Reduced default mode network functional connectivity in patients with recurrent major depressive disorder

Dissecting heterogeneity in major depressive disorder via normative model-driven subtyping of functional brain networks

BACKGROUND

Major depressive disorder (MDD) is a prevalent and intricate mental health condition characterized by a wide range of symptoms. A fundamental challenge in understanding MDD lies in elucidating the brain mechanisms underlying the complexity and diversity of these symptoms, particularly the heterogeneity reflected in individual differences and subtype variations within brain networks.

METHODS

To address this problem, we explored the brain network topology using resting-state functional magnetic resonance imaging (rs-fMRI) data from a cohort of 797 MDD patients and 822 matched healthy controls (HC). Utilizing normative modeling of HC, we quantified individual deviations in brain network degree centrality among MDD patients. Through k-means clustering of these deviation profiles, we identified two clinically meaningful MDD subtypes. Moreover, we employed Neurosynth to analyze the cognitive correlates of these subtypes.

RESULTS

Subtype 1 exhibited positive deviations of degree centrality in the limbic (LIM), frontoparietal (FPN), and default mode networks (DMN), but negative deviations in the visual (VIS) and sensorimotor networks (SMN), positively correlating with higher cognitive functions and negatively with basic perceptual processes. In contrast, subtype 2 demonstrated opposing patterns, characterized by negative deviations in degree centrality of the LIM, FPN, and DMN and positive deviations of the VIS and SMN, along with inverse cognitive associations.

CONCLUSIONS

Our findings underscore the heterogeneity within MDD, revealing two distinct patterns of network topology between unimodal and transmodal networks, offering a valuable reference for personalized diagnosis and treatment strategies.

Reduced mind-wandering and fewer depressive symptoms associated with damage to the medial prefrontal cortex and default mode network

Depressive disorders have been consistently associated with elevated levels of mind-wandering and self-focused negative rumination. Separate tracks of research have implicated brain structures within the default mode network (DMN) in both mind-wandering and depression. In this study, we hypothesized that diminished mind-wandering and fewer depressive symptoms would co-occur in individuals with damage to the DMN. To test this hypothesis, we used a k-means clustering algorithm to identify a target group of patients with reduced mind-wandering and fewer depressive symptoms relative to brain-damaged comparison subjects (n = 37 of 68; ps < .001). The anatomical localization of lesions for this target group was predominantly within the medial prefrontal cortex (mPFC). Structural and functional lesion network mapping results revealed that lesions of the target group had significantly greater connectivity with DMN and limbic regions. Taken together, these results suggest that brain injury affecting the mPFC and DMN is associated with both reduced mind-wandering and fewer depressive symptoms. Further investigation of neuroanatomical substrates that mediate a causal relationship between mind-wandering and mood may facilitate the identification of new therapeutic targets for neuromodulation in patients with disorders characterized by maladaptive mind-wandering, such as rumination.

Using effective connectivity-based predictive modeling to predict MDD scale scores from multisite rs-fMRI data

BACKGROUND

Major depressive disorder (MDD) is a severe mental illness, and the Hamilton Depression Rating Scale (HAMD) is commonly used to quantify its severity. Our aim is to develop a predictive model for MDD symptoms using machine learning techniques based on effective connectivity (EC) from resting-state functional magnetic resonance imaging (rs-fMRI).

NEW METHOD

We obtained large-scale rs-fMRI data and HAMD scores from the multi-site REST-meta-MDD dataset. Average time series were extracted using different atlases. Brain EC features were computed using Granger causality analysis based on symbolic path coefficients, and a machine learning model based on EC was constructed to predict HAMD scores. Finally, the most predictive features were identified and visualized.

RESULTS

Experimental results indicate that different brain atlases significantly impact predictive performance, with the Dosenbach atlas performing best. EC-based models outperformed functional connectivity, achieving the best predictive accuracy (r = 0.81, p < 0.001, Root Mean Squared Error=3.55). Among various machine learning methods, support vector regression demonstrated superior performance.

COMPARISON WITH EXISTING METHODS

Current phenotype score prediction primarily relies on FC, which cannot indicate the direction of information flow within brain networks. Our method is based on EC, which contains more comprehensive brain network information and has been validated on large-scale multi-site data.

CONCLUSIONS

Brain network connectivity features effectively predict HAMD scores in MDD patients. The identified EC feature network may serve as a biomarker for predicting symptom severity. Our work may provide clinically significant insights for the early diagnosis of MDD, thereby facilitating the development of personalized diagnostic tools and therapeutic interventions.

Neuroimaging-based variability in subtyping biomarkers for psychiatric heterogeneity

Neuroimaging-based subtyping is increasingly used to explain heterogeneity in psychiatric disorders. However, the clinical utility of these subtyping efforts remains unclear, and replication has been challenging. Here we examined how the choice of neuroimaging measures influences the derivation of neuro-subtypes and the consequences for clinical delineation. On a clinically heterogeneous dataset (total n = 566) that included controls (n = 268) and cases (n = 298) of psychiatric conditions, including individuals diagnosed with post-traumatic stress disorder (PTSD), traumatic brain injury (TBI), and comorbidity of both (PTSD&TBI), we identified neuro-subtypes among the cases using either structural, resting-state, or task-based measures. The neuro-subtypes for each modality had high internal validity but did not significantly differ in their clinical and cognitive profiles. We further show that the choice of neuroimaging measures for subtyping substantially impacts the identification of neuro-subtypes, leading to low concordance across subtyping solutions. Similar variability in neuro-subtyping was found in an independent dataset (n = 1642) comprised of major depression disorder (MDD, n = 848) and controls (n = 794). Our results suggest that the highly anticipated relationships between neuro-subtypes and clinical features may be difficult to discover.

Abnormal structural covariance network in major depressive disorder: Evidence from the REST-meta-MDD project

BACKGROUND

Major depressive disorder (MDD) is a common mental illness associated with brain morphological abnormalities. Although extensive studies have examined gray matter volume (GMV) changes in MDD, inconsistencies persist in reported findings. In the current study, we employed source-based morphometry (SBM) and structural covariance network (SCN) analyses to a large multi-center sample from the REST-meta-MDD database, aiming to characterize robust results of structural abnormalities in MDD.

METHODS

We analyzed 798 MDD patients and 974 healthy controls (HCs) from the REST-meta-MDD consortium. Voxel-based morphometry was applied to generate GMV maps. SBM was used to adaptively parcellate brain into different components, and SCN was constructed based on SBM components. Volume scores in each component and SCNs between the components were both compared between MDD and HC groups, as well as between first-episode drug-naive (FEDN) and recurrent MDD subgroups.

RESULTS

SBM identified 20 stable components. Three components encompassing the middle temporal gyrus, middle orbitofrontal gyrus and superior frontal gyrus exhibited volumetric differences between the MDD and HC groups. Volume differences were observed in the cingulate cortex and medial frontal gyrus between the FEDN and recurrent groups. SCN analysis revealed 9 aberrant pairs in MDD vs. HCs, and 7 pairs in FEDN vs. recurrent groups. All aberrant component pairs in the SCN implicated the prefrontal cortex.

CONCLUSIONS

These findings demonstrated brain structural deficits in MDD, and highlighted the prefrontal cortex as a central hub of SCN alterations. Our findings advance the understanding of MDD's neural mechanisms and suggest directions for diagnostic research.

Differences in cognitive deficits and brain functional impairments between patients with first-episode and recurrent depression

BACKGROUND

Accumulating evidence shows that cognitive deficits are common in patients with major depressive disorder (MDD). However, the specific differences in cognitive impairment and brain functional alterations between first-episode depression (FED) and recurrent major depression (RMD) remain unclear, as do the relationships among these factors.

METHODS

A total of 43 RMD and 41 FED patients were included in this study. All the patients underwent examinations of resting-state functional magnetic resonance imaging (fMRI), event-related potential (ERP) measurements, and a series of standardised neuropsychological tests, including event-based (EBPM) and time-based (TBPM) prospective memory tasks, the Semantic Fluency Test (SFT), and the Continuous Performance Task-Identical Pairs (CPT-IP). Two-sample t-tests were used to compare cognitive functioning, ERP parameters, and brain functional indices between FED and RMD groups. Correlation analyses were performed to explore the associations between these variables.

RESULTS

Compared with FED patients, those with RMD displayed poorer CPT-IP performance, lower prospective memory (EBPM) scores, lower SFT performance, and prolonged P300 latency (all P < 0.05). Moreover, neuroimaging data analysis revealed increased regional neural activity in the right inferior temporal gyrus (ITG), alongside decreased interhemispheric functional connectivity in the bilateral ITG in RMD relative to FED. Correlation analyses indicated that these functional changes were significantly associated with the observed cognitive deficits.

CONCLUSION

Our data demonstrated more pronounced cognitive deficits and brain functional impairments in RMD relative to FED as well as their potential links. These findings not only elucidate the neural mechanisms underlying cognitive deficits in MDD, but also inform future treatment and prevention of cognitive dysfunction in patients suffering from MDD.

