Dysfunction of the cingulo-opercular network in first-episode medication-naive patients with major depressive disorder

Using dynamic graph convolutional network to identify individuals with major depression disorder

Objective and quantitative neuroimaging biomarkers are crucial for early diagnosis of major depressive disorder (MDD). However, previous studies using machine learning (ML) to distinguish MDD have often used small sample sizes and overlooked MDD's neural connectome and mechanism. To address these gaps, we applied Dynamic Graph Convolutional Nets (DGCNs) to a large multi-site dataset of 2317 resting state functional MRI (RS-fMRI) scans from 1081 MDD patients and 1236 healthy controls from 16 Rest-meta-MDD consortium sites. Our DGCN model combined with the personal whole brain functional connectivity (FC) network achieved an accuracy of 82.5 % (95 % CI:81.6-83.4 %, AUC:0.869), outperforming other universal ML classifiers. The most prominent domains for classification were mainly in the default mode network, fronto-parietal and cingulo-opercular network. Our study supports the stability and efficacy of using DGCN to characterize MDD and demonstrates its potential in enhancing neurobiological comprehension of MDD by detecting clinically related disorders in FC network topologies.

Serum KNG and FVIII may serve as potential biomarkers for depression

BACKGROUND

The global burden of major depressive disorder (MDD) is rising, with current diagnostic methods hindered by significant subjectivity and low inter-rater reliability. Several studies have implied underlying link between coagulation-related proteins, such as kininogen (KNG) and coagulation factor VIII (FVIII), and depressive symptoms, offering new insights into the exploration of depression biomarkers. This study aims to elucidate the roles of KNG and FVIII in depression, potentially providing a foundational basis for biomarker research in this field.

METHODS

A three-part experiment was conducted: (1) we measured serum levels of KNG and FVIII in the chronic unpredictable mild stress (CUMS) model; (2) KNG adeno-associated-virus overexpression (KNG-AAV-OE) model was constructed to further investigate the roles of KNG and FVIII. Meanwhile, quantity PCR, western blotting and immunofluorescence staining detected the KNG-FVIII pathway. (3) Peripheral blood samples were gathered from healthy control (HC, N = 21), as well as first-episode drug-naive patients with MDD (FEDN-MDD, N = 21), to further confirm the association between KNG, FVIII and depression.

RESULTS

Firstly, serum KNG and FVIII levels were significantly elevated in the CUMS model. Then, the rats exhibited pronounced depressive-like behaviors in the KNG-AAV-OE model, with corresponding increases in serum KNG and FVIII. Lastly, clinical data showed increased KNG and FVIII levels in FEDN-MDD compared to HC. Furthermore, KNG and FVIII levels exhibited a strong positive correlation with the scores of the 24-item Hamilton Depression Scale and the 14-item Hamilton Anxiety Scale.

CONCLUSION

To sum up, this study highlights critical roles of serum KNG and FVIII in depression and the KNG-AAV-OE may lead the augment of FVIII in serum. Consequently, our research may offer new evidence and foundation for depression biomarkers research in the future.

Common and distinct neural underpinnings of the association between childhood maltreatment and depression and aggressive behavior

BACKGROUND

Although childhood maltreatment (CM) is widely recognized as a transdiagnostic risk factor for various internalizing and externalizing psychological disorders, the neural basis underlying this association remain unclear. The potential reasons for the inconsistent findings may be attributed to the involvement of both common and specific neural pathways that mediate the influence of childhood maltreatment on the emergence of psychopathological conditions.

METHODS

This study aimed to delineate both the common and distinct neural pathways linking childhood maltreatment to depression and aggression. First, we employed Network-Based Statistics (NBS) on resting-state functional magnetic resonance imaging (fMRI) data to identify functional connectivity (FC) patterns associated with depression and aggression. Mediation analyses were then conducted to assess the role of these FC patterns in the relationship between childhood maltreatment and each outcome.

RESULTS

The results demonstrated that FC within the default mode network (DMN) and between the cingulo-opercular network (CON) and dorsal attention network (DAN) mediated the association between childhood maltreatment and aggression, whereas FC within the reward system and between the CON and the reward system mediated the link between childhood maltreatment and depression.

CONCLUSIONS

We speculate that the control system may serve as a transdiagnostic neural basis accounting for the sequela of childhood maltreatment, and the attention network and the reward network may act as specific neural basis linking childhood maltreatment to depression and aggression, respectively.

Exploring common and distinct neural basis of procrastination and impulsivity through elastic net regression

Prior work highlighted that procrastination and impulsivity shared a common neuroanatomical basis in the dorsolateral prefrontal cortex, implying a tight relationship between these traits. However, theorists hold that procrastination is motivated by avoiding aversiveness, while impulsivity is driven by approaching immediate pleasure. Hence, exploring the common and distinct neural basis underlying procrastination and impulsivity through functional neuroimaging becomes imperative. To address this, we employed elastic net regression to examine the links between whole-brain resting-state functional connectivity and these traits in 822 university students from China. Results showed that the functional connections between the default network and the visual network were positively associated with both traits, indicating that the dysfunction of higher-order cognition (eg self-control) may account for their tight relationship. A distinct neural basis was also identified: Procrastination was negatively associated with functional connections between the frontal-parietal network and the ventral-attention network and between the cingular-opercular network and the subcortical network. In contrast, connections between the default network and the somato-motor network were negatively associated with impulsivity. These findings suggest that procrastination may be rooted in emotion-regulation deficits, while impulsivity may be rooted in reward-processing deficits. This deeper understanding of their neural basis provides insights for developing targeted interventions.

Leveraging Distributed Brain Signal at Rest to Predict Internalizing Symptoms in Youth: Deriving a Polyneuro Risk Score From the ABCD Study Cohort

BACKGROUND

The prevalence of internalizing psychopathology rises precipitously from early to mid-adolescence, yet the underlying neural phenotypes that give rise to depression and anxiety during this developmental period remain unclear.

METHODS

Youths from the Adolescent Brain Cognitive Development (ABCD) Study (ages 9-10 years at baseline) with a resting-state functional magnetic resonance imaging scan and mental health data were eligible for inclusion. Internalizing subscale scores from the Brief Problem Monitor-Youth Form were combined across 2 years of follow-up to generate a cumulative measure of internalizing symptoms. The total sample (N = 6521) was split into a large discovery dataset and a smaller validation dataset. Brain-behavior associations of resting-state functional connectivity with internalizing symptoms were estimated in the discovery dataset. The weighted contributions of each functional connection were aggregated using multivariate statistics to generate a polyneuro risk score (PNRS). The predictive power of the PNRS was evaluated in the validation dataset.

RESULTS

The PNRS explained 10.73% of the observed variance in internalizing symptom scores in the validation dataset. Model performance peaked when the top 2% functional connections identified in the discovery dataset (ranked by absolute β weight) were retained. The resting-state functional connectivity networks that were implicated most prominently were the default mode, dorsal attention, and cingulo-parietal networks. These findings were significant (p < 1 × 10-6) as accounted for by permutation testing (n = 7000).

CONCLUSIONS

These results suggest that the neural phenotype associated with internalizing symptoms during adolescence is functionally distributed. The PNRS approach is a novel method for capturing relationships between resting-state functional connectivity and behavior.

Combined graph convolutional networks with a multi-connection pattern to identify tremor-dominant Parkinson's disease and Essential tremor with resting tremor

Essential tremor with resting tremor (rET) and tremor-dominant Parkinson's disease (tPD) share many similar clinical symptoms, leading to frequent misdiagnoses. Functional connectivity (FC) matrix analysis derived from resting-state functional MRI (Rs-fMRI) offers a promising approach for early diagnosis and for exploring FC network pathogenesis in rET and tPD. However, methods relying solely on a single connection pattern may overlook the complementary roles of different connectivity patterns, resulting in reduced diagnostic differentiation. Therefore, we propose a multi-pattern connection Graph Convolutional Network (MCGCN) method to integrate information from various connection modes, distinguishing between rET and healthy controls (HC), tPD and HC, and rET and tPD. We constructed FC matrices using three different connectivity modes for each subject and used these as inputs to the MCGCN model for disease classification. The classification performance of the model was evaluated for each connectivity mode. Subsequently, gradient-weighted class activation mapping (Grad-CAM) was used to identify the most discriminative brain regions. The important brain regions identified were primarily distributed within cerebellar-motor and non-motor cortical networks. Compared with single-pattern GCN, our proposed MCGCN model demonstrated superior classification accuracy, underscoring the advantages of integrating multiple connectivity modes. Specifically, the model achieved an average accuracy of 88.0% for distinguishing rET from HC, 88.8% for rET from tPD, and 89.6% for tPD from HC. Our findings indicate that combining graph convolutional networks with multi-connection patterns can not only effectively discriminate between tPD, rET, and HC but also enhance our understanding of the functional network mechanisms underlying rET and tPD.

