Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis

Unveiling diverse clinical symptom patterns and neural activity profiles in major depressive disorder subtypes

BACKGROUND

The heterogeneity of major depressive disorder (MDD) significantly hinders its effective and optimal clinical outcomes. This study aimed to identify MDD subtypes by adopting a data-driven approach and assessing validity based on symptomatology and neuroimaging.

METHODS

A total of 259 patients with MDD and 92 healthy controls were enrolled in this cross-sectional study. Latent profile analysis (LPA) was used to identify MDD subtypes based on validated clinical symptoms. To examine whether there were differences between these identified MDD subtypes, network analysis was used to test any differences in symptom patterns between these subtypes. We also compared neural activity between these identified MDD subtypes and tested whether certain neural activities were related to individual subtypes. This MDD subtyping was further tested in an independent dataset that contains 86 patients with MDD.

FINDINGS

Five MDD subtypes with distinct depressive symptom patterns were identified using the LPA model, with the 5-class model selected as the optimal classification solution based on its superior fit indices (AIC = 6656.296, aBIC = 6681.030, entropy = 0.917, LMR p = 0.3267, BLRT p < 0.001). The identified subtypes include atypical-like depression, two melancholic depression (moderate and severe) subtypes with distinct patterns on feeling anxious, and two anhedonic depression subtypes (moderate and severe) with different manifestations on weight/appetite loss. The reproducibility of the classification was also confirmed. Significant differences in symptom structures between melancholic and two anhedonic subtypes, and between anhedonic and atypical subtypes were observed (all p < 0.05). Furthermore, these identified subtypes had differential neural activities in both regional spontaneous neural activity (pFWE < 0.005) and functional connectivity between different brain regions (pFDR < 0.005), linked to different clinical symptoms (FDR q < 0.05).

INTERPRETATION

The network analysis and neuroimaging tests support the existence and validity of the identified MDD subtypes, each exhibiting unique clinical manifestations and neural activity patterns. The categorisation of these subtypes sheds light on the heterogeneity of depression and suggest that personalised treatment and management strategies tailored to specific subtypes may enhance intervention strategies in clinical settings.

FUNDING

National Natural Science Foundation of China (NSFC) and China Scholarship Council (CSC).

Parsing clinical and neurobiological sources of heterogeneity in depression

BACKGROUND

Patients with depression vary from one-another in their clinical and neuroimaging presentation, yet the relationship between clinical and neuroimaging sources of variation is poorly understood. Determining sources of heterogeneity in depression is important to gain insights into its diverse and complex neural etiology. This study aims to test if depression heterogeneity is characterized by subgroups that differ both clinically and neurobiologically and/or whether multiple neuroimaging profiles give rise to the same clinical presentation.

METHODS

This study utilizes population-based data from the UK Biobank over multiple imaging sites. Clinically dissociated groups were selected to isolate clinical characteristics of depression (symptoms of anhedonia, depressed mood, and somatic disturbance; severity indices of lifetime chronicity and acute impairment; and late onset). Residual neuroimaging heterogeneity within each group was assessed using neuroimaging driven clustering.

RESULTS

The clinically dissociated subgroups had significantly larger neuroimaging normative deviations than a comparison heterogeneous group and had distinct neuroimaging profiles from each other. Imaging driven clustering within each clinically dissociated group identified two stable subtypes within the acute impairment group that differed significantly in cognitive ability, despite identical clinical profiles.

CONCLUSIONS

The study identified distinct neuroimaging profiles related to particular clinical depression features that may explain inconsistencies in the literature and sub-clusters within the acute impairment group with cognitive differences that were only differentiable by neuroimaging. Our results provide evidence that multiple neuroimaging profiles may give rise to the same clinical presentation, emphasizing the presence of complex interactions between clinical and neuroimaging sources of heterogeneity.

Functional connectivity across multi-frequency bands in patients with tension-type headache: a resting-state fMRI retrospective study

OBJECTIVES

Tension-type headache (TTH) is the most common nervous system disorder worldwide. This study aimed to examine abnormal network-level brain functional connectivity (FC) alterations in patients with TTH across multi-frequency bands.

METHODS

The study enrolled 63 subjects, comprising 32 patients with TTH and 31 healthy controls (HC). According to our team's previous research, the brain regions with abnormal ReHo in the conventional frequency band (0.01-0.08 Hz) and the slow-5 band (0.01-0.027 Hz) were chosen as seed regions of interest (ROIs). Subsequently, the FC between ROIs and the entire brain analysis across various frequency bands was calculated to evaluate network-level alterations, and differences between the TTH and HC were analyzed. Pearson's correlation analysis was conducted to assess the relationship between significantly altered FC values in two frequency bands and visual analog score (VAS) in TTH patients.

RESULTS

In the slow-5 band (0.01-0.027 Hz), FC between right medial superior frontal gyrus and right medial temporal pole/right inferior temporal gyrus as well as right middle frontal gyrus and left supramarginal gyrus of TTH patients exhibited significantly higher, compared to the HC group, while FC between right middle frontal gyrus and right lateral occipital cortex reduced. For the correlation results, there was no correlation between abnormal brain regions of FC and VAS score.

CONCLUSIONS

Changes in FC within brain regions associated with TTH are linked to pain processing. And the altered FC in TTH patients were frequency dependent. These initial observations could enhance our understanding of TTH's pathophysiological mechanism and offer insights for its future diagnosis and treatment.

Embracing Internal States: A Review of Optimization of Repetitive Transcranial Magnetic Stimulation for Treating Depression

Repetitive transcranial magnetic stimulation (rTMS) is a rapid and effective therapy for major depressive disorder; however, there is significant variability in therapeutic outcomes both within and across individuals, with approximately 50% of patients showing no response to rTMS treatment. Many studies have personalized the stimulation parameters of rTMS (e.g., location and intensity of stimulation) according to the anatomical and functional structure of the brain. In addition to these parameters, the internal states of the individual, such as circadian rhythm, behavior/cognition, neural oscillation, and neuroplasticity, also contribute to the variation in rTMS effects. In this review, we summarize the current literature on the interaction between rTMS and internal states. We propose two possible methods, multimodal treatment, and adaptive closed-loop treatment, to integrate patients' internal states to achieve better rTMS treatment for depression.

