Decreased functional connectivity to posterior cingulate cortex in major depressive disorder

Comparative analysis of resting-state EEG functional connectivity in depression and obsessive-compulsive disorder

Major depressive disorder (MDD) and obsessive-compulsive disorder (OCD) are psychiatric disorders that often co-occur. We aimed to investigate whether their high comorbidity could be traced not only by clinical manifestations, but also at the level of functional brain activity. In this paper, we examined the differences in functional connectivity (FC) at the whole-brain level and within the default mode network (DMN). Resting-state EEG was obtained from 43 controls, 26 OCD patients, and 34 MDD patients. FC was analyzed between 68 cortical sources, and between-group differences in the 4-30 Hz range were assessed via the Network Based Statistic method. The strength of DMN intra-connectivity was compared between groups in the theta, alpha and beta frequency bands. A cluster of 67 connections distinguished the OCD, MDD and control groups. The majority of the connections, 8 of which correlated with depressive symptom severity, were found to be weaker in the clinical groups. Only 3 connections differed between the clinical groups, and one of them correlated with OCD severity. The DMN strength was reduced in the clinical groups in the alpha and beta bands. It can be concluded that the high comorbidity of OCD and MDD can be traced at the level of FC.

Gut proinflammatory bacteria is associated with abnormal functional connectivity of hippocampus in unmedicated patients with major depressive disorder

Accumulating evidence has revealed the gut bacteria dysbiosis and brain hippocampal functional and structural alterations in major depressive disorder (MDD). However, the potential relationship between the gut microbiota and hippocampal function alterations in patients with MDD is still very limited. Data of resting-state functional magnetic resonance imaging were acquired from 44 unmedicated MDD patients and 42 demographically matched healthy controls (HCs). Severn pairs of hippocampus subregions (the bilateral cornu ammonis [CA1-CA3], dentate gyrus (DG), entorhinal cortex, hippocampal-amygdaloid transition area, and subiculum) were selected as the seeds in the functional connectivity (FC) analysis. Additionally, fecal samples of participants were collected and 16S rDNA amplicon sequencing was used to identify the altered relative abundance of gut microbiota. Then, association analysis was conducted to investigate the potential relationships between the abnormal hippocampal subregions FC and microbiome features. Also, the altered hippocampal subregion FC values and gut microbiota levels were used as features separately or together in the support vector machine models distinguishing the MDD patients and HCs. Compared with HCs, patients with MDD exhibited increased FC between the left hippocampus (CA2, CA3 and DG) and right hippocampus (CA2 and CA3), and decreased FC between the right hippocampal CA3 and bilateral posterior cingulate cortex. In addition, we found that the level of proinflammatory bacteria (i.e., Enterobacteriaceae) was significantly increased, whereas the level of short-chain fatty acids producing-bacteria (i.e., Prevotellaceae, Agathobacter and Clostridium) were significantly decreased in MDD patients. Furthermore, FC values of the left hippocampal CA3- right hippocampus (CA2 and CA3) was positively correlated with the relative abundance of Enterobacteriaceae in patients with MDD. Moreover, altered hippocampal FC patterns and gut microbiota level were considered in combination, the best discrimination was obtained (AUC = 0.92). These findings may provide insights into the potential role of gut microbiota in the underlying neuropathology of MDD patients.

Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

Metabolic Covariance Connectivity of Posterior Cingulate Cortex Associated with Depression Symptomatology Level in Healthy Young Adults

Early detection in the development of a Major Depressive Disorder (MDD) could guide earlier clinical interventions. Although MDD can begin at a younger age, most people have their first episode in young adulthood. The underlying pathophysiological mechanisms relating to such an increased risk are not clear. The posterior cingulate cortex (PCC), exhibiting high levels of brain connectivity and metabolic activity, plays a pivotal role in the pathological mechanism underlying MDD. In the current study, we used the (F-18) fluorodeoxyglucose (FDG) positron emission tomography (PET) to measure metabolic covariance connectivity of the PCC and investigated its association with depression symptomatology evaluated by the Centre for Epidemiological Studies Depression Inventory-Revised (CESD-R) among 27 healthy individuals aged between 18 and 23 years. A significant negative correlation has been observed between CESD-R scale scores and the PCC metabolic connectivity with the anterior cingulate, medial prefrontal cortex, inferior and middle frontal gyrus, as well as the insula. Overall, our findings suggest that the neural correlates of depressive symptomatology in healthy young adults without a formal diagnosis involve the metabolic connectivity of the PCC. Our findings may have potential implications for early identification and intervention in people at risk of developing depression.

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.