Prediction of pharmacological treatment efficacy using electroencephalography-based salience network in patients with major depressive disorder

Introduction

Recent resting-state electroencephalogram (EEG) studies have consistently reported an association between aberrant functional brain networks (FBNs) and treatment-resistant traits in patients with major depressive disorder (MDD). However, little is known about the changes in FBNs in response to external stimuli in these patients. This study investigates whether changes in the salience network (SN) could predict responsiveness to pharmacological treatment in resting-state and external stimuli conditions.

Methods

Thirty-one drug-naïve patients with MDD (aged 46.61 ± 10.05, female 28) and twenty-one healthy controls (aged 43.86 ± 14.14, female 19) participated in the study. After 8 weeks of pharmacological treatment, the patients were divided into non-remitted MDD (nrMDD, n = 14) and remitted-MDD (rMDD, n = 17) groups. EEG data under three conditions (resting-state, standard, and deviant) were analyzed. The SN was constructed with three cortical regions as nodes and weighted phase-lag index as edges, across alpha, low-beta, high-beta, and gamma bands. A repeated measures analysis of the variance model was used to examine the group-by-condition interaction. Machine learning-based classification analyses were also conducted between the nrMDD and rMDD groups.

Results

A notable group-by-condition interaction was observed in the high-beta band between nrMDD and rMDD. Specifically, patients with nrMDD exhibited hypoconnectivity between the dorsal anterior cingulate cortex and right insula (p = 0.030). The classification analysis yielded a maximum classification accuracy of 80.65%.

Conclusion

Our study suggests that abnormal condition-dependent changes in the SN could serve as potential predictors of pharmacological treatment efficacy in patients with MDD.

Transcutaneous Auricular Vagus Nerve Stimulation Modulating the Brain Topological Architecture of Functional Network in Major Depressive Disorder: An fMRI Study

BACKGROUND

Transcutaneous auricular vagus nerve stimulation (taVNS) is effective in regulating mood and high-level cognition in patients with major depressive disorder (MDD). This study aimed to investigate the efficacy of taVNS treatment in patients with MDD and an altered brain topological organization of functional networks.

METHODS

Nineteen patients with MDD were enrolled in this study. Patients with MDD underwent 4 weeks of taVNS treatments; resting-state functional magnetic resonance imaging (rs-fMRI) data of the patients were collected before and after taVNS treatment. The graph theory method and network-based statistics (NBS) analysis were used to detect abnormal topological organizations of functional networks in patients with MDD before and after taVNS treatment. A correlation analysis was performed to characterize the relationship between altered network properties and neuropsychological scores.

RESULTS

After 4 weeks of taVNS treatment, patients with MDD had increased global efficiency and decreased characteristic path length (Lp). Additionally, patients with MDD exhibited increased nodal efficiency (NE) and degree centrality (DC) in the left angular gyrus. NBS results showed that patients with MDD exhibited reduced connectivity between default mode network (DMN)-frontoparietal network (FPN), DMN-cingulo-opercular network (CON), and FPN-CON. Furthermore, changes in Lp and DC were correlated with changes in Hamilton depression scores.

CONCLUSIONS

These findings demonstrated that taVNS may be an effective method for reducing the severity of depressive symptoms in patients with MDD, mainly through modulating the brain's topological organization. Our study may offer insights into the underlying neural mechanism of taVNS treatment in patients with MDD.

From movement to motivation: a proposed framework to understand the antidepressant effect of exercise

Depression is the leading cause of disability worldwide, exerting a profound negative impact on quality of life in those who experience it. Depression is associated with disruptions to several closely related neural and cognitive processes, including dopamine transmission, fronto-striatal brain activity and connectivity, reward processing and motivation. Physical activity, especially aerobic exercise, reduces depressive symptoms, but the mechanisms driving its antidepressant effects are poorly understood. Here we propose a novel hypothesis for understanding the antidepressant effects of exercise, centred on motivation, across different levels of explanation. There is robust evidence that aerobic exercise decreases systemic inflammation. Inflammation is known to reduce dopamine transmission, which in turn is strongly implicated in effort-based decision making for reward. Drawing on a broad range of research in humans and animals, we propose that by reducing inflammation and boosting dopamine transmission, with consequent effects on effort-based decision making for reward, exercise initially specifically improves 'interest-activity' symptoms of depression-namely anhedonia, fatigue and subjective cognitive impairment - by increasing propensity to exert effort. Extending this framework to the topic of cognitive control, we explain how cognitive impairment in depression may also be conceptualised through an effort-based decision-making framework, which may help to explain the impact of exercise on cognitive impairment. Understanding the mechanisms underlying the antidepressant effects of exercise could inform the development of novel intervention strategies, in particular personalised interventions and boost social prescribing.

Resting-State EEG Microstates and Power Spectrum in Borderline Personality Disorder: A High-Density EEG Study

Borderline personality disorder (BPD) is a debilitating psychiatric condition characterized by emotional dysregulation, unstable sense of self, and impulsive, potentially self-harming behavior. In order to provide new neurophysiological insights on BPD, we complemented resting-state EEG frequency spectrum analysis with EEG microstates (MS) analysis to capture the spatiotemporal dynamics of large-scale neural networks. High-density EEG was recorded at rest in 16 BPD patients and 16 age-matched neurotypical controls. The relative power spectrum and broadband MS spatiotemporal parameters were compared between groups and their inter-correlations were examined. Compared to controls, BPD patients showed similar global spectral power, but exploratory univariate analyses on single channels indicated reduced relative alpha power and enhanced relative delta power at parietal electrodes. In terms of EEG MS, BPD patients displayed similar MS topographies as controls, indicating comparable neural generators. However, the MS temporal dynamics were significantly altered in BPD patients, who demonstrated opposite prevalence of MS C (lower than controls) and MS E (higher than controls). Interestingly, MS C prevalence correlated positively with global alpha power and negatively with global delta power, while MS E did not correlate with any measures of spectral power. Taken together, these observations suggest that BPD patients exhibit a state of cortical hyperactivation, represented by decreased posterior alpha power, together with an elevated presence of MS E, consistent with symptoms of elevated arousal and/or vigilance. This is the first study to investigate resting-state MS patterns in BPD, with findings of elevated MS E and the suggestion of reduced posterior alpha power indicating a disorder-specific neurophysiological signature previously unreported in a psychiatric population.

Distinctive intrinsic functional connectivity alterations of anterior cingulate cortex subdivisions in major depressive disorder: A systematic review and meta-analysis

Evidence of whether the intrinsic functional connectivity of anterior cingulate cortex (ACC) and its subregions is altered in major depressive disorder (MDD) remains inconclusive. A systematic review and meta-analysis were therefore performed on the whole-brain resting-state functional connectivity (rsFC) studies using the ACC and its subregions as seed regions in MDD, in order to draw more reliable conclusions. Forty-four ACC-based rsFC studies were included, comprising 25 subgenual ACC-based studies, 11 pregenual ACC-based studies, and 17 dorsal ACC-based studies. Specific alterations of rsFC were identified for each ACC subregion in patients with MDD, with altered rsFC of subgenual ACC in emotion-related brain regions, of pregenual ACC in sensorimotor-related regions, and of dorsal ACC in cognition-related regions. Furthermore, meta-regression analysis revealed a significant negative correlation between the pgACC-caudate hypoconnectivity and percentage of female patients in the study cohort. This meta-analysis provides robust evidence of altered intrinsic functional connectivity of the ACC subregions in MDD, which may hold relevance to understanding the origin of, and treating, the emotional, sensorimotor and cognitive dysfunctions that are often observed in these patients.