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.

Examining anterior prefrontal cortex resting-state functional connectivity patterns associated with depressive symptoms in chronic moderate-to-severe traumatic brain injury

In chronic moderate-to-severe TBI (msTBI), depression is one of the most common psychiatric consequences. Yet to date, there is limited understanding of its neural underpinnings. This study aimed to better understand this gap by examining seed-to-voxel connectivity in depression, with all voxel-wise associations seeded to the bilateral anterior prefrontal cortices (aPFC). In a secondary analysis of 32 patients with chronic msTBI and 17 age-matched controls acquired from the Toronto Rehab TBI Recovery Study database, the Personality Assessment Inventory Depression scale scores were used to group patients into an msTBI-Dep group (T ≥ 60; n = 13) and an msTBI-Non-Dep group (T < 60; n = 19). Resting-state fMRI scans were analyzed using seed-based connectivity analyses. F-tests, controlling for age and education, were used to assess differences in bilateral aPFC rsFC across the 3 groups. After nonparametric permutation testing, the left aPFC demonstrated significantly increased rsFC with the left (p = 0.041) and right (p = 0.013) fusiform gyri, the right superior temporal lobe (p = 0.032), and the right precentral gyrus (p = 0.042) in the msTBI-Dep group compared to controls. The msTBI-Non-Dep group had no significant rsFC differences with either group. To our knowledge, this study is the first to examine aPFC rsFC in a sample of patients with msTBI exclusively. Our preliminary findings suggest a role for the aPFC in the pathophysiology of depressive symptoms in patients with chronic msTBI. Increased aPFC-sensory/motor rsFC could be associated with vulnerability to depression post-TBI, a hypothesis that warrants further investigation.

Initial evidence for neural correlates following a therapeutic intervention: altered resting state functional connectivity in the default mode network following attention training technique

Introduction

The Attention Training Technique (ATT) is a psychotherapeutic intervention in Metacogntive Therapy (MCT) and aims at reducing maladaptive processes by strengthening attentional flexibility. ATT has demonstrated efficacy in treating depression on a clinical level. Here, we evaluated ATT at the neural level. We examined functional connectivity (FC) of the default mode network (DMN).

Method

48 individuals diagnosed with Major Depressive Disorder (MDD) and 51 healthy controls (HC) participated in a resting-state (rs) functional magnetic resonance imaging (fMRI) experiment. The participants received either one week of ATT or a sham intervention. Rs-fMRI scans before and after treatment were compared using seed-to-voxel analysis.

Results

The 2x2x2 analysis did not reach significance. Nevertheless, a resting-state connectivity effect was found on the basis of a posttest at the second measurement time point in MDD. After one week, MDD patients who had received ATT intervention presented lower functional connectivity between the left posterior cingulate cortex (PCC) and the bilateral middle frontal gyrus (MFG) as well as between the right PCC and the left MFG compared to the MDD patients in the sham group. In HC we observed higher rsFC in spatially close but not the same brain regions under the same experimental condition.

Conclusion

We found a first hint of a change at the neural level on the basis of ATT. Whether the changes in rsFC found here indicate an improvement in the flexible shift of attentional focus due to ATT needs to be investigated in further research paradigms. Further experiments have to show whether this change in functional connectivity can be used as a specific outcome measure of ATT treatment.

Exploring the impact of maternal early life adversity on interoceptive sensibility in pregnancy: implications for prenatal depression

PURPOSE

Pregnancy is a sensitive period of development in adult life characterized by massive changes in physical, emotional, and cognitive function. Such changes may be adaptive, e.g., facilitating adjustment to physical demands, but they may also reflect or contribute to risks inherent to this stage of life, e.g., prenatal depression. One cognitive ability that may undergo change during pregnancy and contribute to mental wellness is interoception - the ability to perceive, integrate, and model sensory information originating from the body. Strong interoceptive abilities are associated with lower rates of depression in non-pregnant adult populations, and interoception is generally weaker in individuals at higher risk for depression, for example, exposure to early life adversity (ELA). In the present online, cross-sectional study, we investigated whether interoception in pregnant women differed based on histories of ELA, in ways that increased their relative risk for prenatal depression symptoms.

METHODS

The pregnant individuals were in the second trimester of their first pregnancy and were compared to a group of nulliparous, non-parenting women.

RESULTS

Previous exposure to ELA significantly moderated pregnancy-related differences in self-reported interoception (interoceptive sensibility). A further moderated-mediation analysis revealed that the extent to which interoceptive sensibility buffered against depressive symptoms was conditional on ELA exposure, suggesting more ELA is associated with lower interoceptive sensibility during pregnancy, which increased prenatal depression risk.

CONCLUSIONS

Together this work suggests that levels of interoception during pregnancy are sensitive to previous adversity exposure. It also suggests that interoceptive-focused interventions for preventing/treating prenatal depressive symptoms in high-risk women may be worth exploring.

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.

Bayesian scalar-on-network regression with applications to brain functional connectivity

This paper presents a Bayesian regression model relating scalar outcomes to brain functional connectivity represented as symmetric positive definite (SPD) matrices. Unlike many proposals that simply vectorize the matrix-valued connectivity predictors, thereby ignoring their geometric structure, the method presented here respects the Riemannian geometry of SPD matrices by using a tangent space modeling. Dimension reduction is performed in the tangent space, relating the resulting low-dimensional representations to the responses. The dimension reduction matrix is learned in a supervised manner with a sparsity-inducing prior imposed on a Stiefel manifold to prevent overfitting. Our method yields a parsimonious regression model that allows uncertainty quantification of all model parameters and identification of key brain regions that predict the outcomes. We demonstrate the performance of our approach in simulation settings and through a case study to predict Picture Vocabulary scores using data from the Human Connectome Project.