CLINICAL TRIAL NUMBER

Not applicable.

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis

Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods either combine clinical information to assist analysis or use data augmentation for sample expansion, ignoring the mining of individual information and the exploration of inter-individual associations in population. To solve these problems, this work proposes a novel approach for detecting abnormal neural circuits associated with brain diseases, named Topology-guided Graph Masked autoencoder Learning method (TGML), which focuses on individual representation and intra-population association, to achieve the effective diagnosis of brain diseases within the population. Concretely, the TGML comprises 1) the topology-guided group association module (T ${G}^{{2}}$ AM) that reconstructs the edges and update the initial population graph, 2) the intra-population interaction masked autoencoder network (IPI_MAE) captures the discriminative characteristics of subjects based on the novel Masked Autoencoder, which incorporates traditional masked autoencoders into a task-related process. The proposed method is evaluated on two neurodevelopmental disorder diagnosis tasks of Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). The results show that the proposed TGML achieves significant improvements and surpasses the state-of-the-art methods.

Seed-based resting-state connectivity as a neurosignature in fibromyalgia and depression: a narrative systematic review

Background

Major depressive disorder (MDD) often co-occur with fibromyalgia (FM), and both conditions have been associated with impaired resting state functional connectivity (rs-FC). The present systematic review aims to summarize the evidence on rs-FC in individuals with MDD and FM compared with healthy controls and explore overlapping connectivity patterns and their relationships with clinical symptoms.

Methods

A systematic search of the EMBASE, PubMed, Scopus and ScienceDirect databases was conducted according to PRISMA guidelines. Studies were included that addressed rs-FC using seed-based analysis in MDD and FM patients compared to HC. Methodological quality and risk of bias were assessed using a 13-point checklist adapted from previous neuroimaging meta-analyzes.

Results

A total of 33 articles were included in the analysis (17 with MDD and 16 with FM). The sample comprised 1,877 individuals, including 947 patients and 930 controls, with a mean age of 39.83 years. The seeds were categorized into six neural networks. Shared disruptions across MDD and FM studies have been identified in key circuits, including decreased connectivity between the insula and anterior cingulate cortex (ACC), middle frontal gyrus (MFG), superior frontal gyrus (SFG), and putamen. Increased FC was observed between the dorsolateral prefrontal cortex (DLPFC) and ACC, as well as between the thalamus and precuneus. Decreased insula-ACC connectivity correlated with greater pain intensity and catastrophizing in FM and with more severe depressive symptoms in MDD. Unique patterns of rs-FC were also observed: FM-specific changes involved the periaqueductal gray, hypothalamus, and thalamus, indicating impaired pain modulation and emotional processing. In contrast, MDD-specific changes were primarily observed in the reward, salience, and default mode networks, reflecting impaired emotional regulation. The studies showed considerable heterogeneity in the selection of seeds and study designs, which limits the feasibility of meta-analyses and underlines the need for standardized methods.

Findings

This study provides information about overlapping and distinct neural mechanisms in FM and MDD, suggesting potentially the presence of a potential neurosignature that reflects shared disruptions in pain and emotion regulation networks while highlighting unique pathways underlying their respective pathophysiology.

Depicting Coupling Between Cortical Morphology and Functional Networks in Major Depressive Disorder

An enduring mystery in neuroscience is the intricate interplay between brain anatomical structure and functional dynamics, particularly in the context of mental disorders such as major depressive disorder (MDD). A pivotal scientific question arises: How does the cortical morphology-function coupling (MFC) manifest in MDD, and what insights can this coupling provide into the clinical manifestations of the disorder? To tackle this question, we conducted a comprehensive analysis using high-resolution T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional MRI (rs-fMRI) data from a cohort of 830 MDD patients and 853 healthy control (HC). By constructing morphological and functional networks based on cortical gray matter (GM) morphology and regional rs-fMRI time series correlations, respectively, we aimed to quantify MFC by assessing the spatial correspondence between these networks. Results revealed that MDD patients exhibited a spatial hierarchical pattern of MFC similar to HC, with variations in specific networks. Specifically, lower coupling was observed in the visual network (VIS) and sensorimotor network (SMN), while higher coupling was noted in the default mode network (DMN) and frontoparietal network (FPN). Notably, MDD patients demonstrated significantly increased MFC within the VIS, SMN, and dorsal attention network (DAN) compared to HC. Furthermore, altered MFC in the VIS correlated positively with depressive symptom severity. These findings contribute to our understanding of the potential clinical significance of MFC alterations in MDD.

Ketamine-induced static and dynamic functional connectivity changes are modulated by opioid receptors and biological sex in rats

Subanesthetic ketamine is currently used as a rapid-acting treatment for varied neuropsychiatric disorders. However, the mechanistic underpinnings of its therapeutic action remain unclear, and emerging clinical and preclinical evidence highlights a potential involvement of the opioid system. We used pharmacological functional ultrasound imaging data acquired during and after ketamine administration in male and female rats pretreated with naltrexone, an opioid receptor antagonist, or vehicle. We found that ketamine-induced functional connectivity changes are modulated by opioid receptor blockade, and that these responses are dependent on biological sex. Specifically, naltrexone sex-dependently altered the connectivity patterns within the medial prefrontal cortex (mPFC), a key node of the brain's default-mode network, and between the mPFC and other functional nodes. Furthermore, ketamine produced an opioid-dependent shift toward states of increased dysconnectivity and brain entropy in male rats only. Our findings warrant further investigation into the neurophysiological underpinnings of ketamine action and potential sex-specific interactions with opioid receptors.

Altered brain network dynamics during rumination in remitted depression

Rumination is a known risk factor for depression relapse. Understanding its neurobiological mechanisms during depression remission can inform strategies to prevent relapse, yet the temporal dynamics of brain networks during rumination in remitted depression remain unclear. Here, we collected rumination induction fMRI data from 42 patients with remitted depression and 41 healthy controls (HCs). Using an energy landscape approach, we investigated the temporal dynamics of brain networks during rumination. The appearance frequency (AF) and transition frequency (TF) metrics were defined to quantify the dynamic properties of brain states. Patients during remission showed higher levels of rumination than HCs. Both groups exhibited four brain states during rumination, which consisted of complementary network group activation (states 1 and 2, states 3 and 4). In patients, the AFs of and reciprocal TFs between states 1 and 2 during rumination were significantly increased, while AFs of states 3 and 4 and reciprocal TFs involving states 1-3, 1-4, 2-3, and 2-4 were decreased, both when compared to HCs and relative to patients themselves during distraction. Moreover, we found that for patients, the AF of state 1 was negatively correlated with rumination levels and marginally positively associated with attention, while the AF of state 2 was negatively associated with performance on attention tasks. Our study revealed altered dynamic characteristics of brain states composed of network groups during rumination in remitted depression. Additionally, the findings suggest that heightened self-focus linked to rumination may impair the brain's ability to efficiently allocate attentional resources.

Altered connection properties of the left dorsolateral superior frontal gyrus in de novo drug-naïve insomnia disorder

Background

Insomnia disorder (ID) is increasingly prevalent, posing significant risks to patients' physical and mental health. However, its neuropathological mechanisms remain unclear. Despite extensive research on ID using resting-state functional magnetic resonance imaging, a unified framework for describing its brain function alterations remains absent. Moreover, most prior studies have not fully accounted for the potential impact of medication on outcomes regarding enrollment criteria.

Methods

We recruited 22 ID and 22 healthy controls (HC), matched for age and gender. Patients with ID were never prescribed medications for sleep disorders before enrollment. We detected differences in voxel-wise degree centrality (DC) between the two groups and analyzed the correlation between altered DC values and insomnia severity. Additionally, we conducted receiver operating characteristic analysis to evaluate the diagnostic effectiveness of the altered DC values for ID.

Results

In ID patients, the weighted DC values of the left dorsolateral superior frontal gyrus (SFG) and the left supramarginal gyrus (SMG) were significantly lower than those of HC, with a notable negative correlation between the weighted DC values of the left dorsolateral SFG and PSQI scores. Receiver operating characteristic analysis showed that the weighted DC of the left dorsolateral SFG effectively differentiates between ID and HC, exhibiting high sensitivity and specificity.

Conclusion

This study offers new insights into brain dysfunction and the pathophysiology of ID through voxel-based DC measurements. The results indicate that altered DC properties of the left dorsolateral SFG might serve as a diagnostic marker for ID and a potential therapeutic target for brain function modulation.

Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysis

BACKGROUND

Current interventions for major depressive disorder (MDD) demonstrate limited and heterogeneous efficacy, highlighting the need for improving the precision of treatment. Although findings have been mixed, resting-state functional connectivity (rsFC) at baseline shows promise as a predictive biomarker. This meta-analysis evaluates the evidence for baseline rsFC as a predictor of treatment outcomes of MDD interventions.

METHOD

We included MDD literature published between 2012 and 2024 that used antidepressants, non-invasive brain stimulation, and cognitive behavioral therapy. Pearson correlations or their equivalents were analyzed between baseline rsFC and treatment outcome. Nodes were categorized according to the type of brain networks they belong to, and pooled coefficients were generated for rsFC connections reported by more than three studies.