Resting state functional connectivity as a marker of internalizing disorder onset in high-risk youth

While research has linked alterations in functional connectivity of the default mode (DMN), cognitive control (CCN), and salience networks (SN) to depression and anxiety, little research has examined whether these alterations may be premorbid vulnerabilities. This study examined resting state functional connectivity (RSFC) of the CCN, DMN, and SN as markers of risk for developing an onset of a depressive or anxiety disorder in adolescents at high familial risk for these disorders. At baseline, 135 participants aged 11-17 completed resting-state functional magnetic resonance imaging, measures of internalizing symptoms, and diagnostic interviews to assess history of depressive and anxiety disorders. Diagnostic assessments were completed again at 9- or 18-month follow-up for 112 participants. At baseline, increased CCN connectivity to areas of the visual network, and decreased connectivity between the left SN and the precentral gyrus, predicted an increased likelihood of a new onset at follow-up. Increased connectivity between the right SN and postcentral gyrus at baseline predicted first episode onsets at follow-up. Altered connectivity between these regions may represent a risk factor for developing a clinically significant onset of an internalizing disorder. Results may have implications for understanding the neural bases of internalizing disorders for early identification and prevention efforts.

Chronic Mild Traumatic Brain Injury: Aberrant Static and Dynamic Connectomic Features Identified Through Machine Learning Model Fusion

Traumatic Brain Injury (TBI) is a frequently occurring condition and approximately 90% of TBI cases are classified as mild (mTBI). However, conventional MRI has limited diagnostic and prognostic value, thus warranting the utilization of additional imaging modalities and analysis procedures. The functional connectomic approach using resting-state functional MRI (rs-fMRI) has shown great potential and promising diagnostic capabilities across multiple clinical scenarios, including mTBI. Additionally, there is increasing recognition of a fundamental role of brain dynamics in healthy and pathological cognition. Here, we undertake an in-depth investigation of mTBI-related connectomic disturbances and their emotional and cognitive correlates. We leveraged machine learning and graph theory to combine static and dynamic functional connectivity (FC) with regional entropy values, achieving classification accuracy up to 75% (77, 74 and 76% precision, sensitivity and specificity, respectively). As compared to healthy controls, the mTBI group displayed hypoconnectivity in the temporal poles, which correlated positively with semantic (r = 0.43, p < 0.008) and phonemic verbal fluency (r = 0.46, p < 0.004), while hypoconnectivity in the right dorsal posterior cingulate correlated positively with depression symptom severity (r = 0.54, p < 0.0006). These results highlight the importance of residual FC in these regions for preserved cognitive and emotional function in mTBI. Conversely, hyperconnectivity was observed in the right precentral and supramarginal gyri, which correlated negatively with semantic verbal fluency (r=-0.47, p < 0.003), indicating a potential ineffective compensatory mechanism. These novel results are promising toward understanding the pathophysiology of mTBI and explaining some of its most lingering emotional and cognitive symptoms.

The alterations of brain functional connectivity networks in major depressive disorder detected by machine learning through multisite rs-fMRI data

BACKGROUND

The current diagnosis of major depressive disorder (MDD) is mainly based on the patient's self-report and clinical symptoms. Machine learning methods are used to identify MDD using resting-state functional magnetic resonance imaging (rs-fMRI) data. However, due to large site differences in multisite rs-fMRI data and the difficulty of sample collection, most of the current machine learning studies use small sample sizes of rs-fMRI datasets to detect the alterations of functional connectivity (FC) or network attribute (NA), which may affect the reliability of the experimental results.

METHODS

Multisite rs-fMRI data were used to increase the size of the sample, and then we extracted the functional connectivity (FC) and network attribute (NA) features from 1611 rs-fMRI data (832 patients with MDD (MDDs) and 779 healthy controls (HCs)). ComBat algorithm was used to harmonize the data variances caused by the multisite effect, and multivariate linear regression was used to remove age and sex covariates. Two-sample t-test and wrapper-based feature selection methods (support vector machine recursive feature elimination with cross-validation (SVM-RFECV) and LightGBM's "feature_importances_" function) were used to select important features. The Shapley additive explanations (SHAP) method was used to assign the contribution of features to the best classification effect model.

RESULTS

The best result was obtained from the LinearSVM model trained with the 136 important features selected by SVMRFE-CV. In the nested five-fold cross-validation (consisting of an outer and an inner loop of five-fold cross-validation) of 1611 data, the model achieved the accuracy, sensitivity, and specificity of 68.90 %, 71.75 %, and 65.84 %, respectively. The 136 important features were tested in a small dataset and obtained excellent classification results after balancing the ratio between patients with depression and HCs.

CONCLUSIONS

The combined use of FC and NA features is effective for classifying MDDs and HCs. The important FC and NA features extracted from the large sample dataset have some generalization performance and may be used as a reference for the altered brain functional connectivity networks in MDD.