Higher amplitudes of visual networks are associated with trait- but not state-depression

Despite depression being a leading cause of global disability, neuroimaging studies have struggled to identify replicable neural correlates of depression or explain limited variance. This challenge may, in part, stem from the intertwined state (current symptoms; variable) and trait (general propensity; stable) experiences of depression.Here, we sought to disentangle state from trait experiences of depression by leveraging a longitudinal cohort and stratifying individuals into four groups: those in remission ('trait depression group'), those with large longitudinal severity changes in depression symptomatology ('state depression group'), and their respective matched control groups (total analytic n = 1030). We hypothesized that spatial network organization would be linked to trait depression due to its temporal stability, whereas functional connectivity between networks would be more sensitive to state-dependent depression symptoms due to its capacity to fluctuate.We identified 15 large-scale probabilistic functional networks from resting-state fMRI data and performed group comparisons on the amplitude, connectivity, and spatial overlap between these networks, using matched control participants as reference. Our findings revealed higher amplitude in visual networks for the trait depression group at the time of remission, in contrast to controls. This observation may suggest altered visual processing in individuals predisposed to developing depression over time. No significant group differences were observed in any other network measures for the trait-control comparison, nor in any measures for the state-control comparison. These results underscore the overlooked contribution of visual networks to the psychopathology of depression and provide evidence for distinct neural correlates between state and trait experiences of depression.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

Allostatic Interoceptive Overload Across Psychiatric and Neurological Conditions

Emerging theories emphasize the crucial role of allostasis (anticipatory and adaptive regulation of the body's biological processes) and interoception (integration, anticipation, and regulation of internal bodily states) in adjusting physiological responses to environmental and bodily demands. In this review, we explore the disruptions in integrated allostatic interoceptive mechanisms in psychiatric and neurological disorders, including anxiety, depression, Alzheimer's disease, and frontotemporal dementia. We assess the biological mechanisms associated with allostatic interoception, including whole-body cascades, brain structure and function of the allostatic interoceptive network, heart-brain interactions, respiratory-brain interactions, the gut-brain-microbiota axis, peripheral biological processes (inflammatory, immune), and epigenetic pathways. These processes span psychiatric and neurological conditions and call for developing dimensional and transnosological frameworks. We synthesize new pathways to understand how allostatic interoceptive processes modulate interactions between environmental demands and biological functions in brain disorders. We discuss current limitations of the framework and future transdisciplinary developments. This review opens a new research agenda for understanding how allostatic interoception involves brain predictive coding in psychiatry and neurology, allowing for better clinical application and the development of new therapeutic interventions.

An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network

This study developed an artificial intelligence (AI) system using a local-global multimodal fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major depressive disorder (MDD), a complex disease influenced by social, psychological, and biological factors. Utilizing functional MRI, structural MRI, and electronic health records, the system offers an objective diagnostic method by integrating individual brain regions and population data. Tested across cohorts from China, Japan, and Russia with 1,182 healthy controls and 1,260 MDD patients from 24 institutions, it achieved a classification accuracy of 78.75%, an area under the receiver operating characteristic curve (AUROC) of 80.64%, and correctly identified MDD subtypes. The system further discovered distinct brain connectivity patterns in MDD, including reduced functional connectivity between the left gyrus rectus and right cerebellar lobule VIIB, and increased connectivity between the left Rolandic operculum and right hippocampus. Anatomically, MDD is associated with thickness changes of the gray and white matter interface, indicating potential neuropathological conditions or brain injuries.

Exploring the Potential of Probiotics and Prebiotics in Major Depression: From Molecular Function to Clinical Therapy

Major depressive disorder (MDD) represents a complex and challenging mental health condition with multifaceted etiology. Recent research exploring the gut-brain axis has shed light on the potential influence of gut microbiota on mental health, offering novel avenues for therapeutic intervention. This paper reviews current evidence on the role of prebiotics and probiotics in the context of MDD treatment. Clinical studies assessing the effects of prebiotic and probiotic interventions have demonstrated promising results, showcasing improvements in depression symptoms and metabolic parameters in certain populations. Notably, prebiotics and probiotics have shown the capacity to modulate inflammatory markers, cortisol levels, and neurotransmitter pathways linked to MDD. However, existing research presents varied outcomes, underscoring the need for further investigation into specific microbial strains, dosage optimization, and long-term effects. Future research should aim at refining personalized interventions, elucidating mechanisms of action, and establishing standardized protocols to integrate these interventions into clinical practice. While prebiotics and probiotics offer potential adjunctive therapies for MDD, continued interdisciplinary efforts are vital to harnessing their full therapeutic potential and reshaping the landscape of depression treatment paradigms.

Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation

Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.72s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.

Functional connectivity of the amygdala subnuclei in various mood states of bipolar disorder

Amygdala functional dysconnectivity lies at the heart of the pathophysiology of bipolar disorder (BD). Recent preclinical studies suggest that the amygdala is a heterogeneous group of nuclei, whose specific connectivity could drive positive or negative emotional valence. We investigated functional connectivity (FC) changes within these circuits emerging from each amygdala's subdivision in 127 patients with BD in different mood states and 131 healthy controls (HC), who underwent resting-state functional MRI. FC was evaluated between lateral and medial nuclei of amygdalae, and key subcortical regions of the emotion processing network: anterior and posterior parts of the hippocampus, and core and shell parts of the nucleus accumbens. FC was compared across groups, and subgroups of patients depending on their mood states, using linear mixed models. We also tested correlations between FC and depression (MADRS) and mania (YMRS) scores. We found no difference between the whole sample of BD patients vs. HC but a significant correlation between MADRS and right lateral amygdala /right anterior hippocampus, right lateral amygdala/right posterior hippocampus and right lateral amygdala/left anterior hippocampus FC (r = -0.44, r = -0.32, r = -0.27, respectively, all pFDR<0.05). Subgroup analysis revealed decreased right lateral amygdala/right anterior hippocampus and right lateral amygdala/right posterior hippocampus FC in depressed vs. non-depressed patients and increased left medial amygdala/shell part of the left nucleus accumbens FC in manic vs non-manic patients. These results demonstrate that acute mood states in BD concur with FC changes in individual nuclei of the amygdala implicated in distinct emotional valence processing. Overall, our data highlight the importance to consider the amygdala subnuclei separately when studying its FC patterns including patients in distinct homogeneous mood states.