Mechanisms of Action of TMS in the Treatment of Depression

Transcranial magnetic stimulation (TMS) is entering increasingly widespread use in treating depression. The most common stimulation target, in the dorsolateral prefrontal cortex (DLPFC), emerged from early neuroimaging studies in depression. Recently, more rigorous casual methods have revealed whole-brain target networks and anti-networks based on the effects of focal brain lesions and focal brain stimulation on depression symptoms. Symptom improvement during therapeutic DLPFC-TMS appears to involve directional changes in signaling between the DLPFC, subgenual and dorsal anterior cingulate cortex, and salience-network regions. However, different networks may be involved in the therapeutic mechanisms for other TMS targets in depression, such as dorsomedial prefrontal cortex or orbitofrontal cortex. The durability of therapeutic effects for TMS involves synaptic neuroplasticity, and specifically may depend upon dopamine acting at the D1 receptor family, as well as NMDA-receptor-dependent synaptic plasticity mechanisms. Although TMS protocols are classically considered 'excitatory' or 'inhibitory', the actual effects in individuals appear quite variable, and might be better understood at the level of populations of synapses rather than individual synapses. Synaptic meta-plasticity may provide a built-in protective mechanism to avoid runaway facilitation or inhibition during treatment, and may account for the relatively small number of patients who worsen rather than improve with TMS. From an ethological perspective, the antidepressant effects of TMS may involve promoting a whole-brain attractor state associated with foraging/hunting behaviors, centered on the rostrolateral periaqueductal gray and salience network, and suppressing an attractor state associated with passive threat defense, centered on the ventrolateral periaqueductal gray and default-mode network.

A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns

Importance

Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments.

Objective

To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment.

Design

Double blind randomized controlled trial.

Setting

Multicenter.

Participants

Patients with early onset recurrent depression from the public Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study.

Interventions

Either sertraline or placebo during 8 weeks (stage 1), and according to response a second line of treatment for 8 additional weeks (stage 2).

Main Outcomes and Measures

To identify a data-driven pattern of symptom variations during these two stages, we performed a Principal Component Analysis (PCA) on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas and manic-like symptoms, resulting in a univariate measure of clinical improvement. We then investigated how initial clinical and neural factors predicted this measure during stage 1. To do so, we extracted resting-state global brain connectivity (GBC) at baseline at the individual level using a whole-brain functional network parcellation. In turn, we computed a linear model for each brain parcel with individual data-driven clinical improvement scores during stage 1 for each group.

Results

192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% of clinical variation was similar across treatment groups at stage 1 and stage 2, suggesting a reproducible pattern of symptom improvement. PC1 patients' scores significantly differed according to treatment during stage 1, whereas no difference of response was evidenced between groups with the Clinical Global Impressions (CGI). Baseline GBC correlated to stage 1 PC1 scores in the sertraline, but not in the placebo group.

Conclusions and Relevance

Using data-driven reduction of symptoms scales, we identified a common profile of symptom improvement across placebo and sertraline. However, the neural patterns of baseline that mapped onto symptom improvement distinguished between treatment and placebo. Our results underscore that mapping from data-driven symptom improvement onto neural circuits is vital to detect treatment-responsive neural profiles that may aid in optimal patient selection for future trials.

Targeted neurostimulation reverses a spatiotemporal biomarker of treatment-resistant depression

Major depressive disorder (MDD) is widely hypothesized to result from disordered communication across brain-wide networks. Yet, prior resting-state-functional MRI (rs-fMRI) studies of MDD have studied zero-lag temporal synchrony (functional connectivity) in brain activity absent directional information. We utilize the recent discovery of stereotyped brain-wide directed signaling patterns in humans to investigate the relationship between directed rs-fMRI activity, MDD, and treatment response to FDA-approved neurostimulation paradigm termed Stanford neuromodulation therapy (SNT). We find that SNT over the left dorsolateral prefrontal cortex (DLPFC) induces directed signaling shifts in the left DLPFC and bilateral anterior cingulate cortex (ACC). Directional signaling shifts in the ACC, but not the DLPFC, predict improvement in depression symptoms, and moreover, pretreatment ACC signaling predicts both depression severity and the likelihood of SNT treatment response. Taken together, our findings suggest that ACC-based directed signaling patterns in rs-fMRI are a potential biomarker of MDD.

Preliminary findings on the effect of childhood trauma on the functional connectivity of the anterior cingulate cortex subregions in major depressive disorder

Objectives

Childhood trauma (CT) is a known risk factor for major depressive disorder (MDD), but the mechanisms linking CT and MDD remain unknown. The purpose of this study was to examine the influence of CT and depression diagnosis on the subregions of the anterior cingulate cortex (ACC) in MDD patients.

Methods

The functional connectivity (FC) of ACC subregions was evaluated in 60 first-episode, drug-naïve MDD patients (40 with moderate-to-severe and 20 with no or low CT), and 78 healthy controls (HC) (19 with moderate-to-severe and 59 with no or low CT). The correlations between the anomalous FC of ACC subregions and the severity of depressive symptoms and CT were investigated.

Results

Individuals with moderate-to severe CT exhibited increased FC between the caudal ACC and the middle frontal gyrus (MFG) than individuals with no or low CT, regardless of MDD diagnosis. MDD patients showed lower FC between the dorsal ACC and the superior frontal gyrus (SFG) and MFG. They also showed lower FC between the subgenual/perigenual ACC and the middle temporal gyrus (MTG) and angular gyrus (ANG) than the HCs, regardless of CT severity. The FC between the left caudal ACC and the left MFG mediated the correlation between the Childhood Trauma Questionnaire (CTQ) total score and HAMD-cognitive factor score in MDD patients.

Conclusion

Functional changes of caudal ACC mediated the correlation between CT and MDD. These findings contribute to our understanding of the neuroimaging mechanisms of CT in MDD.

Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression

Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method was developed to identify discriminative spatio-temporal states associated with FEDN MDD. Finally, statistical analysis was applied to intrinsic and clinical brain characteristics. Compared with HC, FEDN MDD patients exhibited altered spatio-temporal functional states of the default mode network (DMN), the salience network, a hub network (centered on the dorsolateral prefrontal cortex), and a relatively complex coupling network (visual, DMN, motor-somatosensory and subcortical networks). Multi-kernel classification models to distinguish patients from HC obtained areas under the receiver operating characteristic curves up to 0.80. Classification scores correlated with Hamilton Depression Rating Scale scores and age at MDD onset. FEDN MDD patients had multiple abnormal spatio-temporal functional states. Classification scores derived from these states were related to symptom severity. The assessment of spatio-temporal states may represent a powerful clinical and research tool to distinguish between neuropsychiatric patients and controls.

Neural effects of childhood maltreatment on dynamic large-scale brain networks in major depressive disorder

Emerging evidence suggests that childhood maltreatment (CM) alters trajectories of brain development to affect network architecture and is a risk factor for the development and maintenance of depression. The current study aimed to explore the association between CM and depressive severity on the large-scale resting-state networks (RSNs) level in major depressive disorder (MDD) patients and explored the network basis of clinical symptoms. 42 healthy controls without childhood maltreatment, 13 healthy controls with CM, 35 MDD without CM and 50 MDD with CM were included in the study population. Group differences in ten large-scale RSNs, associations between CM and depressive symptom dimensions and network variables were tested. And we explored whether symptom-related networks might discriminate between the four groups. We found one-versus-all-others-network showed an inverted U-shaped curve across groups. Network variables were significantly associated with Hamilton Depression Scale subscores and Childhood Trauma Questionnaire subscores. Different symptoms showed different imaging patterns, and overlapping connections of patterns had better ability to distinguish groups. Our findings suggest that CM could lead to significant changes in both network measures and connections in healthy individuals and MDD. These results deepen our understanding of the neuroimaging mechanisms of CM and MDD.