A voice without a mouth no more: The neurobiology of language and consciousness

Most research on the neurobiology of language ignores consciousness and vice versa. Here, language, with an emphasis on inner speech, is hypothesised to generate and sustain self-awareness, i.e., higher-order consciousness. Converging evidence supporting this hypothesis is reviewed. To account for these findings, a 'HOLISTIC' model of neurobiology of language, inner speech, and consciousness is proposed. It involves a 'core' set of inner speech production regions that initiate the experience of feeling and hearing words. These take on affective qualities, deriving from activation of associated sensory, motor, and emotional representations, involving a largely unconscious dynamic 'periphery', distributed throughout the whole brain. Responding to those words forms the basis for sustained network activity, involving 'default mode' activation and prefrontal and thalamic/brainstem selection of contextually relevant responses. Evidence for the model is reviewed, supporting neuroimaging meta-analyses conducted, and comparisons with other theories of consciousness made. The HOLISTIC model constitutes a more parsimonious and complete account of the 'neural correlates of consciousness' that has implications for a mechanistic account of mental health and wellbeing.

Alterations in brain iron deposition with progression of late-life depression measured by magnetic resonance imaging (MRI)-based quantitative susceptibility mapping

BACKGROUND

Previous studies have revealed abnormality of iron deposition in the brain of patients with depression. The progression of iron deposition associated with depression remains to be elucidated.

METHODS

This is a longitudinal study. We explored brain iron deposition with disease progression in 20 patients older than 55 years with depression and on antidepressants, using magnetic resonance imaging (MRI)-based quantitative susceptibility mapping (QSM). Magnetic susceptibility values of the whole brain were compared between baseline and approximately one-year follow-up scans using permutation testing. Furthermore, we examined the relationship of changes between the susceptibility values and disease improvement using Spearman's partial correlation analysis, controlling for age, gender, and the visit interval.

RESULTS

Compared to the initial scan, increased magnetic susceptibility values were found in the medial prefrontal cortex (mPFC), dorsal anterior cingulate cortex (dACC), occipital areas, habenula, brainstem, and cerebellum (P<0.05, corrected). The susceptibility values decreased in the dorsal part of the mPFC, middle and posterior cingulate cortex (MCC and PCC), right postcentral gyrus, right inferior parietal lobule, right precuneus, right supramarginal gyrus, left lingual gyrus, left dorsal striatum, and right thalamus (P<0.05, corrected). Notably, the increase in susceptibility values at the mPFC and dACC negatively correlated with the changes in depression scores, as calculated using the Hamilton Depression Scale (HAMD) (r=-0.613, P=0.009), and the increase in susceptibility values at the cerebellum and habenula negatively correlated with the changes in cognitive scores, which were calculated using the Mini-Mental State Examination (MMSE) (cerebellum: r=-0.500, P=0.041; habenula: r=-0.588, P=0.013). Additionally, the decreased susceptibility values at the white matter near the mPFC (anterior corona radiata) also correlated with the changes in depression scores (r=-0.541, P=0.025), and the decreased susceptibility values at the left lingual gyrus correlated with the changes in cognitive scores (r=-0.613, P=0.009).

CONCLUSIONS

Our study identified brain areas where iron deposition changed with the progression of depression while on antidepressants. The linear relationship of changes in the magnetic susceptibility values in the mPFC, dACC, and some subcortical areas with changes in depression symptoms and cognitive functions of patients is highlighted. Our results strengthen the understanding of the alterations of brain iron levels associated with disease progression in patients with late-life depression.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

C-reactive protein is related to a distinct set of alterations in resting-state functional connectivity contributing to a differential pathophysiology of major depressive disorder

BACKGROUND

Several studies in major depressive disorder (MDD) have found inflammation, especially C-reactive protein (CRP), to be consistently associated with MDD and network dysfunction. The aim was to investigate whether CRP is linked to a distinct set of resting-state functional connectivity (RSFC) alterations.

METHODS

For this reason, we investigated the effects of diagnosis and elevated blood plasma CRP levels on the RSFC in 63 participants (40 females, mean age 31.4 years) of which were 27 patients with a primary diagnosis of MDD and 36 healthy control-subjects (HC), utilizing a seed-based approach within five well-established RSFC networks obtained using fMRI.

RESULTS

Of the ten network pairs examined, five showed increased between-network RSFC-values unambiguously connected either to a diagnosis of MDD or elevated CRP levels. For elevated CRP levels, increased RSFC between DMN and AN was found. Patients showed increased RSFC within DMN areas and between the DMN and ECN and VAN, ECN and AN and AN and DAN.

CONCLUSIONS

The results of this study show dysregulated neural circuits specifically connected to elevated plasma CRP levels and independent of other alterations of RSFC in MDD. This dysfunction in neural circuits might in turn result in a certain immune-inflammatory subtype of MDD.