Motor versus Psychomotor? Deciphering the Neural Source of Psychomotor Retardation in Depression

Major depressive disorder (MDD) is characterized by psychomotor retardation whose underlying neural source remains unclear. Psychomotor retardation may either be related to a motor source like the motor cortex or, alternatively, to a psychomotor source with neural changes outside motor regions, like input regions such as visual cortex. These two alternative hypotheses in main (n = 41) and replication (n = 18) MDD samples using 7 Tesla MRI are investigated. Analyzing both global and local connectivity in primary motor cortex (BA4), motor network and middle temporal visual cortex complex (MT+), the main findings in MDD are: 1) Reduced local and global synchronization and increased local-to-global output in motor regions, which do not correlate with psychomotor retardation, though. 2) Reduced local-to-local BA4 - MT+ functional connectivity (FC) which correlates with psychomotor retardation. 3) Reduced global synchronization and increased local-to-global output in MT+ which relate to psychomotor retardation. 4) Reduced variability in the psychophysical measures of MT+ based motion perception which relates to psychomotor retardation. Together, it is shown that visual cortex MT+ and its relation to motor cortex play a key role in mediating psychomotor retardation. This supports psychomotor over motor hypothesis about the neural source of psychomotor retardation in MDD.

Effect of Xiaoyaosan on brain volume and microstructure diffusion changes to exert antidepressant-like effects in mice with chronic social defeat stress

Objective

Depression is a prevalent mental disorder characterized by persistent negative mood and loss of pleasure. Although there are various treatment modalities available for depression, the rates of response and remission remain low. Xiaoyaosan (XYS), a traditional Chinese herbal formula with a long history of use in treating depression, has shown promising effects. However, the underlying mechanism of its therapeutic action remains elusive. The aim of this study is to investigate the neuroimaging changes in the brain associated with the antidepressant-like effects of XYS.

Methods

Here, we combined voxel-based morphometry of T2-weighted images and voxel-based analysis on diffusion tensor images to evaluate alterations in brain morphometry and microstructure between chronic social defeat stress (CSDS) model mice and control mice. Additionally, we examined the effect of XYS treatment on structural disruptions in the brains of XYS-treated mice. Furthermore, we explored the therapeutic effect of 18β-glycyrrhetinic acid (18β-GA), which was identified as the primary compound present in the brain following administration of XYS. Significant differences in brain structure were utilized as classification features for distinguishing mice with depression model form the controls using a machine learning method.

Results

Significant changes in brain volume and diffusion metrics were observed in the CSDS model mice, primarily concentrated in the nucleus accumbens (ACB), primary somatosensory area (SSP), thalamus (TH), hypothalamus (HY), basomedical amygdala nucleus (BMA), caudoputamen (CP), and retrosplenial area (RSP). However, both XYS and 18β-GA treatment prevented disruptions in brain volume and diffusion metrics in certain regions, including bilateral HY, right SSP, right ACB, bilateral CP, and left TH. The classification models based on each type of neuroimaging feature achieved high accuracy levels (gray matter volume: 76.39%, AUC=0.83; white matter volume: 76.39%, AUC=0.92; fractional anisotropy: 82.64%, AUC=0.9; radial diffusivity: 76.39%, AUC=0.82). Among these machine learning analyses, the right ACB, right HY, and right CP were identified as the most important brain regions for classification purposes.

Conclusion

These findings suggested that XYS can prevent abnormal changes in brain volume and microstructure within TH, SSP, ACB, and CP to exert prophylactic antidepressant-like effects in CSDS model mice. The neuroimaging features within these regions demonstrate excellent performance for classifying CSDS model mice from controls while providing valuable insights into the antidepressant effects of XYS.

Individual large-scale functional network mapping for major depressive disorder with electroconvulsive therapy

Personalized functional connectivity mapping has been demonstrated to be promising in identifying underlying neurophysiological basis for brain disorders and treatment effects. Electroconvulsive therapy (ECT) has been proved to be an effective treatment for major depressive disorder (MDD) while its active mechanisms remain unclear. Here, 46 MDD patients before and after ECT as well as 46 demographically matched healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. A spatially regularized form of non-negative matrix factorization (NMF) was used to accurately identify functional networks (FNs) in individuals to map individual-level static and dynamic functional network connectivity (FNC) to reveal the underlying neurophysiological basis of therepetical effects of ECT for MDD. Moreover, these static and dynamic FNCs were used as features to predict the clinical treatment outcomes for MDD patients. We found that ECT could modulate both static and dynamic large-scale FNCs at individual level in MDD patients, and dynamic FNCs were closely associated with depression and anxiety symptoms. Importantly, we found that individual FNCs, particularly the individual dynamic FNCs could better predict the treatment outcomes of ECT suggesting that dynamic functional connectivity analysis may be better to link brain functional characteristics with clinical symptoms and treatment outcomes. Taken together, our findings provide new evidence for the active mechanisms and biomarkers for ECT to improve diagnostic accuracy and to guide individual treatment selection for MDD patients.

Measuring functional connectivity in frequency-domain helps to better characterize brain function

Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

Dynamic Organization of Large-scale Functional Brain Networks Supports Interactions Between Emotion and Executive Control

Emotion and executive control are often conceptualized as two distinct modes of human brain functioning. Little, however, is known about how the dynamic organization of large-scale functional brain networks that support flexible emotion processing and executive control, especially their interactions. The amygdala and prefrontal systems have long been thought to play crucial roles in these processes. Recent advances in human neuroimaging studies have begun to delineate functional organization principles among the large-scale brain networks underlying emotion, executive control, and their interactions. Here, we propose a dynamic brain network model to account for interactive competition between emotion and executive control by reviewing recent resting-state and task-related neuroimaging studies using network-based approaches. In this model, dynamic interactions among the executive control network, the salience network, the default mode network, and sensorimotor networks enable dynamic processes of emotion and support flexible executive control of multiple processes; neural oscillations across multiple frequency bands and the locus coeruleus-norepinephrine pathway serve as communicational mechanisms underlying dynamic synergy among large-scale functional brain networks. This model has important implications for understanding how the dynamic organization of complex brain systems and networks empowers flexible cognitive and affective functions.