Altered functional connectivity in common resting-state networks in patients with major depressive disorder: A resting-state functional connectivity study

The neural correlates of major depressive disorder (MDD) remain disputed. In the absence of reliable biological markers, the dysfunction and interaction of neural networks have been proposed as pathophysiological neural mechanisms in depression. Here, we examined the functional connectivity (FC) of brain networks. 51 healthy volunteers (mean age 33.57 ± 7.80) and 55 individuals diagnosed with MDD (mean age 33.89 ± 11.00) participated by performing a resting-state (rs) fMRI scan. Seed to voxel FC analyses were performed. Compared to healthy control (HC), MDD patients showed higher connectivity between the hippocampus and the anterior cingulate cortex (ACC) and lower connectivity between the insula and the ACC. The MDD group displayed lower connectivity between the inferior parietal lobule (IPL) and the superior frontal gyrus (SFG). The current data replicate previous findings regarding the cortico-limbic network (hippocampus - ACC connection) and the salience network (insula - ACC connection) and provide novel insight into altered rsFC in MDD, in particular involving the hippocampus - ACC and the insula - ACC connection. Furthermore, altered connectivity between the IPL and SFG indicates that the processing in higher cognitive processes such as attention and working memory is affected in MDD. These data further support dysfunctional neuronal networks as an interesting pathophysiological marker in depression.

Altered brain regional homogeneity is associated with depressive symptoms in COVID-19

BACKGROUND

COVID-19 is an infectious disease that has spread worldwide in 2020, causing a severe pandemic. In addition to respiratory symptoms, neuropsychiatric manifestations are commonly observed, including chronic fatigue, depression, and anxiety. The neural correlates of neuropsychiatric symptoms in COVID-19 are still largely unknown.

METHODS

A total of 79 patients with COVID-19 (COV) and 17 healthy controls (HC) underwent 3 T functional magnetic resonance imaging at rest, as well as structural imaging. Regional homogeneity (ReHo) was calculated. We also measured depressive symptoms with the Patient Health Questionnaire (PHQ-9), anxiety using the General Anxiety Disorder 7-item scale, and fatigue with the Multidimension Fatigue Inventory.

RESULTS

In comparison with HC, COV showed significantly higher depressive scores. Moreover, COV presented reduced ReHo in the left angular gyrus, the right superior/middle temporal gyrus and the left inferior temporal gyrus, and higher ReHo in the right hippocampus. No differences in gray matter were detected in these areas. Furthermore, we observed a negative correlation between ReHo in the left angular gyrus and PHQ-9 scores and a trend toward a positive correlation between ReHo in the right hippocampus and PHQ-9 scores.

LIMITATIONS

Heterogeneity in the clinical presentation in COV, the different timing from the first positive molecular swab test to the MRI, and the cross-sectional design of the study limit the generalizability of our findings.

CONCLUSIONS

Our results suggest that COVID-19 infection may contribute to depressive symptoms via a modulation of local functional connectivity in cortico-limbic circuits.

Resting-state functional connectivity correlates of anxiety co-morbidity in major depressive disorder

Major depressive disorder (MDD) is frequently co-morbid with anxiety disorders. The co-morbid state has poorer functional outcomes and greater resistance to first line treatments, highlighting the need for novel treatment targets. This systematic review examined differences in resting-state brain connectivity associated with anxiety comorbidity in young- and middle-aged adults with MDD, with the aim of identifying novel targets for neuromodulation treatments, as these treatments are thought to work partly by altering dysfunctional connectivity pathways. Twenty-one studies met inclusion criteria, including a total of 1292 people with MDD. Only two studies included people with MDD and formally diagnosed co-morbid anxiety disorders; the remainder included people with MDD with dimensional anxiety measurement. The quality of most studies was judged as fair. Results were heterogeneous, partly due to a focus on a small set of connectivity relationships within individual studies. There was evidence for dysconnectivity between the amygdala and other brain networks in co-morbid anxiety, and an indication that abnormalities of default mode network connectivity may play an underappreciated role in this condition.

A longitudinal study of functional connectome uniqueness and its association with psychological distress in adolescence

Each human brain has a unique functional synchronisation pattern (functional connectome) analogous to a fingerprint that underpins brain functions and related behaviours. Here we examine functional connectome (whole-brain and 13 networks) maturation by measuring its uniqueness in adolescents who underwent brain scans longitudinally from 12 years of age every four months. The uniqueness of a functional connectome is defined as its ratio of self-similarity (from the same subject at a different time point) to the maximal similarity-to-others (from a given subject and any others at a different time point). We found that the unique whole brain connectome exists in 12 years old adolescents, with 92% individuals having a whole brain uniqueness value greater than one. The cingulo-opercular network (CON; a long-acting 'brain control network' configuring information processing) demonstrated marginal uniqueness in early adolescence with 56% of individuals showing uniqueness greater than one (i.e., more similar to her/his own CON four months later than those from any other subjects) and this increased longitudinally. Notably, the low uniqueness of the CON correlates (β = -18.6, FDR-Q < < 0.001) with K10 levels at the subsequent time point. This association suggests that the individualisation of CON network is related to psychological distress levels. Our findings highlight the potential of longitudinal neuroimaging to capture mental health problems in young people who are undergoing profound neuroplasticity and environment sensitivity period.

Network Diffusion Embedding Reveals Transdiagnostic Subnetwork Disruption and Potential Treatment Targets in Internalizing Psychopathologies

Network diffusion models are a common and powerful way to study the propagation of information through a complex system and they offer straightforward approaches for studying multimodal brain network data. We developed an analytic framework to identify brain subnetworks with perturbed information diffusion capacity using the structural basis that best maps to resting state functional connectivity and applied it towards a heterogeneous dataset of internalizing psychopathologies (IPs), a set of psychiatric conditions in which similar brain network deficits are found across the swath of the disorders, but a unifying neuropathological substrate for transdiagnostic symptom expression is currently unknown. This research provides preliminary evidence of a transdiagnostic brain subnetwork deficit characterized by information diffusion impairment of the right area 8BM, a key brain region involved in organizing a broad spectrum of cognitive tasks, which may underlie previously reported dysfunction of multiple brain circuits in the IPs. We also demonstrate that models of neuromodulation involving targeting this brain region normalize IP diffusion dynamics towards those of healthy controls. These analyses provide a framework for multimodal methods that identify both brain subnetworks with disrupted information diffusion and potential targets of these subnetworks for therapeutic neuromodulatory intervention based on previously well-characterized methodology.

Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites

BACKGROUND

Establishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify MDD were based on small sample size and did not account for the brain connectome that is associated with the pathophysiology of MDD. Here, we addressed these limitations by applying graph convolutional network (GCN) in a large multi-site MDD dataset.

METHODS

Resting-state functional MRI scans of 1586 participants (821 MDD vs. 765 controls) across 16 sites of Rest-meta-MDD consortium were collected. GCN model was trained with individual whole-brain functional network to identify MDD patients from controls, characterize the most salient regions contributing to classification, and explore the relationship between topological characteristics of salient regions and clinical measures.

FINDINGS

GCN achieved an accuracy of 81·5% (95%CI: 80·5-82·5%, AUC: 0·865), which was higher than other common machine learning classifiers. The most salient regions contributing to classification were primarily identified within the default mode, fronto-parietal, and cingulo-opercular networks. Nodal topologies of the left inferior parietal lobule and left dorsolateral prefrontal cortex were associated with depressive severity and illness duration, respectively.

INTERPRETATION

These findings based on a large, multi-site dataset support the feasibility and effectiveness of GCN in characterizing MDD, and also illustrate the potential utility of GCN for enhancing understanding of the neurobiology of MDD by detecting clinically-relevant disruption in functional network topology.

FUNDING

This study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 82027808, 81820108018).