Resting-state fMRI functional connectivity and mindfulness in clinical and non-clinical contexts: A review and synthesis

This review synthesizes relations between mindfulness and resting-state fMRI functional connectivity of brain networks. Mindfulness is characterized by present-moment awareness and experiential acceptance, and relies on attention control, self-awareness, and emotion regulation. We integrate studies of functional connectivity and (1) trait mindfulness and (2) mindfulness meditation interventions. Mindfulness is related to functional connectivity in the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Specifically, mindfulness-mediated functional connectivity changes include (1) increased connectivity between posterior cingulate cortex (DMN) and dorsolateral prefrontal cortex (FPN), which may relate to attention control; (2) decreased connectivity between cuneus and SN, which may relate to self-awareness; (3) increased connectivity between rostral anterior cingulate cortex region and dorsomedial prefrontal cortex (DMN) and decreased connectivity between rostral anterior cingulate cortex region and amygdala region, both of which may relate to emotion regulation; and lastly, (4) increased connectivity between dorsal anterior cingulate cortex (SN) and anterior insula (SN) which may relate to pain relief. While further study of mindfulness is needed, neural signatures of mindfulness are emerging.

Electrophysiological Changes Related to Childhood Trauma in Patients with Major Depressive Disorder: An Event-related Potential Study

Objective

Methods

Results

Conclusion

Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach

An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.

Hyperconnectivity between the posterior cingulate and middle frontal and temporal gyrus in depression: Based on functional connectivity meta-analyses

Disrupted whole-brain resting-state functional connectivity (RSFC) of the posterior cingulate (PCC) has been highlighted to associate with cognitive and affective dysfunction in major depressive disorder (MDD). However, prior findings showed certain inconsistency about the RSFC of the PCC in MDD. This study aims to investigate the aberrant RSFC of the PCC in MDD using anisotropic effect-size version of seed-based d mapping (AES-SDM). Web of Science and PubMed were searched for studies investigating PCC-based RSFC in MDD. A total of 17 studies, involving 804 patients and 724 healthy controls (HCs), fit our selection criteria. Additionally, to seek for the link between functional and structural differences, we did a meta-analysis on the studies in conjunction with voxel-based morphology (VBM) analysis. The PCC showed higher RSFC with the left middle temporal gyrus (MTG) and the right middle frontal gyrus (MFG), and lower RSFC with the left superior frontal gyrus (SFG) and the left precuneus in patients with MDD than HCs. Moreover, the meta-regression analysis revealed a negative correlation between the FC alteration of the right MFG with the PCC and depression severity. Notably, the left MTG and the left MFG demonstrated gray matter deviations in conjunction analysis. Our results indicated that the aberrant RSFC between the PCC and brain regions sub-serving cognitive control and emotional regulation in patients with MDD. And such functional alterations may have structural basis. These findings may underlie the mechanisms of deficits in cognitive control and emotional regulation of MDD.

Xiaoyaosan Exerts Antidepressant Effect by Downregulating RAGE Expression in Cingulate Gyrus of Depressive-Like Mice

Xiaoyaosan (XYS), as a classic Chinese medicine compound, has been proven to have antidepressant effect in many studies, but its mechanism has not been clarified. In our previous studies, we found that chronic stress can induce depressive-like behavior and lead to emotion-related cingulate gyrus (Cg) dysfunction, as well as the decrease of neurotrophic factors and the increase of inflammatory-related proteins. Therefore, we speculated that XYS may play an antidepressant role by regulating the inflammation-related receptor of advanced glycation protein end product (RAGE) to affect the functional connectivity (FC) signal of the Cg and improve the depressive-like behavior. In order to verify this hypothesis, we analyzed the FC and RAGE expression in the Cg of depressive-like mice induced by chronic unpredictable mild stress (CUMS) and verified it with RAGE knockout mice. At the same time, we detected the effect of XYS on the depressive-like behavior, expression of RAGE, and the FC of the Cg of mice. The results showed that the FC of the Cg of depressive-like mice induced by CUMS was weakened, and the expression of RAGE was upregulated. The antidepressant effect of XYS is similar to that of fluoxetine hydrochloride, which can significantly reduce the depressive-like behavior of mice and inhibit the expression of the RAGE protein and mRNA in the Cg, and increase the FC of the Cg in mice. In conclusion, XYS may play an antidepressant role by downregulating the expression of RAGE in the Cg of depressive-like mice induced by CUMS, thereby affecting the functional signal and improving the depressive-like behavior.

Ultra-high field imaging in Major Depressive Disorder: a review of structural and functional studies

BACKGROUND

Major depressive disorder (MDD) is a severe and pervasive psychiatric condition with a lifetime prevalence of 15-25%. Numerous Magnetic Resonance Imaging (MRI) studies employing scans at field strengths of 1.5T or 3T have been carried out in the last decades, providing an unprecedented insight into the neural correlates of MDD. However, in recent years, MRI technology has largely progressed and the use of scans at ultra-high field (≥ 7T) has improved the sensitivity and the resolution of MR images. In this context, with this review we aim to summarize evidence of structural and functional brain mechanisms underlying MDD obtained with ultra-high field MRI.

METHODS

We conducted a search on PubMed, Scopus and Web of Science of neuroimaging studies on MDD patients, which employed ultra-high field MRI. We detected six structural MRI studies, two Diffusion Tensor Imaging (DTI) studies and five functional MRI (fMRI) studies.