Complement C1q/C3-CR3 signaling pathway mediates abnormal microglial phagocytosis of synapses in a mouse model of depression

BACKGROUND

Both functional brain imaging studies and autopsy reports have indicated the presence of synaptic loss in the brains of depressed patients. The activated microglia may dysfunctionally engulf neuronal synapses, leading to synaptic loss and behavioral impairments in depression. However, the mechanisms of microglial-synaptic interaction under depressive conditions remain unclear.

METHODS

We utilized lipopolysaccharide (LPS) to induce a mouse model of depression, examining the effects of LPS on behaviors, synapses, microglia, microglial phagocytosis of synapses, and the C1q/C3-CR3 complement signaling pathway. Additionally, a C1q neutralizing antibody was employed to inhibit the C1q/C3-CR3 signaling pathway and assess its impact on microglial phagocytosis of synapses and behaviors in the mice.

RESULTS

LPS administration resulted in depressive and anxiety-like behaviors, synaptic loss, and abnormal microglial phagocytosis of synapses in the hippocampal dentate gyrus (DG) of mice. We found that the C1q/C3-CR3 signaling pathway plays a crucial role in this abnormal microglial activity. Treatment with the C1q neutralizing antibody moderated the C1q/C3-CR3 pathway, leading to a decrease in abnormal microglial phagocytosis, reduced synaptic loss, and improved behavioral impairments in the mice.

CONCLUSIONS

The study suggests that the C1q/C3-CR3 complement signaling pathway, which mediates abnormal microglial phagocytosis of synapses, presents a novel potential therapeutic target for depression treatment.

Atypical dynamic network reconfiguration and genetic mechanisms in patients with major depressive disorder

BACKGROUND

Brain dynamics underlie complex forms of flexible cognition or the ability to shift between different mental modes. However, the precise dynamic reconfiguration based on multi-layer network analysis and the genetic mechanisms of major depressive disorder (MDD) remains unclear.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) data were acquired from the REST-meta-MDD consortium, including 555 patients with MDD and 536 healthy controls (HC). A time-varying multi-layer network was constructed, and dynamic modular characteristics were used to investigate the network reconfiguration. Additionally, partial least squares regression analysis was performed using transcriptional data provided by the Allen Human Brain Atlas (AHBA) to identify genes associated with atypical dynamic network reconfiguration in MDD.

RESULTS

In comparison to HC, patients with MDD exhibited lower global and local recruitment coefficients. The local reduction was particularly evident in the salience and subcortical networks. Spatial transcriptome correlation analysis revealed an association between gene expression profiles and atypical dynamic network reconfiguration observed in MDD. Further functional enrichment analyses indicated that positively weighted reconfiguration-related genes were primarily associated with metabolic and biosynthetic pathways. Additionally, negatively enriched genes were predominantly related to programmed cell death-related terms.

CONCLUSIONS

Our findings offer robust evidence of the atypical dynamic reconfiguration in patients with MDD from a novel perspective. These results offer valuable insights for further exploration into the mechanisms underlying MDD.

Recognition of autism spectrum disorder in children based on electroencephalogram network topology

Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.

Gradient Matching Federated Domain Adaptation for Brain Image Classification

Federated learning has shown its unique advantages in many different tasks, including brain image analysis. It provides a new way to train deep learning models while protecting the privacy of medical image data from multiple sites. However, previous studies suggest that domain shift across different sites may influence the performance of federated models. As a solution, we propose a gradient matching federated domain adaptation (GM-FedDA) method for brain image classification, aiming to reduce domain discrepancy with the assistance of a public image dataset and train robust local federated models for target sites. It mainly includes two stages: 1) pretraining stage; we propose a one-common-source adversarial domain adaptation (OCS-ADA) strategy, i.e., adopting ADA with gradient matching loss to pretrain encoders for reducing domain shift at each target site (private data) with the assistance of a common source domain (public data) and 2) fine-tuning stage; we develop a gradient matching federated (GM-Fed) fine-tuning method for updating local federated models pretrained with the OCS-ADA strategy, i.e., pushing the optimization direction of a local federated model toward its specific local minimum by minimizing gradient matching loss between sites. Using fully connected networks as local models, we validate our method with the diagnostic classification tasks of schizophrenia and major depressive disorder based on multisite resting-state functional MRI (fMRI), respectively. Results show that the proposed GM-FedDA method outperforms other commonly used methods, suggesting the potential of our method in brain imaging analysis and other fields, which need to utilize multisite data while preserving data privacy.

A deep learning approach for mental health quality prediction using functional network connectivity and assessment data

While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approaches that visualize brain activity to questionnaires in the context of individualized prediction, we can gain new insights into the biology and behavioral aspects of brain health. Resting-state fMRI (rs-fMRI) can be used to identify biomarkers of these conditions and study patterns of abnormal connectivity. In this work, we estimate mental health quality for individual participants using static functional network connectivity (sFNC) data from rs-fMRI. The deep learning model uses the sFNC data as input to predict four categories of mental health quality and visualize the neural patterns indicative of each group. We used guided gradient class activation maps (guided Grad-CAM) to identify the most discriminative sFNC patterns. The effectiveness of this model was validated using the UK Biobank dataset, in which we showed that our approach outperformed four alternative models by 4-18% accuracy. The proposed model's performance evaluation yielded a classification accuracy of 76%, 78%, 88%, and 98% for the excellent, good, fair, and poor mental health categories, with poor mental health accuracy being the highest. The findings show distinct sFNC patterns across each group. The patterns associated with excellent mental health consist of the cerebellar-subcortical regions, whereas the most prominent areas in the poor mental health category are in the sensorimotor and visual domains. Thus the combination of rs-fMRI and deep learning opens a promising path for developing a comprehensive framework to evaluate and measure mental health. Moreover, this approach had the potential to guide the development of personalized interventions and enable the monitoring of treatment response. Overall this highlights the crucial role of advanced imaging modalities and deep learning algorithms in advancing our understanding and management of mental health.