Temporal and Spatial Dynamics of EEG Features in Female College Students with Subclinical Depression

Synchronization of the dynamic processes in structural networks connect the brain across a wide range of temporal and spatial scales, creating a dynamic and complex functional network. Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data. Few studies have focused on potential brain spatiotemporal dynamics in the early stages of depression to use as an early screening feature for depression. Thus, this study aimed to explore large-scale brain network dynamics of individuals both with and without subclinical depression, from the perspective of temporal and spatial dimensions and to input them as features into a machine learning framework for the automatic diagnosis of early-stage depression. To achieve this, spatio–temporal dynamics of rest-state EEG signals in female college students (n = 40) with and without (n = 38) subclinical depression were analyzed using EEG microstate and omega complexity analysis. Then, based on differential features of EEGs between the two groups, a support vector machine was utilized to compare performances of spatio–temporal features and single features in the classification of early depression. Microstate results showed that the occurrence rate of microstate class B was significantly higher in the group with subclinical depression when compared with the group without. Moreover, the duration and contribution of microstate class C in the subclinical group were both significantly lower than in the group without subclinical depression. Omega complexity results showed that the global omega complexity of β-2 and γ band was significantly lower for the subclinical depression group compared with the other group (p < 0.05). In addition, the anterior and posterior regional omega complexities were lower for the subclinical depression group compared to the comparison group in α-1, β-2 and γ bands. It was found that AUC of 81% for the differential indicators of EEG microstates and omega complexity was deemed better than a single index for predicting subclinical depression. Thus, since temporal and spatial complexity of EEG signals were manifestly altered in female college students with subclinical depression, it is possible that this characteristic could be adopted as an early auxiliary diagnostic indicator of depression.

7T ultra-high-field neuroimaging for mental health: an emerging tool for precision psychiatry?

Given the huge symptom diversity and complexity of mental disorders, an individual approach is the most promising avenue for clinical transfer and the establishment of personalized psychiatry. However, due to technical limitations, knowledge about the neurobiological basis of mental illnesses has, to date, mainly been based on findings resulting from evaluations of average data from certain diagnostic groups. We postulate that this could change substantially through the use of the emerging ultra-high-field MRI (UHF-MRI) technology. The main advantages of UHF-MRI include high signal-to-noise ratio, resulting in higher spatial resolution and contrast and enabling individual examinations of single subjects. Thus, we used this technology to assess changes in the properties of resting-state networks over the course of therapy in a naturalistic study of two depressed patients. Significant changes in several network property measures were found in regions corresponding to prior knowledge from group-level studies. Moreover, relevant parameters were already significantly divergent in both patients at baseline. In summary, we demonstrate the feasibility of UHF-MRI for capturing individual neurobiological correlates of mental diseases. These could serve as a tool for therapy monitoring and pave the way for a truly individualized and predictive clinical approach in psychiatric care.

Imbalance between default mode and sensorimotor connectivity is associated with perseverative thinking in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is highly heterogeneous. Although perseverative negative thinking (PT) is a feature of OCD, little is known about its neural mechanisms or relationship to clinical heterogeneity in the disorder. In a sample of 85 OCD patients, we investigated the relationships between self-reported PT, clinical symptom subtypes, and resting-state functional connectivity measures of local and global connectivity. Results indicated that PT scores were highly variable within the OCD sample, with greater PT relating to higher severity of the "unacceptable thoughts" symptom dimension. PT was positively related to local connectivity in subgenual anterior cingulate cortex (ACC), pregenual ACC, and the temporal poles-areas that are part of, or closely linked to, the default mode network (DMN)-and negatively related to local connectivity in sensorimotor cortex. While the majority of patients showed higher local connectivity strengths in sensorimotor compared to DMN regions, OCD patients with higher PT scores had less of an imbalance between sensorimotor and DMN connectivity than those with lower PT scores, with healthy controls exhibiting an intermediate pattern. Clinically, this imbalance was related to both the "unacceptable thoughts" and "symmetry/not-just-right-experiences" symptom dimensions, but in opposite directions. These effects remained significant after accounting for variance related to psychiatric comorbidity and medication use in the OCD sample, and no significant relationships were found between PT and global connectivity. These data indicate that PT is related to symptom and neural variability in OCD. Future work may wish to target this circuity when developing personalized interventions for patients with these symptoms.

Neural functional connectivity during rumination in individuals with adverse childhood experiences

BACKGROUND

Childhood adversity has been associated with greater risk of developing psychopathology, altered processing of emotional stimuli, and changes in neural functioning. Although the neural correlates of rumination have been previously described, little is known about how adverse childhood experiences are related to brain functioning during rumination.

OBJECTIVE

This study explored differences in neural functional connectivity between participants with and without histories of childhood adversity, controlling for tendency to ruminate, during resting-state and induction of rumination.

METHOD

A total of 86 adults (51 women) took part. Based on a diagnostic clinical interview, participants were divided into groups with and without adverse childhood experiences. All participants underwent resting-state imaging and a functional magnetic resonance imaging scan where they performed a rumination induction task.

RESULTS

Individuals with childhood adversities differed from those without adverse experiences in seed-based functional connectivity from right angular gyrus and left superior frontal gyrus during the rumination task. There were also group differences during resting-state in seed-based functional connectivity from the right angular gyrus, left middle temporal gyrus, and left superior frontal gyrus.

CONCLUSIONS

Childhood adversity is associated with altered brain functioning during rumination and resting-state, even after controlling for tendency to ruminate. Our results shed light on the consequences of early adversity. People who experienced childhood adversities differ from those with no adverse experiences in brain functional connectivity when engaged in negative repetitive self-referential thinking.

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

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

Integrating Multimodal and Longitudinal Neuroimaging Data with Multi-Source Network Representation Learning

Uncovering the complex network of the brain is of great interest to the field of neuroimaging. Mining from these rich datasets, scientists try to unveil the fundamental biological mechanisms in the human brain. However, neuroimaging data collected for constructing brain networks is generally costly, and thus extracting useful information from a limited sample size of brain networks is demanding. Currently, there are two common trends in neuroimaging data collection that could be exploited to gain more information: 1) multimodal data, and 2) longitudinal data. It has been shown that these two types of data provide complementary information. Nonetheless, it is challenging to learn brain network representations that can simultaneously capture network properties from multimodal as well as longitudinal datasets. Here we propose a general fusion framework for multi-source learning of brain networks - multimodal brain network fusion with longitudinal coupling (MMLC). In our framework, three layers of information are considered, including cross-sectional similarity, multimodal coupling, and longitudinal consistency. Specifically, we jointly factorize multimodal networks and construct a rotation-based constraint to couple network variance across time. We also adopt the consensus factorization as the group consistent pattern. Using two publicly available brain imaging datasets, we demonstrate that MMLC may better predict psychometric scores than some other state-of-the-art brain network representation learning algorithms. Additionally, the discovered significant brain regions are synergistic with previous literature. Our new approach may boost statistical power and sheds new light on neuroimaging network biomarkers for future psychometric prediction research by integrating longitudinal and multimodal neuroimaging data.

Brain Networks Connectivity in Mild to Moderate Depression: Resting State fMRI Study with Implications to Nonpharmacological Treatment

Network mechanisms of depression development and especially of improvement from nonpharmacological treatment remain understudied. The current study is aimed at examining brain networks functional connectivity in depressed patients and its dynamics in nonpharmacological treatment. Resting state fMRI data of 21 healthy adults and 51 patients with mild or moderate depression were analyzed with spatial independent component analysis; then, correlations between time series of the components were calculated and compared between-group (study 1). Baseline and repeated-measure data of 14 treated (psychotherapy or fMRI neurofeedback) and 15 untreated depressed participants were similarly analyzed and correlated with changes in depression scores (study 2). Aside from diverse findings, studies 1 and 2 both revealed changes in within-default mode network (DMN) and DMN to executive control network (ECN) connections. Connectivity in one pair, initially lower in depression, decreased in no treatment group and was inversely correlated with Montgomery-Asberg depression score change in treatment group. Weak baseline connectivity in this pair also predicted improvement on Montgomery-Asberg scale in both treatment and no treatment groups. Coupling of another pair, initially stronger in depression, increased in therapy though was unrelated to improvement. The results demonstrate possible role of within-DMN and DMN-ECN functional connectivity in depression treatment and suggest that neural mechanisms of nonpharmacological treatment action may be unrelated to normalization of initially disrupted connectivity.