RESULTS

Overall, the MRI and DTI studies showed volumetric and structural connectivity alterations in the hippocampus and, to a lesser extent, in the amygdala. In contrast, more heterogeneous results were reported by fMRI studies, which, though, described functional abnormalities in the cingulate cortex, thalamus and several other brain areas.

LIMITATIONS

The small sample size and the heterogeneity in patients' samples, processing and study design limit the conclusion of the present review.

CONCLUSIONS

Studies employing scans at ultra-high magnetic field may provide a useful contribution to the mixed body of literature on MDD. This preliminary but promising evidence confirms the importance of performing ultra-high field MRI investigations in order to detect and better characterize subtle brain abnormalities in MDD.

Strategies to solve the reverse inference fallacy in future MRI studies of schizophrenia: a review

Few advances in schizophrenia research have been translated into clinical practice, despite 60 years of serum biomarkers studies and 50 years of genetic studies. During the last 30 years, neuroimaging studies on schizophrenia have gradually increased, partly due to the beautiful prospect that the pathophysiology of schizophrenia could be explained entirely by the Human Connectome Project (HCP). However, the fallacy of reverse inference has been a critical problem of the HCP. For this reason, there is a dire need for new strategies or research "bridges" to further schizophrenia at the biological level. To understand the importance of research "bridges," it is vital to examine the strengths and weaknesses of the recent literature. Hence, in this review, our team has summarized the recent literature (1995-2018) about magnetic resonance imaging (MRI) of schizophrenia in terms of regional and global structural and functional alterations. We have also provided a new proposal that may supplement the HCP for studying schizophrenia. As postulated, despite the vast number of MRI studies in schizophrenia, the lack of homogeneity between the studies, along with the relatedness of schizophrenia with other neurological disorders, has hindered the study of schizophrenia. In addition, the reverse inference cannot be used to diagnose schizophrenia, further limiting the clinical impact of findings from medical imaging studies. We believe that multidisciplinary technologies may be used to develop research "bridges" to further investigate schizophrenia at the single neuron or neuron cluster levels. We have postulated about future strategies for overcoming the current limitations and establishing the research "bridges," with an emphasis on multimodality imaging, molecular imaging, neuron cluster signals, single transmitter biomarkers, and nanotechnology. These research "bridges" may help solve the reverse inference fallacy and improve our understanding of schizophrenia for future studies.

Identification of common neural substrates with connectomic abnormalities in four major psychiatric disorders: A connectome-wide association study

Background

Recent imaging studies of large datasets suggested that psychiatric disorders have common biological substrates. This study aimed to identify all the common neural substrates with connectomic abnormalities across four major psychiatric disorders by using the data-driven connectome-wide association method of multivariate distance matrix regression (MDMR).

Methods

This study analyzed a resting functional magnetic resonance imaging dataset of 100 patients with schizophrenia, 100 patients with bipolar I disorder, 100 patients with bipolar II disorder, 100 patients with major depressive disorder, and 100 healthy controls (HCs). We calculated a voxel-wise 4,330 × 4,330 matrix of whole-brain functional connectivity (FC) with 8-mm isotropic resolution for each participant and then performed MDMR to identify structures where the overall multivariate pattern of FC was significantly different between each patient group and the HC group. A conjunction analysis was performed to identify common neural regions with FC abnormalities across these four psychiatric disorders.

Results

The conjunction of the MDMR maps revealed that the four groups of patients shared connectomic abnormalities in distributed cortical and subcortical structures, which included bilateral thalamus, cerebellum, frontal pole, supramarginal gyrus, postcentral gyrus, lingual gyrus, lateral occipital cortex, and parahippocampus. The follow-up analysis based on pair-wise FC of these regions demonstrated that these psychiatric disorders also shared similar patterns of FC abnormalities characterized by sensory/subcortical hyperconnectivity, association/subcortical hypoconnectivity, and sensory/association hyperconnectivity.

Conclusions

These findings suggest that major psychiatric disorders share common connectomic abnormalities in distributed cortical and subcortical regions and provide crucial support for the common network hypothesis of major psychiatric disorders.

Association of cortical thickness with age of onset in first-episode, drug-naïve major depression

OBJECTIVE

We previously showed differences in brain grey matter volume changes between patients with early-onset adult depression (EOD) and late-onset adult depression (LOD). Here, we aim to identify whether cortical thickness (CT) is affected by the age of onset in patients with depression.

METHODS

High-resolution MRI images were obtained for 54 major depressive disorder (MDD) patients with EOD, 58 patients with LOD, 57 young healthy controls (HCs), and 58 aged HCs. Depression severity was assessed using the Hamilton Depression Rating Scale 17-item (HDRS17). Associations between CT of patients and clinical scores were analyzed.