RESULT

Among the 16 included studies and 892 MDD patients, data from nine studies were used to generate pooled coefficients for the rsFC connection between the frontoparietal network (FPN) and default mode network (DMN), and within the DMN (six studies each, with three overlapping studies, involving 534 and 300 patients, respectively). The rsFC between the DMN and FPN had a pooled predictability of -0.060 (p = 0.171, fixed effect model), and the rsFC within the DMN had a pooled predictability of 0.207 (p < 0.001, fixed effect model). The rsFC between the DMN and FPN and the rsFC within the DMN had a larger effect in predicting the outcome of non-invasive brain stimulation (-0.215, p < 0.001, fixed effect model) and antidepressants (0.315, p < 0.001, fixed effect model), respectively. Heterogeneity was observed in both types of rsFC, study design, sample characteristics and data analysis pipeline.

CONCLUSION

Baseline rsFC within the DMN and between the DMN and FPN demonstrated a small but differential predictive effect on the outcome of antidepressants and non-invasive brain stimulation, respectively. The small predictability of rsFC suggested that rsFC between the FPN and DMN and the rsFC within the DMN might not be a good biomarker for predicting treatment outcome. Future research should focus on exploring treatment-specific predictions of baseline rsFC and its predictive utility for other types of MDD interventions.

TRIAL REGISTRATION

The review was pre-registered at PROSPERO CRD42022370235 (33).

Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental Disorders

The Hopf whole-brain model, based on structural connectivity, overcomes limitations of traditional structural or functional connectivity-focused methods by incorporating heterogeneity parameters, quantifying dynamic brain characteristics in healthy and diseased states. Traditional parameter fitting techniques lack precision, restricting broader use. To address this, we validated parameter fitting methods using simulated networks and synthetic models, introducing improvements such as individual-specific initialization and optimized gradient descent, which reduced individual data loss. We also developed an approximate loss function and gradient adjustment mechanism, enhancing parameter fitting accuracy and stability. Applying this refined method to datasets for major depressive disorder (MDD) and autism spectrum disorder (ASD), we identified differences in brain regions between patients and healthy controls, explaining related anomalies. This rigorous validation is crucial for clinical application, paving the way for precise neuropathological identification and novel treatments in neuropsychiatric research, demonstrating substantial potential in clinical neurology.

Age-related abnormalities in brain functional and molecular neuroimaging signatures in first-episode depression

Abnormalities in resting-state brain activity have been demonstrated in depression patients with different ages, yet the age-related changes in dynamics of brain activity in depression are still limited. Here, we investigated the impacts of age on dynamics of brain activity and the molecular architecture. Resting-state functional magnetic resonance images were obtained from 138 first-episode depression patients and 120 healthy volunteers. All the participants were classified into two age cohorts, including adolescents and adults. Two-way analysis of variance was performed to examine the effect of age on dynamic amplitude of low-frequency fluctuations (ALFF) in depression. Then, cross-modal correlation analyses between dynamic ALFF and neurotransmitter maps were established. Significant diagnosis-by-age interaction of dynamic ALFF was located in medial frontal gyrus, supplementary motor area, postcentral gyrus, paracentral lobule and rolandic operculum. Dynamic ALFF alterations in the diagnosis-by-age interaction effect were associated with serotonergic, dopaminergic, noradrenergic, and GABAergic systems. These findings highlight the interaction between depression and age in brain functional and molecular neuroimaging signatures, which may be useful for future treatment strategies of different ages of depression.

Transcriptional Patterns of Nodal Entropy Abnormalities in Major Depressive Disorder Patients with and without Suicidal Ideation

Previous studies have indicated that major depressive disorder (MDD) patients with suicidal ideation (SI) present abnormal functional connectivity (FC) and network organization in node-centric brain networks, ignoring the interactions among FCs. Whether the abnormalities of edge interactions affect the emergence of SI and are related to the gene expression remains largely unknown. In this study, resting-state functional magnetic resonance imaging (fMRI) data were collected from 90 first-episode, drug-naive MDD with suicidal ideation (MDDSI) patients, 60 first-episode, drug-naive MDD without suicidal ideation (MDDNSI) patients, and 98 healthy controls (HCs). We applied the methodology of edge-centric network analysis to construct the functional brain networks and calculate the nodal entropy. Furthermore, we examined the relationships between nodal entropy alterations and gene expression. The MDDSI group exhibited significantly lower subnetwork entropy in the dorsal attention network (DAN) and significantly greater subnetwork entropy in the default mode network than the MDDNSI group. The visual learning score of the measurement and treatment research to improve cognition in schizophrenia (MATRICS) consensus cognitive battery was negatively correlated with the subnetwork entropy of DAN in the MDDSI group. The support vector machine model based on nodal entropy achieved an accuracy of 81.87% when distinguishing the MDDNSI and MDDSI. Additionally, the changes in SI-related nodal entropy were associated with the expression of genes in cell signaling and interactions, as well as immune and inflammatory responses. These findings reveal the abnormalities in nodal entropy between the MDDSI and MDDNSI groups, demonstrated their association with molecular functions, and provided novel insights into the neurobiological underpinnings and potential markers for the prediction and prevention of suicide.

Self-supervised learning for graph-structured data in healthcare applications: A comprehensive review

The increasing complexity and interconnectedness of healthcare data present numerous opportunities to improve prediction, diagnosis, and treatment. Graph-structured data, which represents entities and their relationships, is well-suited for modeling these complex connections. However, effectively utilizing this data often requires strong and efficient learning algorithms, especially when dealing with limited labeled data. Self-supervised learning (SSL) has emerged as a powerful paradigm for leveraging unlabeled data to learn effective representations. This paper presents a comprehensive review of SSL approaches specifically designed for graph-structured data in healthcare applications. We explore the challenges and opportunities associated with healthcare data and assess the effectiveness of SSL techniques in real-world healthcare applications. Our discussion encompasses various healthcare settings, such as disease prediction, medical image analysis, and drug discovery. We critically evaluate the performance of different SSL methods across these tasks, highlighting their strengths, limitations, and potential future research directions. To the best of our knowledge, this is the first comprehensive review of SSL applied to graph data in healthcare, providing valuable guidance for researchers and practitioners looking to leverage these techniques to enhance outcomes and drive progress in the field.

Regional Gene Expression Patterns are Associated with Functional Connectivity Alterations in Major Depressive Disorder with Anxiety Symptoms

Background

Understanding gene expression and functional connectivity (FC) changes in depressed patients with anxiety can help develop personalized therapies. Herein we examine the link between transcriptome data and FC differences in patients with major depressive disorder with significant anxiety (MDD/ANX+) and patients with major depressive disorder without significant anxiety (MDD/ANX-).

Methods

We compared the FC between the MDD/ANX+ group (n = 294) and the MDD/ANX- group (n = 218) to identify FC differences at both edge-based and network levels. Using the Allen Human Brain Atlas, we performed partial least squares regression analysis to identify genes associated with the observed FC disparities, followed by a functional enrichment analysis.

Results

The results from both edge-based and network-level FC analyses consistently indicated significantly increased FC between the subcortical network (SC) and visual network, as well as between the SC and dorsal attention network, in the MDD/ANX+ group compared with the MDD/ANX- group. Additionally, transcriptome-neuroimaging correlation analysis revealed that the expression of 1066 genes was spatially correlated with the FC differences between the MDD/ANX+ and MDD/ANX- groups. These genes were enriched in translation at synapses and adenosine triphosphate (ATP) generation.

Conclusions

Our results indicate that gene expression variations in synaptic translation and ATP generation may affect FC and anxiety risk in MDD patients.

The Impact of Atlas Parcellation on Functional Connectivity Analysis Across Six Psychiatric Disorders

Neuropsychiatric disorders are associated with altered functional connectivity (FC); however, the reported regional patterns of functional alterations suffered from low replicability and high variability. This is partly because of differences in the atlas and delineation techniques used to measure FC-related deficits within/across disorders. We systematically investigated the impact of the brain parcellation approach on the FC-based brain network analysis. We focused on identifying the replicable FCs using three structural brain atlases, including Automated Anatomical Labeling (AAL), Brainnetome atlas (BNA) and HCP_MMP_1.0, and four functional brain parcellation approaches: Yeo-Networks (Yeo), Gordon parcel (Gordon) and two Schaefer parcelletions, among correlation, group difference, and classification tasks in six neuropsychiatric disorders: attention deficit and hyperactivity disorder (ADHD, n = 340), autism spectrum disorder (ASD, n = 513), schizophrenia (SZ, n = 200), schizoaffective disorder (SAD, n = 142), bipolar disorder (BP, n = 172), and major depression disorder (MDD, n = 282). Our cross-atlas/disorder analyses demonstrated that frontal-related FC deficits were reproducible in all disorders, independent of the atlasing approach; however, replicable FC extraction in other areas and the classification accuracy were affected by the parcellation schema. Overall, functional atlases with finer granularity performed better in classification tasks. Specifically, the Schaefer atlases generated the most repeatable FC deficit patterns across six illnesses. These results indicate that frontal-related FCs may serve as potential common and robust neuro-abnormalities across 6 psychiatric disorders. Furthermore, in order to improve the replicability of rsfMRI-based FC analyses, this study suggests the use of functional templates at larger granularity.