Modulation of habenular and nucleus accumbens functional connectivity by ketamine in major depression

INTRODUCTION

Major depressive disorder (MDD) is associated with dysfunctional reward processing, which involves functional circuitry of the habenula (Hb) and nucleus accumbens (NAc). Since ketamine elicits rapid antidepressant and antianhedonic effects in MDD, this study sought to investigate how serial ketamine infusion (SKI) treatment modulates static and dynamic functional connectivity (FC) in Hb and NAc functional networks.

METHODS

MDD participants (n = 58, mean age = 40.7 years, female = 28) received four ketamine infusions (0.5 mg/kg) 2-3 times weekly. Resting-state functional magnetic resonance imaging (fMRI) scans and clinical assessments were collected at baseline and 24 h post-SKI. Static FC (sFC) and dynamic FC variability (dFCv) were calculated from left and right Hb and NAc seeds to all other brain regions. Changes in FC pre-to-post SKI, and correlations with changes with mood and anhedonia were examined. Comparisons of FC between patients and healthy controls (HC) at baseline (n = 55, mean age = 32.6, female = 31), and between HC assessed twice (n = 16) were conducted as follow-up analyses.

RESULTS

Following SKI, significant increases in left Hb-bilateral visual cortex FC, decreases in left Hb-left inferior parietal cortex FC, and decreases in left NAc-right cerebellum FC occurred. Decreased dFCv between left Hb and right precuneus and visual cortex, and decreased dFCv between right NAc and right visual cortex both significantly correlated with improvements in mood ratings. Decreased FC between left Hb and bilateral visual/parietal cortices as well as increased FC between left NAc and right visual/parietal cortices both significantly correlated with improvements in anhedonia. No differences were observed between HC at baseline or over time.

CONCLUSION

Subanesthetic ketamine modulates functional pathways linking the Hb and NAc with visual, parietal, and cerebellar regions in MDD. Overlapping effects between Hb and NAc functional systems were associated with ketamine's therapeutic response.

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis

Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.

The association between gray matter volume in the hippocampal subfield and antidepressant efficacy mediated by abnormal dynamic functional connectivity

An abnormality of structures and functions in the hippocampus may have a key role in the pathophysiology of major depressive disorder (MDD). However, it is unclear whether structure factors of the hippocampus effectively impact antidepressant responses by hippocampal functional activity in MDD patients. We collected longitudinal data from 36 MDD patients before and after a 3-month course of antidepressant pharmacotherapy. Additionally, we obtained baseline data from 43 healthy controls matched for sex and age. Using resting-state functional magnetic resonance imaging (rs-fMRI), we estimated the dynamic functional connectivity (dFC) of the hippocampal subregions using a sliding-window method. The gray matter volume was calculated using voxel-based morphometry (VBM). The results indicated that patients with MDD exhibited significantly lower dFC of the left rostral hippocampus (rHipp.L) with the right precentral gyrus, left superior temporal gyrus and left postcentral gyrus compared to healthy controls at baseline. In MDD patients, the dFC of the rHipp.L with right precentral gyrus at baseline was correlated with both the rHipp.L volume and HAMD remission rate, and also mediated the effects of the rHipp.L volume on antidepressant performance. Our findings suggested that the interaction between hippocampal structure and functional activity might affect antidepressant performance, which provided a novel insight into the hippocampus-related neurobiological mechanism of MDD.

Topological disruption of low- and high-order functional networks in presbycusis

Prior efforts have manifested that functional connectivity (FC) network disruptions are concerned with cognitive disorder in presbycusis. The present research was designed to investigate the topological reorganization and classification performance of low-order functional connectivity (LOFC) and high-order functional connectivity (HOFC) networks in patients with presbycusis. Resting-state functional magnetic resonance imaging (Rs-fMRI) data were obtained in 60 patients with presbycusis and 50 matched healthy control subjects (HCs). LOFC and HOFC networks were then constructed, and the topological metrics obtained from the constructed networks were compared to evaluate topological differences in global, nodal network metrics, modularity and rich-club organization between patients with presbycusis and HCs. The use of HOFC profiles boosted presbycusis classification accuracy, sensitivity and specificity compared to that using LOFC profiles. The brain networks in both patients with presbycusis and HCs exhibited small-world properties within the given threshold range, and striking differences between groups in topological metrics were discovered in the constructed networks (LOFC and HOFC). NBS analysis identified a subnetwork involving 26 nodes and 23 signally altered internodal connections in patients with presbycusis in comparison to HCs in HOFC networks. This study highlighted the topological differences between LOFC and HOFC networks in patients with presbycusis, suggesting that HOFC profiles may help to further identify brain network abnormalities in presbycusis.

Ordinal Pattern Tree: A New Representation Method for Brain Network Analysis

Brain networks, describing the functional or structural interactions of brain with graph theory, have been widely used for brain imaging analysis. Currently, several network representation methods have been developed for describing and analyzing brain networks. However, most of these methods ignored the valuable weighted information of the edges in brain networks. In this paper, we propose a new representation method (i.e., ordinal pattern tree) for brain network analysis. Compared with the existing network representation methods, the proposed ordinal pattern tree (OPT) can not only leverage the weighted information of the edges but also express the hierarchical relationships of nodes in brain networks. On OPT, nodes are connected by ordinal edges which are constructed by using the ordinal pattern relationships of weighted edges. We represent brain networks as OPTs and further develop a new graph kernel called optimal transport (OT) based ordinal pattern tree (OT-OPT) kernel to measure the similarity between paired brain networks. In OT-OPT kernel, the OT distances are used to calculate the transport costs between the nodes on the OPTs. Based on these OT distances, we use exponential function to calculate OT-OPT kernel which is proved to be positive definite. To evaluate the effectiveness of the proposed method, we perform classification and regression experiments on ADHD-200, ABIDE and ADNI datasets. The experimental results demonstrate that our proposed method outperforms the state-of-the-art graph methods in the classification and regression tasks.