RESULTS

There was a significant main effect of diagnosis for the left rostal anterior cingulate (rACC), right inferior temporal, right lateral orbitofrontal cortex (lOFC), and bilateral pericalcarine. A remarkable onset age-group effect on CT was observed in the rACC and bilateral caudal anterior cingulate (cACC). The diagnosis-by-onset age interaction effect was found in bilateral rACC and right lOFC. Thinning CT in bilateral rACC was observed in EOD patients compared to young HCs. Compared to older HCs, thicker CT in lOFC was seen in the LOD patient group. Compared with the LOD group, the EOD group showed cortical thinning of the right cACC and posterior cingulate cortex (PCC). There were no significant associations between CT in right cACC or PCC with symptom severity or illness duration.

CONCLUSIONS

MDD patients with different age at onset show distinct CT alterations, suggesting potentially divergent pathological mechanisms of EOD and LOD.

Abnormal functional connectivity strength in first-episode, drug-naïve adult patients with major depressive disorder

BACKGROUND

The pathogenesis of major depressive disorder (MDD) is complicated and equivocal. Previous studies have found an incidence of abnormal changes of neural networks, with plentiful evidence pointing the finger of suspicion firmly at the default mode network (DMN) and cortico-limbic networks. The aim of the present study was to use the approach of functional connectivity strength (FCS) to directly investigate the features of spontaneous brain activity in the case of first-episode, drug-naïve adult patients with MDD at rest.

METHODS

Resting-state functional magnetic resonance imaging (MRI) scans were performed on 23 first-episode drug-naïve major depressive disorder (MDD) patients and 20 healthy controls (HCs). In this study, using graph-theory approaches(FCS), we computed the characteristics of brain connectivity. Simultaneously, we used a series of validated test procedures to evaluate the patients' cognitive function. Subsequently, the results were compared with the peak of FCS value and a correlation analysis was conducted.

RESULTS

Compared with the HCs group, MDD patients showed significantly decreased FCS in bilateral posterior cingulate cortex (PCC)/precuneus and bilateral prefrontal cortex(PFC) and increased FCS in right posterior central gyrus, left thalamus and left temporal lobe. These brain regions belongs to the default-mode network and cortico-limbic networks. Finally, the correlation analyses showed the negative correlation of the FCS values in the left posterior cingulate cortex (PCC)/precuneus and Hamilton Anxiety Rating Scale (HAMA, r = -0.472, p = .023), Stroop Color Word Test-A(SCWT-A, r = -0.451, p = .031), Stroop Color Word Test-B(SCWT-B, r = -0.588, p = .003).Meanwhile, there was negative correlation between the FCS values in the left thalamus and SCWT-A(r = -0.473, p = .023), SCWT-B(r = -0.465, p = .025), SCWTC(r = -0.524, p = .010).In addition, the FCS values in the right PCC has negative correlation with Montgomery Asberg Depression Rating Scale (MADRS) (r = -0.433, p = .039).

CONCLUSIONS

DMN is an important node of MDD. FCS within the default mode network and cortico-limbic networks in patients with major depressive disorder has been changed in the early stage of MDD. FCS can provide favourable and additional evidence in the investigation of brain pathophysiology and therapy in depression.

Aberrant functional connectivity of the cingulate subregions in right-sided temporal lobe epilepsy

Patients with temporal lobe epilepsy (TLE) have been indicated to exhibit abnormal resting-state functional connectivity (rsFC) of the cingulate cortex. However, it has remained elusive whether cingulate subregions show different connectivity patterns in TLE. The present study aimed to investigate the differences in rsFC of each cingulate subregion between patients with right-sided TLE (rTLE) and healthy controls (HCs), as well as their association with executive control performance in rTLE. A total of 27 patients with rTLE and 20 age-, sex- and education-matched healthy controls were recruited and all participants underwent resting-state functional MRI and an attention network test for the assessment executive control function. In each hemisphere, the cingulate gyrus (CG) was divided into CG-1 (dorsal area 23; A23d), CG-2 (rostroventral area 24; A24rv), CG-3 (pregenual area 32; A32p), CG-4 (ventral area 23; A23v), CG-5 (caudodorsal area 24; A24cd), CG-6 (caudal area 24; A23c) and CG-7 (subgenual area 32; A32sg). Pearson's correlation analysis was performed to assess the correlation between the altered FCs of the cingulate subregions and clinical variables. In patients with rTLE, the majority of the cingulate subregions exhibited decreased rsFC; this was primarily restricted to the right CG-2, the bilateral CG-6 and the bilateral CG-7. Increased rsFC was only detected in rTLE restricted to the left CG-1. Impairments in executive control efficiency were identified in patients with rTLE in comparison with the HCs. Significant alterations in rsFC between the cingulate subregion and the brain regions were mostly decreased (and some slightly increased), suggesting that FC may potentially have a left-side advantage in patients with rTLE. FC variations of the cingulate subregions were indicated to be specific to rTLE. In addition, increased connectivity in the left CG-1 and left superior frontal gyrus were negatively correlated with executive control performance, suggesting a compensatory mechanism on executive control deficits in pathological conditions. This information on differentially altered FC patterns of the cingulate subregions may provide a deeper understanding of the complex neurological mechanisms and executive control dysfunctions underlying rTLE.