Default Mode Network Functional Connectivity As a Transdiagnostic Biomarker of Cognitive Function

The default mode network (DMN) is intricately linked with processes such as self-referential thinking, episodic memory recall, goal-directed cognition, self-projection, and theory of mind. In recent years, there has been a surge in the number of studies examining its functional connectivity, particularly its relationship with frontoparietal networks involved in top-down attention, executive function, and cognitive control. The fluidity in switching between these internal and external modes of processing, which is highlighted by anticorrelated functional connectivity, has been proposed as an indicator of cognitive health. Due to the ease of estimation of functional connectivity-based measures through resting-state functional magnetic resonance imaging paradigms, there is now a wealth of large-scale datasets, paving the way for standardized connectivity benchmarks. In this review, we explore the promising role of DMN connectivity metrics as potential biomarkers of cognitive state across attention, internal mentation, mind wandering, and meditation states and investigate deviations in trait-level measures across aging and in clinical conditions such as Alzheimer's disease, Parkinson's disease, depression, attention-deficit/hyperactivity disorder, and others. We also tackle the issue of reliability of network estimation and functional connectivity and share recommendations for using functional connectivity measures as a biomarker of cognitive health.

Effect of esketamine on postoperative depression in women with breast cancer and preoperative depressive symptoms: The EASE randomized trial

STUDY OBJECTIVE

To determine whether intraoperative low-dose esketamine ameliorates depression in women having breast cancer surgery.

DESIGN

A prospective single-center double blind randomized placebo-controlled trial.

SETTING

Perioperative period, operating room, post anesthesia care unit and hospital ward.

PATIENTS

108 women 18-65 years old who were scheduled for elective breast cancer surgery. All had preoperative depressive symptoms as defined by Montgomery-Åsberg depression scores ≥12 (range, 0-60; higher scores indicate more severe depression).

INTERVENTIONS

Eligible participants were randomized to esketamine 0.25 mg/kg or saline placebo. Blinded trial drugs were given intravenously over the initial 40 min of anesthesia.

MEASUREMENTS

Our primary outcome was the fraction of patients who had at least a 50 % reduction in the Montgomery-Åsberg depression score within 3 postoperative days. Secondary outcomes included the fraction of patients with depression remission defined as Montgomery-Åsberg scores ≤10, the numeric value of the Montgomery-Åsberg depression scores, postoperative severe pain, and anxiety as determined by the Generalized Anxiety Disorder 7-item score. Adverse events were monitored for 72 postoperative hours.

MAIN RESULTS

54 women each were randomized to esketamine and saline, and 104 were available for our intent-to-treat analysis. The mean age was 50 years. Esketamine non-significantly doubled the fraction of patients who had a 50 % reduction in their depressions scores: 27 % vs 13 %, odds ratio 2.4, [95 % CI 0.9 to 6.6], P = 0.087. Montgomery-Åsberg depression scores were nearly a factor-of-two and significantly lower (better) on postoperative days 1 to 5 in patients given esketamine. Montgomery-Åsberg scores decreased significantly more from baseline in patients randomized to esketamine: mean difference - 2.5 [95 % CI -4.5 to -0.6], P = 0.010. Esketamine treatment had no significant effect on other secondary outcomes or on adverse events.

CONCLUSIONS

Intraoperative administration of 0.25 mg/kg esketamine did not significantly improve the fraction of depressed women having breast cancer patients who had a 50 % reduction in their depression scores at 3 days postoperatively. However, the observed factor-of-two treatment effect was clinically meaningful and esketamine significantly reduced short-term postoperative depression scores without provoking complications. Robust trials are warranted. Registration Trial registry:http://www.chictr.org.cn/; Identifier: ChiCTR2300071062.

DMN network and neurocognitive changes associated with dissociative symptoms in major depressive disorder: a research protocol

Introduction

Depression is a heterogeneous disorder with diverse clinical presentations and etiological underpinnings, necessitating the identification of distinct subtypes to enhance targeted interventions. Dissociative symptoms, commonly observed in major depressive disorder (MDD) and linked to early life trauma, may represent a unique clinical dimension associated with specific neurocognitive deficits. Although emerging research has begun to explore the role of dissociation in depression, most studies have provided only descriptive analyses, leaving the mechanistic interplay between these phenomena underexplored. The primary objective of this study is to determine whether MDD patients with prominent dissociative symptoms differ from those without such symptoms in clinical presentation, neurocognitive performance, and markers of functional connectivity. This investigation will be the first to integrate comprehensive clinical evaluations, advanced neurocognitive testing, and high-resolution brain imaging to delineate the contribution of dissociative symptoms in MDD.

Methods

We will recruit fifty participants for each of three groups: (1) depressive patients with dissociative symptoms, (2) depressive patients without dissociative symptoms, and (3) healthy controls. Diagnostic assessments will be performed using the Structured Clinical Interview for DSM-5 (SCID) alongside standardized scales for depression severity, dissociation, and childhood trauma. Neurocognitive performance will be evaluated through a battery of tests assessing memory, attention, executive function, and processing speed. Structural and functional magnetic resonance imaging (MRI) will be conducted on a 3 Tesla scanner, focusing on the connectivity of the Default Mode Network with key regions such as the orbitofrontal cortex, insula, and posterior cingulate cortex. Data analyses will employ SPM-12 and Matlab-based CONN and PRONTO tools, with multiclass Gaussian process classification applied to differentiate the three groups based on clinical, cognitive, and imaging data.

Discussion

The results of this study will introduce a novel perspective on understanding the connection between major depressive disorder and dissociation. It could also aid in pinpointing a distinct form of depression associated with dissociative symptoms and early childhood stressors.

Conclusion

Future research, aiming to forecast the response to biological and psychological interventions for depression, anticipates this subtype and provides insights.

Multivariate Classification of Adolescent Major Depressive Disorder Using Whole-brain Functional Connectivity

RATIONALE AND OBJECTIVES

Adolescent major depressive disorder (MDD) is a serious mental health condition that has been linked to abnormal functional connectivity (FC) patterns within the brain. However, whether FC could be used as a potential biomarker for diagnosis of adolescent MDD is still unclear. The aim of our study was to investigate the potential diagnostic value of whole-brain FC in adolescent MDD.

METHODS

Resting-state functional magnetic resonance imaging data were obtained from 94 adolescents with MDD and 78 healthy adolescents. The whole brain was segmented into 90 regions of interest (ROIs) using the automated anatomical labeling atlas. FC was assessed by calculating the Pearson correlation coefficient of the average time series between each pair of ROIs. A multivariate pattern analysis was employed to classify patients from controls using the whole-brain FC as input features.

RESULTS

The linear support vector machine classifier achieved an accuracy of 69.18% using the optimal functional connection features. The consensus functional connections were mainly located within and between large-scale brain networks. The top 10 nodes with the highest weight in the classification model were mainly located in the default mode, salience, auditory, and sensorimotor networks.

CONCLUSION

Our findings highlighted the importance of functional network connectivity in the neurobiology of adolescent MDD, and suggested the possibility of altered FC and high-weight regions as complementary diagnostic markers in adolescents with depression.

Effects of acute sleep deprivation on the brain function of individuals with migraine: a resting-state functional magnetic resonance imaging study

BACKGROUND

Sleep deprivation can trigger acute headache attacks in individuals with migraine; however, the underlying mechanism remains poorly understood. The aim of this study was to investigate the effects of acute sleep deprivation (ASD) on brain function in individuals with migraine without aura (MWoA) via functional magnetic resonance imaging (fMRI).

METHODS

Twenty three MWoA individuals and 23 healthy controls (HCs) were fairly included in this study. All participants underwent two MRI scans: one at baseline (prior to sleep deprivation) and another following 24 h of ASD. Images were obtained with blood-oxygen-level-dependent and T1-weighted sequences on a Siemens 7.0 T MRI scanner. We conducted analyses of changes in the low-frequency fluctuations (ALFF) values and functional connectivity (FC) between brain networks and within network before and after ASD in both MWoA group and HC group. Additionally, we investigated the relationship between the changes in ALFF before and after ASD and the clinical features (VAS and monthly headache days).