Nuclei-specific hypothalamus networks predict a dimensional marker of stress in humans

The hypothalamus is part of the hypothalamic-pituitary-adrenal axis which activates stress responses through release of cortisol. It is a small but heterogeneous structure comprising multiple nuclei. In vivo human neuroimaging has rarely succeeded in recording signals from individual hypothalamus nuclei. Here we use human resting-state fMRI (n = 498) with high spatial resolution to examine relationships between the functional connectivity of specific hypothalamic nuclei and a dimensional marker of prolonged stress. First, we demonstrate that we can parcellate the human hypothalamus into seven nuclei in vivo. Using the functional connectivity between these nuclei and other subcortical structures including the amygdala, we significantly predict stress scores out-of-sample. Predictions use 0.0015% of all possible brain edges, are specific to stress, and improve when using nucleus-specific compared to whole-hypothalamus connectivity. Thus, stress relates to connectivity changes in precise and functionally meaningful subcortical networks, which may be exploited in future studies using interventions in stress disorders.

Few-Shot Domain-Adaptive Anomaly Detection for Cross-Site Brain Images

Early screening is essential for effective intervention and treatment of individuals with mental disorders. Functional magnetic resonance imaging (fMRI) is a noninvasive tool for depicting neural activity and has demonstrated strong potential as a technique for identifying mental disorders. Due to the difficulty in data collection and diagnosis, imaging data from patients are rare at a single site, whereas abundant healthy control data are available from public datasets. However, joint use of these data from multiple sites for classification model training is hindered by cross-domain distribution discrepancy and diverse label spaces. Herein, we propose few-shot domain-adaptive anomaly detection (FAAD) to achieve cross-site anomaly detection of brain images based on only a few labeled samples. We introduce domain adaptation to mitigate cross-domain distribution discrepancy and jointly align the general and conditional feature distributions of imaging data across multiple sites. We utilize fMRI data of healthy subjects in the Human Connectome Project (HCP) as the source domain and fMRI images from six independent sites, including patients with mental disorders and demographically matched healthy controls, as target domains. Experiments showed the superiority of the proposed method compared with binary classification, traditional anomaly detection methods, and several recognized domain adaptation methods.

Multimodal imaging measures in the prediction of clinical response to deep brain stimulation for refractory depression: A machine learning approach

OBJECTIVES

This study compared machine learning models using unimodal imaging measures and combined multi-modal imaging measures for deep brain stimulation (DBS) outcome prediction in treatment resistant depression (TRD).

METHODS

Regional brain glucose metabolism (CMRGlu), cerebral blood flow (CBF), and grey matter volume (GMV) were measured at baseline using 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography (PET), arterial spin labelling (ASL) magnetic resonance imaging (MRI), and T1-weighted MRI, respectively, in 19 patients with TRD receiving subcallosal cingulate (SCC)-DBS. Responders (n = 9) were defined by a 50% reduction in HAMD-17 at 6 months from the baseline. Using an atlas-based approach, values of each measure were determined for pre-selected brain regions. OneR feature selection algorithm and the naïve Bayes model was used for classification. Leave-out-one cross validation was used for classifier evaluation.

RESULTS

The performance accuracy of the CMRGlu classification model (84%) was greater than CBF (74%) or GMV (74%) models. The classification model using the three image modalities together led to a similar accuracy (84%0 compared to the CMRGlu classification model.

CONCLUSIONS

CMRGlu imaging measures may be useful for the development of multivariate prediction models for SCC-DBS studies for TRD. The future of multivariate methods for multimodal imaging may rest on the selection of complementing features and the developing better models.Clinical Trial Registration: ClinicalTrials.gov (#NCT01983904).

Evaluating the depression state during perinatal period by non-invasive scalp EEG

Perinatal depression, with a prevalence of 10 to 20% in United States, is usually missed as multiple symptoms of perinatal depression are common in pregnant women. Worse, the diagnosis of perinatal depression still largely relies on questionnaires, leaving the objective biomarker being unveiled yet. This study suggested a safe and non-invasive technique to diagnose perinatal depression and further explore its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram for mothers-to-be and fetuses, we collected the resting-state electroencephalogram of pregnant women at the 38th week of gestation. Subsequently, the difference in network topology between perinatal depression patients and healthy mothers-to-be was explored, with related spatial patterns being adopted to achieve the classification of pregnant women with perinatal depression from those healthy ones. We found that the perinatal depression patients had decreased brain network connectivity, which indexed impaired efficiency of information processing. By adopting the spatial patterns, the perinatal depression could be accurately recognized with an accuracy of 87.88%; meanwhile, the depression severity at the individual level was effectively predicted, as well. These findings consistently illustrated that the resting-state electroencephalogram network could be a reliable tool for investigating the depression state across pregnant women, and will further facilitate the clinical diagnosis of perinatal depression.

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.

Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale

BACKGROUND

Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive, affective and behavioral tasks, adapted for the functional magnetic resonance imaging (MRI) (fMRI) experimental environment. There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders.

AIM

To investigate whether there exist specific neural circuits which underpin differential item responses to depressive, paranoid and neutral items (DN) in patients respectively with schizophrenia (SCZ) and major depressive disorder (MDD).

METHODS

60 patients were recruited with SCZ and MDD. All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm, comprised of block design, including blocks with items from diagnostic paranoid (DP), depression specific (DS) and DN from general interest scale. We performed a two-sample t-test between the two groups-SCZ patients and depressive patients. Our purpose was to observe different brain networks which were activated during a specific condition of the task, respectively DS, DP, DN.

RESULTS

Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task. We identified one component that is task-related and independent of condition (shared between all three conditions), composed by regions within the temporal (right superior and middle temporal gyri), frontal (left middle and inferior frontal gyri) and limbic/salience system (right anterior insula). Another component is related to both diagnostic specific conditions (DS and DP) e.g. It is shared between DEP and SCZ, and includes frontal motor/language and parietal areas. One specific component is modulated preferentially by to the DP condition, and is related mainly to prefrontal regions, whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus, several occipital areas, including lingual and fusiform gyrus, as well as parahippocampal gyrus. Finally, component 12 appeared to be unique for the neutral condition. In addition, there have been determined circuits across components, which are either common, or distinct in the preferential processing of the sub-scales of the task.