Identification of Common Thalamocortical Dysconnectivity in Four Major Psychiatric Disorders

BACKGROUND

Recent genetic and imaging analyses of large datasets suggested that common biological substrates exist across psychiatric diagnoses. Functional connectivity (FC) abnormalities of thalamocortical circuits were consistently found in patients with schizophrenia but have been less studied in other major psychiatric disorders. This study aimed to examine thalamocortical FC in 4 major psychiatric disorders to identify the common connectivity abnormalities across major psychiatric disorders.

METHODS

This study recruited 100 patients with schizophrenia, 100 patients with bipolar I disorder, 88 patients with bipolar II disorder, 100 patients with major depressive disorder, and 160 healthy controls (HCs). Each participant underwent resting functional magnetic resonance imaging. The thalamus was used to derive FC maps, and group comparisons were made between each patient group and HCs using an independent-sample t test. Conjunction analysis was used to identify the common thalamocortical abnormalities among these 4 psychiatric disorders.

RESULTS

The 4 groups of patients shared a similar pattern of thalamocortical dysconnectivity characterized by a decrease in thalamocortical FC with the dorsal anterior cingulate, anterior prefrontal cortex and inferior parietal cortex. The groups also showed an increase in FC with the postcentral gyrus, precentral gyrus, superior temporal cortex, and lateral occipital areas. Further network analysis demonstrated that the frontoparietal regions showing hypoconnectivity belonged to the salience network.

CONCLUSION

Our findings provide FC evidence that supports the common network hypothesis by identifying common thalamocortical dysconnectivities across 4 major psychiatric disorders. The network analysis also supports the cardinal role of salience network abnormalities in major psychiatric disorders.

Pubertal maturation and sex effects on the default-mode network connectivity implicated in mood dysregulation

This study examines the effects of puberty and sex on the intrinsic functional connectivity (iFC) of brain networks, with a focus on the default-mode network (DMN). Consistently implicated in depressive disorders, the DMN's function may interact with puberty and sex in the development of these disorders, whose onsets peak in adolescence, and which show strong sex disproportionality (females > males). The main question concerns how the DMN evolves with puberty as a function of sex. These effects are expected to involve within- and between-network iFC, particularly, the salience and the central-executive networks, consistent with the Triple-Network Model. Resting-state scans of an adolescent community sample (n = 304, male/female: 157/147; mean/std age: 14.6/0.41 years), from the IMAGEN database, were analyzed using the AFNI software suite and a data reduction strategy for the effects of puberty and sex. Three midline regions (medial prefrontal, pregenual anterior cingulate, and posterior cingulate), within the DMN and consistently implicated in mood disorders, were selected as seeds. Within- and between-network clusters of the DMN iFC changed with pubertal maturation differently in boys and girls (puberty-X-sex). Specifically, pubertal maturation predicted weaker iFC in girls and stronger iFC in boys. Finally, iFC was stronger in boys than girls independently of puberty. Brain-behavior associations indicated that lower connectivity of the anterior cingulate seed predicted higher internalizing symptoms at 2-year follow-up. In conclusion, weaker iFC of the anterior DMN may signal disconnections among circuits supporting mood regulation, conferring risk for internalizing disorders.

Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis

Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi-view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l_2,1 -norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group-sparse network at local level. We then linearly fuse the selected features from each individual network via a multi-kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.

Gamma-hydroxybutyrate increases brain resting-state functional connectivity of the salience network and dorsal nexus in humans

According to the triple network hypothesis the brain is equipped with three core neurocognitive networks: the default mode (DMN), the salience (SN), and the central executive (CEN) network. Moreover, the so called dorsal nexus, has met growing interest as it is a hub region connecting these three networks. Assessment of resting-state functional connectivity (rsFC) of these networks enables the elucidation of drug-induced brain alterations. Gamma-hydroxybutyrate (GHB) is a GHB/GABA-B receptor agonist that induces a paradoxical state of mixed stimulation and sedation at moderate doses, which makes it a valuable tool to investigate neural signatures of subjective drug effects. Employing a placebo-controlled, double-blind, randomized, cross-over design, we assessed the effects of GHB (35 mg/kg p. o.) in 19 healthy male subjects on DMN-, SN-, CEN-, and dorsal nexus-rsFC measured by functional magnet resonance imaging and applying independent component as well as seed-based analyses, while subjective drug effects were investigated using visual analog scales (VAS). Subjectively, GHB increased VAS ratings of a general drug effect, stimulation, and sedation. Intrinsic DMN-, and CEN-rsFC remained largely unchanged under GHB, but the drug increased SN-DMN-rsFC and SN-dorsal nexus-rsFC, while dorsal nexus-rsFC was reciprocally increased to both the SN (right anterior insula) and to the CEN (right middle frontal gyrus). Increased sedation significantly predicted the observed SN-dorsal nexus-rsFC. In conclusion, GHB generates a unique stimulant/sedative subjective state that is paralleled by a complex pattern of increased functional connectivity encompassing all three core neurocognitive networks of the brain, while increased SN-dorsal nexus-rsFC was demonstrated to be a potential signature of the sedative component of the drug effect.