RESULTS

In the HC group, ASD led to a significant increase in ALFF values in the left parahippocampal gyrus compared to baseline (p-FDR = 0.01). In the MWoA group, ALFF values were significantly greater in 64 brain regions after ASD than at baseline. The most significant change in ALFF before and after ASD in the MWoA group was detected in the right medial pulvinar of the thalamus (p-FDR = 0.017), which showed a significant negative correlation with monthly headache days. Moreover, seed-based connectivity (SBC) analysis using the right medial pulvinar of the thalamus as the seed point revealed significantly increased connectivity with the cerebellar vermis (p-FWE = 0.035) after ASD in individuals with MWoA, whereas connectivity with the right postcentral gyrus was significantly decreased (p-FWE = 0.048). Furthermore, we performed analyses of between-network connectivity (BNC) and within-network connectivity across 17 brain networks, utilizing the Yeo-17 atlas. Both MWoA individuals and HCs showed no significant changes in BNC after ASD compared to baseline. However, our analysis in within-network revealed that MWoA individuals exhibited a reduced within-network FC in dorsal attention network (DAN) after ASD compared to baseline (p-FDR = 0.031), whereas HCs showed no significant differences in within-network FC across all networks before and after ASD.

CONCLUSIONS

In comparison to HCs, MWoA individuals exhibited significant alterations in brain function after ASD, particularly within the thalamus, and MWoA individuals exhibited a reduced within-network FC in DAN after ASD compared to baseline. Brain regions and networks in MWoA individuals were more susceptible to the effects of ASD.

Abnormal Structure-Function Coupling in Major Depressive Disorder Patients With and Without Anhedonia

Background: As a core symptom of major depressive disorder (MDD), previous magnetic resonance studies have demonstrated that MDD with anhedonia may exhibit distinctive brain structural and functional alterations. Nevertheless, the impact of anhedonia on synchronized alterations in the structure and function of brain regions in MDD remains uncertain. Methods: A total of 92 individuals were enrolled in the study, including 29 MDD patients with anhedonia, 33 MDD patients without anhedonia, and 30 healthy controls (HCs). All subjects underwent structural and resting-state functional magnetic resonance imaging (MRI) scans. The structure-function coupling of cortical and subcortical regions was constructed by using the obtained structural and functional data to quantify the distributional similarity of gray matter volume (GMV) and amplitude of low-frequency fluctuations (ALFFs). Analysis of covariance (ANCOVA) was used to compare differences in structure-function coupling among the three groups. Partial correlation analyses were conducted to identify relationships between structure-function coupling and clinical features. Finally, receiver operating characteristic (ROC) curve and support vector machine (SVM) analysis were employed to verify the capacity to distinguish between MDD with anhedonia and MDD without anhedonia, MDD with anhedonia and HCs, and MDD without anhedonia and HCs. Results: The ANCOVA revealed significant differences in structure-function coupling among three groups in the bilateral precentral gyrus (PrG), right insular gyrus (INS), right cingulate gyrus (CG), right thalamus (Tha), left superior temporal gyrus (STG), and left middle temporal gyrus (MTG). Compared to HCs, both MDD groups showed reduced coupling in the right INS, bilateral PrG, while increased coupling in the right CG. Additionally, MDD with anhedonia showed reduced coupling in the right Tha, right PrG, and left MTG, while increased coupling in the left STG, compared to the other two groups. Furthermore, ROC analyses indicated that structure-function coupling in the right PrG, right CG, and left MTG exhibited the greatest capacity to distinguish between the following groups: MDD with anhedonia from HCs, MDD without anhedonia from HCs, and MDD with anhedonia from MDD without anhedonia. The combined metrics demonstrated greater diagnostic value in two-by-two comparisons. Conclusion: The present findings highlight that altered structure-function synchrony in the frontal, temporal lobes, and Tha may be implicated in the development of symptoms of anhedonia in MDD patients. Altered structure-function coupling in the aforementioned brain regions may serve as a novel neuroimaging biomarker for MDD with anhedonia.

Over-integration of visual network in major depressive disorder and its association with gene expression profiles

Major depressive disorder (MDD) is a common psychiatric condition associated with aberrant functional connectivity in large-scale brain networks. However, it is unclear how the network dysfunction is characterized by imbalance or derangement of network modular interaction in MDD patients and whether this disruption is associated with gene expression profiles. We included 262 MDD patients and 297 healthy controls, embarking on a comprehensive analysis of intrinsic brain activity using resting-state functional magnetic resonance imaging (R-fMRI). We assessed brain network integration by calculating the Participation Coefficient (PC) and conducted an analysis of intra- and inter-modular connections to reveal the dysconnectivity patterns underlying abnormal PC manifestations. Besides, we explored the potential relationship between the above graph theory measures and clinical symptoms severity in MDD. Finally, we sought to uncover the association between aberrant graph theory measures and postmortem gene expression data sourced from the Allen Human Brain Atlas (AHBA). Relative to the controls, alterations in systemic functional connectivity were observed in MDD patients. Specifically, increased PC within the bilateral visual network (VIS) was found, accompanied by elevated functional connectivities (FCs) between VIS and both higher-order networks and Limbic network (Limbic), contrasted by diminished FCs within the VIS and between the VIS and the sensorimotor network (SMN). The clinical correlations indicated positive associations between inter-VIS FCs and depression symptom, whereas negative correlations were noted between intra-VIS FCs with depression symptom and cognitive disfunction. The transcriptional profiles explained 21-23.5% variance of the altered brain network system dysconnectivity pattern, with the most correlated genes enriched in trans-synaptic signaling and ion transport regulation. These results highlight the modular connectome dysfunctions characteristic of MDD and its linkage with gene expression profiles and clinical symptomatology, providing insight into the neurobiological underpinnings and holding potential implications for clinical management and therapeutic interventions in MDD.

Increased individual variability in functional connectivity of the default mode network and its genetic correlates in major depressive disorder

Major depressive disorder (MDD) is a highly heterogeneous psychiatric disorder characterized with considerable individual variability in clinical manifestations which may correspond to brain alterations including the default mode network (DMN). This study analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 796 MDD patients and 823 healthy controls (HC) to investigate individual variability in functional connectivity (IVFC) between the DMN and 108 non-DMN regions. We aimed to identify MDD-related IVFC abnormalities and their clinical relevance, alongside exploring gene expression correlations. The results revealed similar spatial patterns of IVFC within the DMN in both groups, yet significantly increased IVFC values in MDD patients were observed in regions such as the ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, fusiform gyrus, and occipital cortex. Notably, the mean IVFC in the DMN and fusiform gyrus was positively correlated with Hamilton Rating Scale for Depression (HAMD) scores in MDD patients. Gene expression analyses explained 47.0% of the variance in MDD-related IVFC alterations, with the most associated genes enriched in processes including membrane potential regulation, head development, synaptic transmission, and dopaminergic synapse. These findings highlight the clinical importance of IVFC variability in the DMN and suggest its potential role as a biomarker in MDD.

The causal association between resting state intrinsic functional networks and neurodegeneration

Alterations of resting state intrinsic functional networks have been associated with neurodegenerative diseases even before the onset of cognitive symptoms. Emerging hypotheses propose a role of resting state intrinsic functional networks alterations in the risk or vulnerability to neurodegeneration. It is unknown whether intrinsic functional network alterations can be causal for neurodegenerative diseases. We sought to answer this question using two-sample Mendelian randomization. Using the largest genome-wide association study of resting state intrinsic functional connectivity (n = 47 276), we generated genetic instruments (at the significance level 2.8 ×10-11) to proxy resting state intrinsic functional network features. Based on the known brain regions implicated in different neurodegenerative diseases, we generated genetically proxied resting state intrinsic functional features and tested their association with their paired neurodegenerative outcomes: features in parieto-temporal regions and Alzheimer dementia (111 326 cases, 677 663 controls); frontal region and frontotemporal dementia (2154 cases, 4308 controls); temporal pole region and semantic dementia (308 cases, 616 controls), and occipital region with Lewy body dementia (LBD) (2591 cases, 4027 controls). Major depressive disorder outcome (170 756 cases, 329 443 controls) was included as a positive control and tested for its association with genetically proxied default mode network (DMN) exposure. Inverse-variance weighted analysis was used to estimate the association between the exposures (standard deviation units) and outcomes. Power and sensitivity analyses were completed to assess the robustness of the results. None of the genetically proxied functional network features were significantly associated with neurodegenerative outcomes (adjusted P value >0.05), despite sufficient calculated power. Two resting state features in the visual cortex showed a nominal level of association with LBD (P = 0.01), a finding that was replicated using a different instrument (P = 0.03). The genetically proxied DMN connectivity was associated with the risk of depression (P = 0.024), supporting the validity of the genetic instruments. Sensitivity analyses were supportive of the main results. This is the first study to comprehensively assess the potential causal effect of resting state intrinsic functional network features on the risk of neurodegeneration. Overall, the results do not support a causal role for the tested associations. However, we report a nominal association between visual network connectivity and Lewy body dementia that requires further evaluation.

Disrupted small-world architecture and altered default mode network topology of brain functional network in college students with subclinical depression

BACKGROUND

Subclinical depression (ScD), serving as a significant precursor to depression, is a prevalent condition in college students and imposes a substantial health service burden. However, the brain network topology of ScD remains poorly understood, impeding our comprehension of the neuropathology underlying ScD.

