hamilton Anxiety Scale (HAMA), and the Childhood Trauma Questionnaire (CTQ). Compared to HCs, MDD patients demonstrated significantly altered ALFF in the right middle occipital gyrus (MOG), bilateral inferior temporal gyrus (ITG), bilateral cerebellum posterior lobe, bilateral anterior cingulate gyrus (ACC), and bilateral superior frontal gyrus (SFG). More importantly, we found negative correlation between childhood sexual abuse (CSA) scores and ALFF value appeared mainly in the right ITG among MDD patients. After adjusting for covariates (age, gender, mean framewise displacement (FD), HRDS-17, and HAMA scores), this correlation remained significant. Meanwhile, ALFF in the right ITG could distinguish MDD patients with or without CSA (area under the curve (AUC) = 0.713). Our findings revealed that the regional brain activity abnormalities in the right ITG might be a potential biomarker for MDD patients with CSA.

Functional connectivity of the default mode network subsystems in patients with major depressive episodes with mixed features

Background

The neuroimaging mechanism of major depressive episodes with mixed features (MMF) is not clear.

Aims

This study aimed to investigate the functional connectivity of the default mode network (DMN) subsystems among patients with MMF and patients with major depressive disorder without mixed features (MDD_noMF).

Methods

This study recruited 47 patients with MDD_noMF and 27 patients with MMF from Beijing Anding Hospital, Capital Medical University, between April 2021 and June 2022. Forty-five healthy controls (HCs) were recruited. All subjects underwent resting-state functional magnetic resonance imaging scanning and clinical assessments. Intranetwork and internetwork functional connectivity were computed in the DMN core subsystem, dorsal medial prefrontal cortex (dMPFC) subsystem and medial temporal lobe (MTL) subsystem. Analysis of covariance method was performed to compare the intranetwork and internetwork functional connectivity in the DMN subsystems among the MDD_noMF, MMF and HC groups.

Results

The functional connectivity within the DMN core (F=6.32, p_FDR=0.008) and MTL subsystems (F=4.45, p_FDR=0.021) showed significant differences among the MDD_noMF, MMF and HC groups. Compared with the HC group, the patients with MDD_noMF and MMF had increased functional connectivity within the DMN MTL subsystem, and the patients with MMF also showed increased functional connectivity within the DMN core subsystem. Meanwhile, compared with the MDD_noMF, the patients with MMF had increased functional connectivity within the DMN core subsystem (mean difference (MDD_noMF-MMF)=-0.08, SE=0.04, p=0.048). However, no significant differences were found within the DMN dMPFC subsystem and all the internetwork functional connectivity.

Conclusions

Our results indicated abnormal functional connectivity patterns of DMN subsystems in patients with MMF, findings potentially beneficial to deepen our understanding of MMF's neural basis.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Prediction of remission among patients with a major depressive disorder based on the resting-state functional connectivity of emotion regulation networks

The prediction of antidepressant response is critical for psychiatrists to select the initial antidepressant drug for patients with major depressive disorders (MDD). The implicated brain networks supporting emotion regulation (ER) are critical in the pathophysiology of MDD and the prediction of antidepressant response. Therefore, the primary aim of the current study was to identify the neuroimaging biomarkers for the prediction of remission in patients with MDD based on the resting-state functional connectivity (rsFC) of the ER networks. A total of 81 unmedicated adult MDD patients were investigated and they underwent resting-state functional magnetic resonance imagining (fMRI) scans. The patients were treated with escitalopram for 12 weeks. The 17-item Hamilton depression rating scale was used for assessing remission. The 36 seed regions from predefined ER networks were selected and the rsFC matrix was caculated for each participant. The support vector machine algorithm was employed to construct prediction model, which separated the patients with remission from those with non-remission. And leave-one-out cross-validation and the area under the curve (AUC) of the receiver operating characteristic were used for evaluating the performance of the model. The accuracy of the prediction model was 82.08% (sensitivity = 71.43%, specificity = 89.74%, AUC = 0.86). The rsFC between the left medial superior frontal gyrus and the right inferior frontal gyrus as well as the precuneus were the features with the highest discrimination ability in predicting remission from escitalopram among the MDD patients. Results from our study demonstrated that rsFC of the ER brain networks are potential predictors for the response of antidepressant drugs. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377 . Registration number: ChiCTR-OOC-17012566.

Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients with Major Depressive Disorder

Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD.

MRI predictors of pharmacotherapy response in major depressive disorder

Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.