Prefrontal Functional Connectivity During the Verbal Fluency Task in Patients With Major Depressive Disorder: A Functional Near-Infrared Spectroscopy Study

Deviations in activation patterns and functional connectivity have been observed in patients with major depressive disorder (MDD) with prefrontal hemodynamics of patients compared with healthy individuals. The graph-theoretical approach provides useful network metrics for evaluating functional connectivity. The evaluation of functional connectivity during a cognitive task can be used to explain the neurocognitive mechanism underlying the cognitive impairments caused by depression. Overall, 31 patients with MDD and 43 healthy individuals completed a verbal fluency task (VFT) while wearing a head-mounted functional near-infrared spectroscopy (fNIRS) devices. Hemodynamics and functional connectivity across eight prefrontal subregions in the two groups were analyzed and compared. We observed a reduction in prefrontal activation and weaker overall and interhemispheric subregion-wise correlations in the patient group compared with corresponding values in the control group. Moreover, efficiency, the network measure related to the effectiveness of information transfer, showed a significant between-group difference [t (71.64) = 3.66, corrected p < 0.001] along with a strong negative correlation with depression severity (rho = -0.30, p = 0.009). The patterns of prefrontal functional connectivity differed significantly between the patient and control groups during the VFT. Network measures can quantitatively characterize the reduction in functional connectivity caused by depression. The efficiency of the functional network may play an important role in the understanding of depressive symptoms.

Brain responses to social feedback in internalizing disorders: A comprehensive review

Problems with interpersonal relationships are often a chief complaint among those seeking psychiatric treatment; yet heterogeneity and homogeneity across disorders suggests both common and unique mechanisms of impaired interpersonal relationships. Basic science research has begun yielding insights into how the brain responds to social feedback. Understanding how these processes differ as a function of psychopathology can begin to inform the mechanisms that give rise to such interpersonal dysfunction, potentially helping to identify differential treatment targets. We reviewed 46 studies that measured the relationship between brain responses to social feedback and internalizing psychopathology. We found that socially relevant anxiety was associated with amygdala hyperactivity to the anticipation of social feedback. Depression was related to hyperreactivity of regions in the cingulo-opercular network to negative social feedback. Borderline personality disorder (BPD) was associated with hyperactivity of regions in the default mode network to negative social feedback. The review also identified key insights into methodological limitations and potential future directions for the field.

Improved and residual functional abnormalities in major depressive disorder after electroconvulsive therapy

Electroconvulsive therapy (ECT) can induce fast remission of depression but still retain the residual functional impairments in major depressive disorder (MDD) patients. To delineate the different functional circuits of effective antidepressant treatment and residual functional impairments is able to better guide clinical therapy for depression. Herein, voxel-level whole brain functional connectivity homogeneity (FcHo), functional connectivity, multivariate pattern classification approaches were applied to reveal the specific circuits for treatment response and residual impairments in MDD patients after ECT. Increased FcHo values in right dorsomedial prefrontal cortex (dmPFC) and left angular gyrus (AG) and their corresponding functional connectivities between dmPFC and right AG, dorsolateral prefrontal cortex (dlPFC), superior frontal gyrus, precuneus (Pcu) and between left AG with dlPFC, bilateral AG, and left ventrolateral prefrontal cortex in MDD patients after ECT. Moreover, we found decreased FcHo values in left middle occipital gyrus (MOG) and lingual gyrus (LG) and decreased functional connectivities between MOG and dorsal postcentral gyrus (PCG) and between LG and middle PCG/anterior superior parietal lobule in MDD patients before and after ECT compared to healthy controls (HCs). The increased or normalized FcHo and functional connections may be related to effective antidepressant therapy, and the decreased FcHo and functional connectivities may account for the residual functional impairments in MDD patients after ECT. The different change patterns in MDD after ECT indicated a specific brain circuit supporting fast remission of depression, which was supported by the following multivariate pattern classification analyses. Finally, we found that the changed FcHo in dmPFC was correlated with changed depression scores. These results revealed a specific functional circuit supporting antidepressant effects of ECT and neuroanatomical basis for residual functional impairments. Our findings also highlighted the key role of dmPFC in antidepressant and will provide an important reference for depression treatment.

Two-week rTMS-induced neuroimaging changes measured with fMRI in depression

OBJECTIVE

To study the neuroimaging mechanisms of repetitive transcranial magnetic stimulation (rTMS) in treating major depressive disorder (MDD).

METHODS

Twenty-seven treatment-naive patients with major depressive disorder (MDD) and 27 controls were enrolled. All of them were scanned with resting-state functional magnetic resonance imaging (fMRI) at baseline, and 15 patients were rescanned after two-week rTMS. The amplitude of low frequency fluctuation (ALFF) and functional connection degree (FCD), based on voxels and 3 brain networks (default mode network [DMN], central executive network [CEN], salience network[SN]),were used as imaging indicators to analyze. The correlations of brain imaging changes after rTMS with clinical efficacy were calculated.

RESULTS

At baseline, patients groups showed increased ALFF in the right orbital frontal cortex (OFC) and decreased ALFF in the left striatal cortex and medial prefrontal cortex (PFC), while increased FCD in the right dorsal anterior cingulate cortex and OFC and decreased FCD in the right inferior parietal lobe and in the CEN. After rTMS, patients showed increased ALFF in the left dorsolateral prefrontal cortex (DLPFC)and superior frontal gyrus, FCD in the right dorsal anterior cingulate cortex, superior temporal gyrus and CEN, as well as decreased FCD in the bilateral lingual gyrus than pre-rTMS . These rTMS induced neuroimaging changes did not significantly correlated with clinical effecacy.

CONCLUSIONS

This study indicated that rTMS resulted in changes of ALFF and FCD in some brain regions and CEN. But we could not conclude this is the neuroimaging mechanism of rTMS according to the correlation analysis.

Locations for noninvasive brain stimulation in treating depressive disorders: A combination of meta-analysis and resting-state functional connectivity analysis

OBJECTIVE

Many noninvasive brain stimulation techniques have been applied to treat depressive disorders. However, the target brain region in most noninvasive brain stimulation studies is the dorsolateral prefrontal cortex. Exploring new stimulation locations may improve the efficacy of noninvasive brain stimulation for depressive disorders. We aimed to explore potential noninvasive brain stimulation locations for depressive disorders through a meta-analysis and a functional connectivity approach.

METHODS

We conducted a meta-analysis of 395 functional magnetic resonance imaging studies to identify depressive disorder-associated brain regions as regions of interest. Then, we ran resting-state functional connectivity analysis with three different pipelines in 40 depression patients to find brain surface regions correlated with these regions of interest. The 10-20 system coordinates corresponding to these brain surface regions were considered as potential locations for noninvasive brain stimulation.

RESULTS

The 10-20 system coordinates corresponding to the bilateral dorsolateral prefrontal cortex, bilateral inferior frontal gyrus, medial prefrontal cortex, supplementary motor area, bilateral supramarginal gyrus, bilateral primary motor cortex, bilateral operculum, left angular gyrus and right middle temporal gyrus were identified as potential locations for noninvasive brain stimulation in depressive disorders. The coordinates were: posterior to F3, posterior to F4, superior to F3, posterior to F7, anterior to C4, P3, midpoint of F7-T3, posterior to F8, anterior to C3, midpoint of Fz-Cz, midpoint of Fz-Fp1, anterior to T4, midpoint of C3-P3, and anterior to C4.

CONCLUSION

Our study identified several potential noninvasive brain stimulation locations for depressive disorders, which may serve as a basis for future clinical investigations.