METHODS

Functional networks of individuals with ScD (n = 26) and healthy controls (HCs) (n = 33) were constructed based on functional magnetic resonance imaging data. These networks were then optimized using a small-worldness and modular similarity-based network thresholding method to ensure the robustness of functional networks. Subsequently, graph-theoretic methods were employed to investigated both global and nodal topological metrics of these functional networks.

RESULTS

Compared to HCs, individuals with ScD exhibited significantly higher characteristic path length, clustering coefficient, and local efficiency, as well as a significantly lower global efficiency. Additionally, significantly lower nodal centrality metrics were found in the default mode network (DMN) regions (anterior cingulate cortex, superior frontal gyrus, precuneus) and occipital lobe in ScD, and the nodal efficiency of the left precuneus was negatively correlated with the severity of depression.

CONCLUSIONS

Altered global metrics indicate a disrupted small-world architecture and a typical shift toward regular configuration of functional networks in ScD, which may result in lower efficiency of information transmission in the brain of ScD. Moreover, lower nodal centrality in DMN regions suggest that DMN dysfunction is a neuroimaging characteristic shared by both ScD and major depressive disorder, and might serve as a vital factor promoting the development of depression.

Complexity of intrinsic brain activity in relapsing-remitting multiple sclerosis patients: patterns, association with structural damage, and clinical disability

Functional plasticity has been demonstrated in multiple sclerosis (MS) studies. However, the intrinsic brain activity complexity alterations remain unclear. Here, using a coarse-graining time-series procedure algorithm, we obtained multiscale entropy (MSE) from a retrospective multi-centre dataset (208 relapsing-remitting MS patients and 228 healthy controls). By linear mixed model analysis, we demonstrated (1) increased entropy at scale 1 and decreased entropy at scale 6, indicating that regional brain activity shifted towards randomness in the stable MS subgroups (n = 159), and (2) decreased entropy across scales 1-6, trending towards regularity in the acute MS subgroups (n = 49). The main results of the correlation analysis included the following: (1) Decreased entropy was associated with lesion volume and brain volume specifically on longer time scales (scale 3-5), and (2) increased entropy of scale 3 was associated with clinical disability scores. These findings reflect the critical role of structural disruption in the brain activity complexity of BOLD signals in MS patients.

Federated Bayesian network learning from multi-site data

OBJECTIVE

Identifying functional connectivity biomarkers of major depressive disorder (MDD) patients is essential to advance the understanding of disorder mechanisms and early intervention. Multi-site data arise naturally which could enhance the statistical power of single-site-based methods. However, the main concern is the inter-site heterogeneity and data sharing barriers between different sites. Our objective is to overcome these barriers to learn multiple Bayesian networks (BNs) from rs-fMRI data.

METHODS

We propose a federated joint estimator and the corresponding optimization algorithm, called NOTEARS-PFL. Specifically, we incorporate both shared and site-specific information into NOTEARS-PFL by utilizing the sparse group lasso penalty. Addressing data-sharing constraint, we develop the alternating direction method of multipliers for the optimization of NOTEARS-PFL. This entails processing neuroimaging data locally at each site, followed by the transmission of the learned network structures for central global updates.

RESULTS

The effectiveness and accuracy of the NOTEARS-PFL method are validated through its application on both synthetic and real-world multi-site resting-state functional magnetic resonance imaging (rs-fMRI) datasets. This demonstrates its superior efficiency and precision in comparison to alternative approaches.

CONCLUSION

We proposed a toolbox called NOTEARS-PFL to learn the heterogeneous brain functional connectivity in MDD patients using multi-site data efficiently and with the data sharing constraint. The comprehensive experiments on both synthetic data and real-world multi-site rs-fMRI datasets with MDD highlight the excellent efficacy of our proposed method.

Whole-brain turbulent dynamics predict responsiveness to pharmacological treatment in major depressive disorder

Depression is a multifactorial clinical syndrome with a low pharmacological treatment response rate. Therefore, identifying predictors of treatment response capable of providing the basis for future developments of individualized therapies is crucial. Here, we applied model-free and model-based measures of whole-brain turbulent dynamics in resting-state functional magnetic resonance imaging (fMRI) in healthy controls and unmedicated depressed patients. After eight weeks of treatment with selective serotonin reuptake inhibitors (SSRIs), patients were classified as responders and non-responders according to the Hamilton Depression Rating Scale 6 (HAMD6). Using the model-free approach, we found that compared to healthy controls and responder patients, non-responder patients presented disruption of the information transmission across spacetime scales. Furthermore, our results revealed that baseline turbulence level is positively correlated with beneficial pharmacological treatment outcomes. Importantly, our model-free approach enabled prediction of which patients would turn out to be non-responders. Finally, our model-based approach provides mechanistic evidence that non-responder patients are less sensitive to stimulation and, consequently, less prone to respond to treatment. Overall, we demonstrated that different levels of turbulent dynamics are suitable for predicting response to SSRIs treatment in depression.

[The shifting territories of mental travel]

How does experience outside the present moment become part of living matter? Mental travel, which is both creative and low-carbon, is an experience that anyone can enjoy without needing to abstract from immediacy and project themselves beyond reality. The possibility of such a virtual journey has long fascinated philosophers and then scientists. What does mental travel actually involve? Which neural circuits are engaged? What are the conditions that take us on a hallucinatory journey, deprive us of it, or enable us to control it? What are the adaptive advantages of this imaginary journey? Is it present in other living beings and in our "intelligent" machines?

Functional connectivity density of postcentral gyrus predicts rumination and major depressive disorders in males

Major depressive disorder (MDD) is characterized by persistent sadness and loss of interest. Recent evidence suggested that abnormal functional connectivity (FC) may be linked to the development of MDD, and gender differences existed in FC patterns. In this study, we utilized resting-state functional magnetic resonance imaging (RS-fMRI) data from 41 healthy participants to identify FC patterns that correlate with levels of rumination in both genders. A 2-sample t-test showed no gender differences in rumination levels. A functional connectivity density (FCD) analysis was then conducted using DPABI. It was revealed that in males, the FCD of the postcentral gyrus (PCG) was negatively correlated with the levels of rumination and brooding (Pearson's r > 0.25), while not with reflection. No FCD in females was related to rumination or its subtypes. Further FC analysis revealed that the FC between the PCG and several regions, predominantly from the default mode network (DMN), were negatively correlated with rumination levels (Pearson's r > 0.25). This link was assumed to be a risk factor for rumination and MDD in males. In conclusion, our findings indicate that the PCG-DMN connectivity is a potential risk factor for MDD in males, while no FC risk factors were found in females.

Functional Connectivity Changes Associated With Depression in Dementia With Lewy Bodies

OBJECTIVES

Depressive symptoms are frequent in the early stages of dementia with Lewy bodies (DLB), and more than half of DLB patients would have a history of depression. Our study sought to investigate the functional connectivity (FC) changes associated with depressive symptoms in prodromal to mild DLB patients compared with controls.

METHODS

MRI data were collected from 66 DLB patients and 18 controls. Depression was evaluated with the Mini International Neuropsychiatric Interview. Resting-state FC (rsFC) was investigated with the CONN toolbox using a seed-based approach and both regression and comparison analyses.

RESULTS

Correlations were found between the depression scores and the rsFC between fronto-temporal and primary visual areas in DLB patients (p < 0.05, FDR corrected). Depressed DLB patients also showed decreased rsFC within the salience network (SN), increased rsFC between the default mode network (DMN) and the language network (LN) and decreased rsFC between the cerebellar network (CN) and the fronto-parietal network (FPN) compared to non-depressed DLB patients (p < 0.05, uncorrected). Comparison analyses between antidepressant-treated and non-treated DLB patients highlighted FC changes in treated patients involving the SN, the DMN, the FPN and the dorsal attentional network (p < 0.05, uncorrected).

CONCLUSIONS

Our findings revealed that depressive symptoms would especially be associated with rsFC changes between fronto-temporal and primary visual areas in DLB patients. Such alterations could contribute to difficulties in regulating emotions, processing biases towards negative stimuli, and self-focused ruminations.

TRIAL REGISTRATION

This study is part of the cohort study AlphaLewyMA (https://clinicaltrials.gov/ct2/show/NCT01876459).

Resting-state network alterations in depression: a comprehensive meta-analysis of functional connectivity

BACKGROUND

Depression has been linked to disruptions in resting-state networks (RSNs). However, inconsistent findings on RSN disruptions, with variations in reported connectivity within and between RSNs, complicate the understanding of the neurobiological mechanisms underlying depression.

METHODS

A systematic literature search of PubMed and Web of Science identified studies that employed resting-state functional magnetic resonance imaging (fMRI) to explore RSN changes in depression. Studies using seed-based functional connectivity analysis or independent component analysis were included, and coordinate-based meta-analyses were performed to evaluate alterations in RSN connectivity both within and between networks.