Relations between cortical thickness, serotonin 1A receptor binding, and structural connectivity: A multimodal imaging study

Serotonin 1A (5-HT_1A ) receptors play a direct role in neuronal development, cell proliferation, and dendritic branching. We hypothesized that variability in 5-HT_1A binding can affect cortical thickness, and may account for a subtype of major depressive disorder (MDD) in which both are altered. To evaluate this, we measured cortical thickness from structural magnetic resonance imaging (MRI) and 5-HT_1A binding by positron emission tomography (PET) in an exploratory study. To examine a range of 5-HT_1A binding and cortical thickness values, we recruited 25 healthy controls and 19 patients with MDD. We hypothesized increased 5-HT_1A binding in the raphe nucleus (RN) would be negatively associated with cortical thickness due to reduced serotonergic transmission. Contrary to our hypothesis, raphe 5-HT_1A binding was positively correlated with cortical thickness in right posterior cingulate cortex (PCC), a region implicated in the default mode network. Cortical thickness was also positively correlated with 5-HT_1A in each cortical region. We further hypothesized that the strength of 5-HT_1A -cortical thickness correlation depends on the number of axons between the raphe nucleus and each region. To explore this we related 5-HT_1A -cortical thickness correlation coefficients to the number of tracts connecting that region and the raphe, as measured by diffusion tensor imaging (DTI) in an independent sample. The 5-HT_1A -cortical thickness association correlated significantly with the number of tracts to each region, supporting our hypothesis. We posit a defect in the raphe may affect the PCC within the default mode network in MDD through serotonergic fibers, resulting in increased ruminative processing.

Effect of Continuous Touch on Brain Functional Connectivity Is Modified by the Operator's Tactile Attention

Touch has been always regarded as a powerful communication channel playing a key role in governing our emotional wellbeing and possibly perception of self. Several studies demonstrated that the stimulation of C-tactile afferent fibers, essential neuroanatomical elements of affective touch, activates specific brain areas and the activation pattern is influenced by subject’s attention. However, no research has investigated how the cognitive status of who is administering the touch produces changes in brain functional connectivity of touched subjects. In this functional magnetic resonance imaging (fMRI) study, we investigated brain connectivity while subjects were receiving a static touch by an operator engaged in either a tactile attention or auditory attention task. This randomized-controlled single-blinded study enrolled 40 healthy right-handed adults and randomly assigned to either the operator tactile attention (OTA) or the operator auditory attention (OAA) group. During the five fMRI resting-state runs, the touch was delivered while the operator focused his attention either: (i) on the tactile perception from his hands (OTA group); or (ii) on a repeated auditory stimulus (OAA group). Functional connectivity analysis revealed that prolonged sustained static touch applied by an operator engaged with focused tactile attention produced a significant increase of anticorrelation between posterior cingulate cortex (PCC-seed) and right insula (INS) as well as right inferior-frontal gyrus but these functional connectivity changes are markedly different only after 15 min of touching across the OTA and OAA conditions. Interestingly, data also showed anticorrelation between PCC and left INS with a distinct pattern over time. Indeed, the PCC-left INS anticorrelation is showed to start and end earlier compared to that of PCC-right INS. Taken together, the results of this study showed that if a particular cognitive status of the operator is sustained over time, it is able to elicit significant effects on the subjects’ functional connectivity patterns involving cortical areas processing the interoceptive and attentional value of touch.

Aberrant functional connectivity in depression as an index of state and trait rumination

Depression has been shown to be related to a variety of aberrant brain functions and structures. Particularly the investigation of alterations in functional connectivity (FC) in major depressive disorder (MDD) has been a promising endeavor, since a better understanding of pathological brain networks may foster our understanding of the disease. However, the underling mechanisms of aberrant FC in MDD are largely unclear. Using functional near-infrared spectroscopy (fNIRS) we investigated FC in the cortical parts of the default mode network (DMN) during resting-state in patients with current MDD. Additionally, we used qualitative and quantitative measures of psychological processes (e.g., state/trait rumination, mind-wandering) to investigate their contribution to differences in FC between depressed and non-depressed subjects. Our results indicate that 40% of the patients report spontaneous rumination during resting-state. Depressed subjects showed reduced FC in parts of the DMN compared to healthy controls. This finding was linked to the process of state/trait rumination. While rumination was negatively correlated with FC in the cortical parts of the DMN, mind-wandering showed positive associations.