Can't get it off my brain: Meta-analysis of neuroimaging studies on perseverative cognition

Perseverative cognition (i.e. rumination and worry) describes intrusive, uncontrollable, repetitive thoughts. These negative affective experiences are accompanied by physiological arousal, as if the individual were facing an external stressor. Perseverative cognition is a transdiagnostic symptom, yet studies of neural mechanisms are largely restricted to specific clinical populations (e.g. patients with major depression). The present study applied activation likelihood estimation (ALE) meta-analyses to 43 functional neuroimaging studies of perseverative cognition to elucidate the neurobiological substrates across individuals with and without psychopathological conditions. Task-related and resting state functional connectivity studies were examined in separate meta-analyses. Across task-based studies, perseverative cognition engaged medial frontal gyrus, cingulate gyrus, insula, and posterior cingulate cortex. Resting state functional connectivity studies similarly implicated posterior cingulate cortex together with thalamus and anterior cingulate cortex (ACC), yet the involvement of ACC distinguished between perseverative cognition in healthy controls (HC) and clinical groups. Perseverative cognition is accompanied by the engagement of prefrontal, insula and cingulate regions, whose interaction may support the characteristic conjunction of self-referential and affective processing with (aberrant) cognitive control and embodied (autonomic) arousal. Within this context, ACC engagement appears critical for the pathological expression of rumination and worry.

Connectivity patterns of cognitive control network in first episode medication-naive depression and remitted depression

BACKGROUND

Cognitive dysfunctions, such as impaired cognitive control, are frequently observed in patients with major depressive disorder (MDD). Although the cognitive control network (CCN) is widely considered a core feature of major depressive disorder (MDD), the relationship between cognitive dysfunction and symptom dimensions remains unclear. This study investigated differences in resting-state functional connectivity of the cognitive control network (CCN) between first-episode medication-naive MDD patients and remitted MDD.

METHODS

We collected resting-state functional MRI (rs-fMRI) data from 22 first-episode medication-naive major depressive disorder (fMDD) patients, 20 patients previously diagnosed with MDD in the remitted phase of depression (rMDD), and 20 healthy controls (HC). The CCN was derived from fMRI images using independent component analysis (ICA), a data-driven image analysis method.

RESULTS

Changes in functional connectivity (FC) within the CCN was mainly attenuated in the right dorsolateral prefrontal cortex and the left inferior parietal lobule, while strengthened in the right dorsal anterior cingulate cortex and the right insula in both fMDD and rMDD groups. Compared with the fMDD group, the rMDD group had decreased FC in the bilateral insula and the right dorsolateral prefrontal cortex. Further analysis explored that the FC in the bilateral insula, the right dorsal anterior cingulate cortex and the right inferior parietal lobule were correlated positively cognitive disturbance factor scores in both patients groups.

CONCLUSIONS

These findings are in agreement with the previous findings that the cognitive control network are impaired in MDD. Furthermore, our results suggest that the alteration of CCN might underpin the cognitive disturbance and the distinct patterns of the CCN between fMDD and rMDD patients may be an important target for effective cognitive remediation in MDD.

Resting-state dynamics as a neuromarker of dopamine administration in healthy female adults

BACKGROUND

Different neuromarkers of people's emotions, personality traits and behavioural performance have recently been identified. However, not much attention has been devoted to neuromarkers of neural responsiveness to drug administration.

AIMS

We investigated the predictive neuromarkers of acute dopamine (DA) administration.

METHODS

In a double-blind, within-subject study, we administrated a DA agonist (pramipexole) or placebo to 27 healthy female subjects. Using multivariate classification and prediction analyses, we examined whether dopaminergic modulations of task-free resting-state brain dynamics predict individual differences in pramipexole's modulation of facial attractiveness evaluations.

RESULTS

Our results demonstrate that pramipexole's effects on brain dynamics could be successfully discriminated from resting-state functional connectivity (accuracy: 78.9%; p < 0.0001). On the behavioural level, pramipexole increased facial attractiveness evaluations (t₍₃₉₎ = 4.44; p < 0.0001). In particular, pramipexole administration enhanced connectivity strength of the cinguloopercular network (t₍₂₃₎ = 3.29; p = 0.003) and increased brain signal variability in subcortical and prefrontal brain areas (t₍₁₃₎ = 3.05, p = 0.009). Importantly, multivariate predictive models reveal that pramipexole-dependent modulation of resting-state dynamics predicted the increase of facial attractiveness evaluations after pramipexole (connectivity strength: standardized mean squared error, smse = 0.65; p = 0.0007; brain signal variability: smse = 0.94, p = 0.015).

CONCLUSION

These results demonstrate that modulations of resting-state brain dynamics induced by a DA agonist predict drug-related effects on evaluation processes, providing a neuromarker of the neural responsiveness of specific brain networks to DA administration.

The superior longitudinal fasciculus and its functional triple-network mechanisms in brooding

Brooding, which refers to a repetitive focus on one's distress, is associated with functional connectivity within Default-Mode, Salience, and Executive-Control networks (DMN; SN; ECN), comprising the so-called "triple-network" of attention. Individual differences in brain structure that might perseverate dysfunctional connectivity of brain networks associated with brooding are less clear, however. Using diffusion and functional Magnetic Resonance Imaging, we explored multimodal relationships between brooding severity, white-matter microstructure, and resting-state functional connectivity in depressed adults (N = 32-44), and then examined whether findings directly replicated in a demographically-similar, independent sample (N = 36-45). Among the fully-replicated results, three core findings emerged. First, brooding severity is associated with functional integration and segregation of the triple-network, particularly with a Precuneal subnetwork of the DMN. Second, microstructural asymmetry of the Superior Longitudinal Fasciculus (SLF) provides a robust structural connectivity basis for brooding and may account for over 20% of its severity (Discovery: adj. R² = 0.18; Replication: adj. R² = 0.22; MSE = 0.06, Predictive R² = 0.22). Finally, microstructure of the right SLF and auxiliary white-matter is associated with the functional connectivity correlates of brooding, both within and between components of the triple-network (Discovery: adj. R² = 0.21; Replication: adj. R² = 0.18; MSE = 0.03, Predictive R² = 0.21-0.22). By cross-validating multimodal discovery with replication, the present findings help to reproducibly unify disparate perspectives of brooding etiology. Based on that synthesis, our study reformulates brooding as a microstructural-functional connectivity neurophenotype.

The psycho-periodic cube

The current diagnostic classification systems in psychiatry have been developed primarily for evidence-based clinical decision making with both categorical and dimensional approaches having their own advantages and disadvantages. Efforts have been made to improve these classification systems, and we are now at the point where we must expand beyond the one-dimensionality of these systems. In this paper, we propose that psychiatric disorders can be arranged in a three-dimensional classification system according to the degree of dysfunctions on three specific axes in a way that is similar to the arrangement of chemical elements according to their atomic weights in Mendeleyev's periodic table. For the three axes, we chose externalization, drive, and attention to represent the three-dimensional descriptions of mental health, namely, well-being in social, motivational, and cognitive areas, respectively. Throughout the paper, we explain our reasons for choosing these three axes and compare our hypothesis with categorical diagnostic systems as well as Cloninger's dimensional diagnostic system using personality disorders, affective disorders, and schizophrenia as the specific diagnostic samples.

Altered Spatial and Temporal Brain Connectivity in the Salience Network of Sensorineural Hearing Loss and Tinnitus

Sensorineural hearing loss (SNHL), sometimes accompanied with tinnitus, is associated with dysfunctions within and outside the classical auditory pathway. The salience network, which is anchored in bilateral anterior insula and dorsal anterior cingulate cortex, has been implicated in sensory integration. Partial auditory deprivation could alter the characteristics of the salience network and other related brain areas, thereby contributing to hearing impairments-induced neuropsychiatric symptoms. To test this hypothesis, we performed fMRI scanning and neuropsychological tests on 32 subjects with long-term bilateral hearing impairment and 30 well-matched Controls. Non-directional functional connectivity and directional Granger causality analysis were used to identify aberrant spatial and temporal patterns of connections targeting bilateral anterior insula and dorsal anterior cingulate cortex. We found that the left anterior insula showed decreased connectivity with right precentral gyrus and superior frontal gyrus. The connections between the dorsal anterior cingulate cortex and middle frontal gyrus, superior parietal gyrus and supplementary motor area (SMA) were also reduced. Relative to Controls, SNHL patients showed abnormal effective connectivity of the salience network, including inferior temporal gyrus, cerebellum lobule VI, lobule VIII, precentral gyrus, middle frontal gyrus and SMA. Furthermore, correlation analysis demonstrated that some of these atypical connectivity measures were correlated with performance of neuropsychiatric tests. These findings suggest that the inefficient modulation of the salience network might contribute to the neural basis of SNHL and tinnitus, as well as associated cognition and emotion deficits.