RESULTS

A total of 58 studies were included, comprising 2321 patients with depression and 2197 healthy controls. The meta-analysis revealed significant alterations in RSN connectivity, both within and between networks, in patients with depression compared with healthy controls. Specifically, within-network changes included both increased and decreased connectivity in the default mode network (DMN) and increased connectivity in the frontoparietal network (FPN). Between-network findings showed increased DMN-FPN and limbic network (LN)-DMN connectivity, decreased DMN-somatomotor network and LN-FPN connectivity, and varied ventral attention network (VAN)-dorsal attentional network (DAN) connectivity. Additionally, a positive correlation was found between illness duration and increased connectivity between the VAN and DAN.

CONCLUSIONS

These findings not only provide a comprehensive characterization of RSN disruptions in depression but also enhance our understanding of the neurobiological mechanisms underlying depression.

Disrupted intersubject variability architecture in structural and functional brain connectomes in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous condition characterized by significant intersubject variability in clinical presentations. Recent neuroimaging studies have indicated that MDD involves altered brain connectivity across widespread regions. However, the variability in abnormal connectivity among MDD patients remains understudied.

METHODS

Utilizing a large, multi-site dataset comprising 1,276 patients with MDD and 1,104 matched healthy controls, this study aimed to investigate the intersubject variability of structural covariance (IVSC) and functional connectivity (IVFC) in MDD.

RESULTS

Patients with MDD demonstrated higher IVSC in the precuneus and lingual gyrus, but lower IVSC in the medial frontal gyrus, calcarine, cuneus, and cerebellum anterior lobe. Conversely, they exhibited an overall increase in IVFC across almost the entire brain, including the middle frontal gyrus, anterior cingulate cortex, hippocampus, insula, striatum, and precuneus. Correlation and mediation analyses revealed that abnormal IVSC was positively correlated with gray matter atrophy and mediated the relationship between abnormal IVFC and gray matter atrophy. As the disease progressed, IVFC increased in the left striatum, insula, right lingual gyrus, posterior cingulate, and left calcarine. Pharmacotherapy significantly reduced IVFC in the right insula, superior temporal gyrus, and inferior parietal lobule. Furthermore, we found significant but distinct correlations between abnormal IVSC and IVFC and the distribution of neurotransmitter receptors, suggesting potential molecular underpinnings. Further analysis confirmed that abnormal patterns of IVSC and IVFC were reproducible and MDD specificity.

CONCLUSIONS

These results elucidate the heterogeneity of abnormal connectivity in MDD, underscoring the importance of addressing this heterogeneity in future research.

Common and disease-specific patterns of functional connectivity and topology alterations across unipolar and bipolar disorder during depressive episodes: a transdiagnostic study

Bipolar disorder (BD) and unipolar depression (UD) are defined as distinct diagnostic categories. However, due to some common clinical and pathophysiological features, it is a clinical challenge to distinguish them, especially in the early stages of BD. This study aimed to explore the common and disease-specific connectivity patterns in BD and UD. This study was constructed over 181 BD, 265 UD and 204 healthy controls. In addition, an independent group of 90 patients initially diagnosed with major depressive disorder at the baseline and then transferred to BD with the episodes of mania/hypomania during follow-up, was identified as initial depressive episode BD (IDE-BD). All participants completed resting-state functional magnetic resonance imaging (R-fMRI) at recruitment. Both network-based analysis and graph theory analysis were applied. Both BD and UD showed decreased functional connectivity (FC) in the whole brain network. The shared aberrant network across groups of patients with depressive episode (BD, IDE-BD and UD) mainly involves the visual network (VN), somatomotor networks (SMN) and default mode network (DMN). Analysis of the topological properties over the three networks showed that decreased clustering coefficient was found in BD, IDE-BD and UD, however, decreased shortest path length and increased global efficiency were only found in BD and IDE-BD but not in UD. The study indicate that VN, SMN, and DMN, which involve stimuli reception and abstraction, emotion processing, and guiding external movements, are common abnormalities in affective disorders. The network separation dysfunction in these networks is shared by BD and UD, however, the network integration dysfunction is specific to BD. The aberrant network integration functions in BD and IDE-BD might be valuable diagnostic biomarkers.

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.

Hippocampal gray matter volume alterations in patients with first-episode and recurrent major depressive disorder and their associations with gene profiles

BACKGROUND

Recent studies indicate that patients with first-episode drug-naïve (FEDN) and recurrent major depressive disorder (R-MDD) exhibit distinct atrophy patterns in the hippocampal subregions along the proximal-distal axis. However, it remains unclear whether such differences occur along the long axis and how they may relate to specific genes.

METHODS

In the present study, we analyzed T1-weighted images from 421 patients (FEDN: n = 232; R-MDD: n = 189) and 544 normal controls (NC) as part of the REST-meta-MDD consortium. Additionally, transcriptome maps and structural Magnetic Resonance Imaging (MRI) data of six donated brains were obtained from the Allen Human Brain Atlas (AHBA). We first identified changes in gray matter volume (GMV) within the hippocampus of both FEDN and R-MDD patients and then integrated these findings with AHBA transcriptome data to investigate the genes associated with hippocampal GMV changes.

RESULTS

Compared to NC, FEDN patients displayed reduced GMV in the left hippocampal tail, whereas R-MDD patients exhibited decreased GMV in the bilateral hippocampal body and increased GMV in the bilateral hippocampal tail. Further analysis revealed that expression levels of SYTL2 positively correlated with GMV changes in the hippocampus of FEDN patients, while SORCS3 and SLIT2 positively correlated with those in R-MDD.

CONCLUSIONS

Our results suggest that GMV alterations in hippocampal subfields along the long axis differ between FEDN and R-MDD, reflecting progressive hippocampal deterioration with prolonged depression, potentially supported by the expression of specific genes. These findings offer valuable insights into the distinct neural and genetic mechanisms underlying FEDN and R-MDD, which may aid in the development of more targeted and effective treatment strategies for MDD subtypes.

Common neuroanatomical differential factors underlying heterogeneous gray matter volume variations in five common psychiatric disorders

Multifaceted evidence has shown that psychiatric disorders share common neurobiological mechanisms. However, the tremendous inter-individual heterogeneity among patients with psychiatric disorders limits trans-diagnostic studies with case-control designs, aimed at identifying clinically promising neuroimaging biomarkers. This study aims to identify neuroanatomical differential factors (ND factors) underlying gray matter volume variations in five psychiatric disorders. We leverage 4 independent datasets of 878 patients diagnosed with psychiatric disorders and 585 healthy controls (HCs) to identify shared ND factors underlying individualized gray matter volume variations. Individualized gray matter volume variations are represented with the linear weighted sum of ND factors, and each case is assigned a unique factor composition, thus preserving interindividual variation. We identify four robust ND factors that can be generalized to unseen disorders. ND factors show significant association with group-level morphological abnormalities, reconciling individual- and group-level morphological abnormalities, and are characterized by dissociable cognitive processes, molecular signatures, and connectome-informed epicenters. Moreover, using factor compositions as features, we discover two robust transdiagnostic subtypes with opposite gray matter volume variations relative to HCs. In conclusion, we identify four reproducible and shared neuroanatomical factors that underlie the highly heterogeneous morphological abnormalities in psychiatric disorders.

Structural brain changes in the anterior cingulate cortex of major depressive disorder individuals with suicidal ideation: Evidence from the REST-meta-MDD project

BACKGROUND

Suicidal ideation (SI) is very common in patients with major depressive disorder (MDD). However, its neural mechanisms remain unclear. The anterior cingulate cortex (ACC) region may be associated with SI in MDD patients. This study aimed to elucidate the neural mechanisms of SI in MDD patients by analyzing changes in gray matter volume (GMV) in brain structures in the ACC region, which has not been adequately studied to date.

METHODS

According to the REST-meta-MDD project, this study subjects consisted of 235 healthy controls and 246 MDD patients, including 123 MDD patients with and 123 without SI, and their structural magnetic resonance imaging data were analyzed. The 17-item Hamilton Depression Rating Scale (HAMD) was used to assess depressive symptoms. Correlation analysis and logistic regression analysis were used to determine whether there was a correlation between GMV of ACC and SI in MDD patients.

RESULTS

MDD patients with SI had higher HAMD scores and greater GMV in bilateral ACC compared to MDD patients without SI (all p < 0.001). GMV of bilateral ACC was positively correlated with SI in MDD patients and entered the regression equation in the subsequent logistic regression analysis.

CONCLUSIONS

Our findings suggest that GMV of ACC may be associated with SI in patients with MDD and is a sensitive biomarker of SI.