Segregation of salience network predicts treatment response of depression to repetitive transcranial magnetic stimulation

Background

The present study tested the hypothesis that network segregation, a graph theoretic measure of functional organization of the brain, is correlated with treatment response in patients with major depressive disorder (MDD) undergoing repetitive transcranial magnetic stimulation (rTMS).

Methods

Network segregation, calculated from resting state functional magnetic resonance imaging scans, was measured in 32 patients with MDD who entered a sham-controlled, double-blinded, randomized trial of rTMS to the left dorsolateral prefrontal cortex, and a cohort of 20 healthy controls (HCs). Half of the MDD patients received sham treatment in the blinded phase, followed by active rTMS in the open-label phase. The analyses focused on segregation of the following networks: default mode (DMN), salience (SN), fronto-parietal (FPN), cingulo-opercular (CON), and memory retrieval (MRN).

Results

There was no differential change in network segregation comparing sham to active treatment. However, in the combined group of patients who completed active rTMS treatment (in the blinded plus open-label phases), higher baseline segregation of SN significantly predicted more symptom improvement after rTMS. Compared to HCs at baseline, MDD patients showed decreased segregation in DMN, and trend-level decreases in SN and MRN.

Conclusion

The results highlight the importance of network segregation in MDD, particularly in the SN, where more normal baseline segregation of SN may predict better treatment response to rTMS in depression.

•

We examined network segregation in a cohort of MDD patients receiving rTMS treatment.

•

More normal segregation of SN predicted better response of depression to rTMS.

•

Patients with MDD had decreased network segregation in DMN.

A quantitative meta-analysis of fMRI studies investigating emotional processing in excessive worriers: Application of activation likelihood estimation analysis

BACKGROUND

Excessive worry is a highly impairing cognitive activity which features a range of psychological disorders. Investigations of its disturbed underlying neural mechanisms have presented largely heterogeneous results. This quantitative neuroimaging meta-analysis aims to identify consistent functional disturbances in emotional processing associated with excessive worry across previously published studies.

METHODS

We used the activation likelihood estimation (ALE) method to test for significant convergence across findings of 16 neuroimaging experiments reporting functional aberrations during emotional processing between individuals experiencing high versus normal levels of worry.

RESULTS

Results demonstrated convergent aberrations in high compared to normal worriers mainly in a left-hemispheric cluster comprising parts of the middle frontal gyrus, inferior frontal gyrus and anterior insula. Behavioral characterization indicated the identified cluster to be associated with language processing and memory, while meta-analytic connectivity mapping yielded strong functional connections between the observed convergent regions and parts of the salience network as well as the default mode network.

LIMITATIONS

The ALE method cannot consider findings based on regions of interest analyses and studies without significant group differences.

CONCLUSION

Our results indicate that in response to emotional contexts worry prone individuals exhibit disturbed functioning in brain areas which are possibly associated with deviant inner speech processes experienced by these individuals. The observed clusters may further constitute key nodes within interacting neural networks that support internally and externally oriented cognition and control the dynamic interplay among these processes.

EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms

Background: The few previous studies on resting-state electroencephalography (EEG) microstates in depressive patients suggest altered temporal characteristics of microstates compared to those of healthy subjects. We tested whether resting-state microstate temporal characteristics could capture large-scale brain network dynamic activity relevant to depressive symptomatology. Methods: To evaluate a possible relationship between the resting-state large-scale brain network dynamics and depressive symptoms, we performed EEG microstate analysis in 19 patients with moderate to severe depression in bipolar affective disorder, depressive episode, and recurrent depressive disorder and in 19 healthy controls. Results: Microstate analysis revealed six classes of microstates (A-F) in global clustering across all subjects. There were no between-group differences in the temporal characteristics of microstates. In the patient group, higher depressive symptomatology on the Montgomery-Åsberg Depression Rating Scale correlated with higher occurrence of microstate A (Spearman's rank correlation, r = 0.70, p < 0.01). Conclusion: Our results suggest that the observed interindividual differences in resting-state EEG microstate parameters could reflect altered large-scale brain network dynamics relevant to depressive symptomatology during depressive episodes. Replication in larger cohort is needed to assess the utility of the microstate analysis approach in an objective depression assessment at the individual level.

Reduced Resting State Neural Activity in the Right Orbital Part of Middle Frontal Gyrus in Anxious Depression

BACKGROUND

Anxious depression (AD), which is generally recognized as a common clinical subtype of major depressive disorder (MDD), holds distinctive features compared with unanxious depression (UAD). However, the neural mechanism of AD still remains unrevealed. To give insight to it, we compared resting-state functional magnetic resonance amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) between AD and UAD patients.

METHOD

The data were collected from 60 AD patients, 38 UAD patients, and 60 matched healthy controls. The ALFF and seed-based FC were examined. Pearson correlations were computed between ALFF/FC and clinical measures.

RESULTS

In Comparison with the UAD group, the ALFF value of the right orbital part of middle frontal gyrus (RO-MFG) decreased in AD group. Specifically, the ALFF values of the RO-MFG were negatively correlated with retardation factor scores in AD group (r = -0.376, p = 0.003).

CONCLUSIONS

AD patients exhibited disturbed intrinsic brain function compared with UAD patients. The decreased activity of the RO-MFG is indicative of the alterations involved in the neural basis of AD.

Network changes associated with transdiagnostic depressive symptom improvement following cognitive behavioral therapy in MDD and PTSD

Despite widespread use of cognitive behavioral therapy (CBT) in clinical practice, its mechanisms with respect to brain networks remain sparsely described. In this study, we applied tools from graph theory and network science to better understand the transdiagnostic neural mechanisms of this treatment for depression. A sample of 64 subjects was included in a study of network dynamics: 33 patients (15 MDD, 18 PTSD) received longitudinal fMRI resting state scans before and after 12 weeks of CBT. Depression severity was rated on the Montgomery-Asberg Depression Rating Scale (MADRS). Thirty-one healthy controls were included to determine baseline network roles. Univariate and multivariate regression analyses were conducted on the normalized change scores of within- and between-system connectivity and normalized change score of the MADRS. Penalized regression was used to select a sparse set of predictors in a data-driven manner. Univariate analyses showed greater symptom reduction was associated with an increased functional role of the Ventral Attention (VA) system as an incohesive provincial system (decreased between- and decreased within-system connectivity). Multivariate analyses selected between-system connectivity of the VA system as the most prominent feature associated with depression improvement. Observed VA system changes are interesting in light of brain controllability descriptions: attentional control systems, including the VA system, fall on the boundary between-network communities, and facilitate integration or segregation of diverse cognitive systems. Thus, increasing segregation of the VA system following CBT (decreased between-network connectivity) may result in less contribution of emotional attention to cognitive processes, thereby potentially improving cognitive control.

Focal application of accelerated iTBS results in global changes in graph measures

Graph analysis was used to study the effects of accelerated intermittent theta burst stimulation (aiTBS) on the brain's network topology in medication‐resistant depressed patients. Anatomical and resting‐state functional MRI (rs‐fMRI) was recorded at baseline and after sham and verum stimulation. Depression severity was assessed using the Hamilton Depression Rating Scale (HDRS). Using various graph measures, the different effects of sham and verum aiTBS were calculated. It was also investigated whether changes in graph measures were correlated to clinical responses. Furthermore, by correlating baseline graph measures with the changes in HDRS in terms of percentage, the potential of graph measures as biomarker was studied. Although no differences were observed between the effects of verum and sham stimulation on whole‐brain graph measures and changes in graph measures did not correlate with clinical response, the baseline values of clustering coefficient and global efficiency showed to be predictive of the clinical response to verum aiTBS. Nodal effects were found throughout the whole brain. The distribution of these effects could not be linked to the strength of the functional connectivity between the stimulation site and the node. This study showed that the effects of aiTBS on graph measures distribute beyond the actual stimulation site. However, additional research into the complex interactions between different areas in the brain is necessary to understand the effects of aiTBS in more detail.