Individualized resting-state functional connectivity abnormalities unveil two major depressive disorder subtypes with contrasting abnormal patterns of abnormality

Modern neuroimaging research has recognized that major depressive disorder (MDD) is a connectome disorder, characterized by altered functional connectivity across large-scale brain networks. However, the clinical heterogeneity, likely stemming from diverse neurobiological disturbances, complicates findings from standard group comparison methods. This variability has driven the search for MDD subtypes using objective neuroimaging markers. In this study, we sought to identify potential MDD subtypes from subject-level abnormalities in functional connectivity, leveraging a large multi-site dataset of resting-state MRI from 1276 MDD patients and 1104 matched healthy controls. Subject-level extreme functional connections, determined by comparing against normative ranges derived from healthy controls using tolerance intervals, were used to identify biological subtypes of MDD. We identified a set of extreme functional connections that were predominantly between the visual network and the frontoparietal network, the default mode network and the ventral attention network, with the key regions in the anterior cingulate cortex, bilateral orbitofrontal cortex, and supramarginal gyrus. In MDD patients, these extreme functional connections were linked to age of onset and reward-related processes. Using these features, we identified two subtypes with distinct patterns of functional connectivity abnormalities compared to healthy controls (p < 0.05, Bonferroni correction). When considering all patients together, no significant differences were found. These subtypes significantly enhanced case-control discriminability and showed strong internal discriminability between subtypes. Furthermore, the subtypes were reproducible across varying parameters, study sites, and in untreated patients. Our findings provide new insights into the taxonomy and have potential implications for both diagnosis and treatment of MDD.

Quantification of Glutathione and Its Associated Spontaneous Neuronal Activity in Major Depressive Disorder and Obsessive-Compulsive Disorder

BACKGROUND

Glutathione (GSH) is a crucial antioxidant in the human brain. Although proton magnetic resonance spectroscopy using the Mescher-Garwood point-resolved spectroscopy sequence is highly recommended, limited literature has measured cortical GSH using this method in major psychiatric disorders.

METHODS

By combining magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging, we quantified brain GSH and glutamate in the medial prefrontal cortex and precuneus and explored relationships between GSH levels and intrinsic neuronal activity as well as clinical symptoms among healthy control (HC) participants (n = 30), people with major depressive disorder (MDD) (n = 28), and people with obsessive-compulsive disorder (OCD) (n = 28).

RESULTS

GSH concentrations were lower in the medial prefrontal cortex and precuneus in both the MDD and OCD groups than in the HC group. In the HC group, positive correlations were noted between GSH and glutamate levels and between GSH and fractional amplitude of low-frequency fluctuations in both regions. However, while these correlations were absent in both patient groups, there was a weak positive correlation between glutamate and fractional amplitude of low-frequency fluctuations. Moreover, GSH levels were negatively correlated with depressive and compulsive symptoms in MDD and OCD, respectively.

CONCLUSIONS

These findings suggest that reduced GSH levels and an imbalance between GSH and glutamate could increase oxidative stress and alter neurotransmitter signaling, thereby leading to disruptions in GSH-related neurochemical-neuronal coupling and psychopathologies across MDD and OCD. Understanding these mechanisms could provide valuable insights into the processes that underlie these disorders and potentially become a springboard for future directions and advancing our knowledge of their neurobiological foundations.

M₂DC: A Meta-Learning Framework for Generalizable Diagnostic Classification of Major Depressive Disorder

Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The accurate and timely diagnosis of the diseases is essential for effective treatment and intervention. Thanks to the rapid development of brain imaging technology and machine learning algorithms, diagnostic classification of psychiatric diseases can be achieved based on brain images. However, due to divergences in scanning machines or parameters, the generalization capability of diagnostic classification models has always been an issue. We propose Meta-learning with Meta batch normalization and Distance Constraint (M2DC) for training diagnostic classification models. The framework can simulate the train-test domain shift situation and promote intra-class cohesion, as well as inter-class separation, which can lead to clearer classification margins and more generalizable models. To better encode dynamic brain graphs, we propose a concatenated spatiotemporal attention graph isomorphism network (CSTAGIN) as the backbone. The network is trained for the diagnostic classification of major depressive disorder (MDD) based on multi-site brain graphs. Extensive experiments on brain images from over 3261 subjects show that models trained by M2DC achieve the best performance on cross-site diagnostic classification tasks compared to various contemporary domain generalization methods and SOTA studies. The proposed M2DC is by far the first framework for multi-source closed-set domain generalizable training of diagnostic classification models for MDD and the trained models can be applied to reliable auxiliary diagnosis on novel data.

Structural neural plasticity evoked by rapid-acting antidepressant interventions

A feature in the pathophysiology of major depressive disorder (MDD), a mood disorder, is the impairment of excitatory synapses in the prefrontal cortex. Intriguingly, different types of treatment with fairly rapid antidepressant effects (within days or a few weeks), such as ketamine, electroconvulsive therapy and non-invasive neurostimulation, seem to converge on enhancement of neural plasticity. However, the forms and mechanisms of plasticity that link antidepressant interventions to the restoration of excitatory synaptic function are still unknown. In this Review, we highlight preclinical research from the past 15 years showing that ketamine and psychedelic drugs can trigger the growth of dendritic spines in cortical pyramidal neurons. We compare the longitudinal effects of various psychoactive drugs on neuronal rewiring, and we highlight rapid onset and sustained time course as notable characteristics for putative rapid-acting antidepressant drugs. Furthermore, we consider gaps in the current understanding of drug-evoked in vivo structural plasticity. We also discuss the prospects of using synaptic remodelling to understand other antidepressant interventions, such as repetitive transcranial magnetic stimulation. Finally, we conclude that structural neural plasticity can provide unique insights into the neurobiological actions of psychoactive drugs and antidepressant interventions.

AFMDD: Analyzing Functional Connectivity Feature of Major Depressive Disorder by Graph Neural Network-Based Model

The extraction of biomarkers from functional connectivity (FC) in the brain is of great significance for the diagnosis of mental disorders. In recent years, with the development of deep learning, several methods have been proposed to assist in the diagnosis of depression and promote its automatic identification. However, these methods still have some limitations. The current approaches overlook the importance of subgraphs in brain graphs, resulting in low accuracy. Using these methods with low accuracy for FC analysis may lead to unreliable results. To address these issues, we have designed a graph neural network-based model called AFMDD, specifically for analyzing FC features of depression and depression identification. Through experimental validation, our model has demonstrated excellent performance in depression diagnosis, achieving an accuracy of 73.15%, surpassing many state-of-the-art methods. In our study, we conducted visual analysis of nodes and edges in the FC networks of depression and identified several novel FC features. Those findings may provide valuable clues for the development of biomarkers for the clinical diagnosis of depression.

The different impacts of functional network centrality and connectivity on the complexity of brain signals in healthy control and first-episode drug-naïve patients with major depressive disorder

In recent years, brain signal complexity has gained attention as an indicator of brain well-being and a predictor of disease and dysfunction. Brain entropy quantifies this complexity. Assessment of functional network centrality and connectivity reveals that information communication induces neural signal oscillations in certain brain regions. However, their relationship is uncertain. This work studied brain signal complexity, network centrality, and connectivity in both healthy and depressed individuals. The current work comprised a sample of 124 first-episode drug-naïve patients with major depressive disorder (MDD) and 105 healthy controls (HC). Six functional networks were created for each person using resting-state functional magnetic resonance imaging. For each network, entropy, centrality, and connectivity were computed. Using structural equation modeling, this study examined the associations between brain network entropy, centrality, and connectivity. The findings demonstrated substantial correlations of entropy with both centrality and connectivity in HC and these correlation patterns were disrupted in MDD. Compared to HC, MDD exhibited higher entropy in four networks and demonstrated changes in centralities across all networks. The structural equation modeling showed that network centralities, connectivity, and depression severity had impacts on brain entropy. Nevertheless, no impacts were observed in the opposite directions. This study indicated that the complexity of brain signals was influenced not only by the interactions among different areas of the brain but also by the severity level of depression. These findings enhanced our comprehension of the associations of brain entropy with its influential factors.

Structural and functional brain correlates of the neutrophil- and monocyte-to-lymphocyte ratio in neuropsychiatric disorders

Skews in the neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) increasingly demonstrate prognostic capability in a range of acute and chronic mental health conditions. There has been a recent uptick in structural and functional magnetic responance imaging data corroborating the role of NLR and MLR in a cluster of neuropsychiatric disorders that are characterized by cognitive, affective, and psychomotor dysfunction. Moreover, these deficits are mostly evident in setting of acute and chronic disease comorbidity implicating aging and immunosenescent processes in the manifestation of these geriatric syndromes. The studies reviewed in this special edition implicate neutrophil and monocyte expansion relative to lymphocytopenia in the sequelae of depression, cognitive and functional decline, as well as provide support from a range of neuroimaging techniques that identify brain alteartions concommitant with expansion of the NLR or MLR and the sequelae of depression, dementia, and functional decline.

Using precision approaches to improve brain-behavior prediction

Predicting individual behavioral traits from brain idiosyncrasies has broad practical implications, yet predictions vary widely. This constraint may be driven by a combination of signal and noise in both brain and behavioral variables. Here, we expand on this idea, highlighting the potential of extended sampling 'precision' studies. First, we discuss their relevance to improving the reliability of individualized estimates by minimizing measurement noise. Second, we review how targeted within-subject experiments, when combined with individualized analysis or modeling frameworks, can maximize signal. These improvements in signal-to-noise facilitated by precision designs can help boost prediction studies. We close by discussing the integration of precision approaches with large-sample consortia studies to leverage the advantages of both.