CONCLUSION

This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

Ketamine treatment modulates habenular and nucleus accumbens static and dynamic functional connectivity in major depression

Dysfunctional reward processing in major depressive disorder (MDD) involves functional circuitry of the habenula (Hb) and nucleus accumbens (NAc). Ketamine elicits rapid antidepressant and alleviates anhedonia in MDD. To clarify how ketamine perturbs reward circuitry in MDD, we examined how serial ketamine infusions (SKI) modulate static and dynamic functional connectivity (FC) in Hb and NAc networks. MDD participants (n=58, mean age=40.7 years, female=28) received four ketamine infusions (0.5mg/kg) 2-3 times weekly. Resting-state fMRI scans and clinical assessments were collected at baseline and 24 hours post-SKI completion. Static FC (sFC) and dynamic FC variability (dFCv) were calculated from left and right Hb and NAc seeds to all other brain regions. Paired t-tests examined changes in FC pre-to-post SKI, and correlations were used to determine relationships between FC changes with mood and anhedonia. Following SKI, significant increases in left Hb-bilateral visual cortex FC, decreases in left Hb-left inferior parietal cortex FC, and decreases in left NAc-right cerebellum FC occurred. Decreased dFCv between left Hb and right precuneus and visual cortex, and decreased dFCv between right NAc and right visual cortex both significantly correlated with improvements in Hamilton Depression Rating Scale. Decreased FC between left Hb and bilateral visual/parietal cortices as well as increased FC between left NAc and right visual/parietal cortices both significantly correlated with improvements in anhedonia. Subanesthetic ketamine modulates functional pathways linking the Hb and NAc with visual, parietal, and cerebellar regions. Overlapping effects between Hb and NAc functional systems were associated with ketamine's therapeutic response.

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

BACKGROUND

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

Prenatal infection and adolescent social adversity affect microglia, synaptic density, and behavior in male rats

Maternal infection during pregnancy and childhood social trauma have been associated with neurodevelopmental and affective disorders, such as schizophrenia, autism spectrum disorders, bipolar disorder and depression. These disorders are characterized by changes in microglial cells, which play a notable role in synaptic pruning, and synaptic deficits. Here, we investigated the effect of prenatal infection and social adversity during adolescence - either alone or in combination - on behavior, microglia, and synaptic density. Male offspring of pregnant rats injected with poly I:C, mimicking prenatal infection, were exposed to repeated social defeat during adolescence. We found that maternal infection during pregnancy prevented the reduction in social behavior and increase in anxiety induced by social adversity during adolescence. Furthermore, maternal infection and social adversity, alone or in combination, induced hyperlocomotion in adulthood. Longitudinal in vivo imaging with [11C]PBR28 positron emission tomography revealed that prenatal infection alone and social adversity during adolescence alone induced a transient increase in translocator protein TSPO density, an indicator of glial reactivity, whereas their combination induced a long-lasting increase that remained until adulthood. Furthermore, only the combination of prenatal infection and social adversity during adolescence induced an increase in microglial cell density in the frontal cortex. Prenatal infection increased proinflammatory cytokine IL-1β protein levels in hippocampus and social adversity reduced anti-inflammatory cytokine IL-10 protein levels in hippocampus during adulthood. This reduction in IL-10 was prevented if rats were previously exposed to prenatal infection. Adult offspring exposed to prenatal infection or adolescent social adversity had a higher synaptic density in the frontal cortex, but not hippocampus, as evaluated by synaptophysin density. Interestingly, such an increase in synaptic density was not observed in rats exposed to the combination of prenatal infection and social adversity, perhaps due to the long-lasting increase in microglial density, which may lead to an increase in microglial synaptic pruning. These findings suggest that changes in microglia activity and cytokine release induced by prenatal infection and social adversity during adolescence may be related to a reduced synaptic pruning, resulting in a higher synaptic density and behavioral changes in adulthood.

Altered voxel-based and surface-based morphometry in inflammatory bowel disease

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is characterized by inflammation of the gastrointestinal tract and is a disorder of the brain-gut axis. Neuroimaging studies of brain function and structure have helped better understand the relationships between the brain, gut, and comorbidity in IBD. Studies of brain structure have primarily employed voxel-based morphometry to measure grey matter volume and surface-based morphometry to measure cortical thickness. Far fewer studies have employed other surface-based morphometry metrics such as gyrification, cortical complexity, and sulcal depth. In this study, brain structure differences between 72 adults with IBD and 90 healthy controls were assessed using all five metrics. Significant differences were found for cortical thickness with the IBD group showing extensive left-lateralized thinning, and for cortical complexity with the IBD group showing greater complexity in the left fusiform and right posterior cingulate. No significant differences were found in grey matter volume, gyrification, or sulcal depth. Within the IBD group, a post hoc analysis identified that disease duration is associated with cortical complexity of the right supramarginal gyrus, albeit with a more lenient threshold applied.

Resting-state functional connectivity of the cerebellum-cerebrum in older women with depressive symptoms

BACKGROUND

Although there has been much neurobiological research on major depressive disorder, research on the neurological function of depressive symptoms (DS) or subclinical depression is still scarce, especially in older women with DS.

OBJECTIVES

Resting-state functional magnetic resonance imaging (rs-fMRI) was used to compare functional connectivity (FC) between the cerebellum and cerebral in older women with DS and normal controls (NC), to explore unique changes in cerebellar FC in older women with DS.

METHODS

In all, 16 older women with DS and 17 NC were recruited. All subjects completed rs-fMRI. The 26 sub-regions of the cerebellum divided by the AAL3 map were used as regions of interest (ROI) to analyze the difference in FC strength of cerebellar seeds from other cerebral regions between the two groups. Finally, partial correlation analysis between abnormal FC strength and Geriatric Depression Scale (GDS) score and Reminiscence Functions Scale (RFS) score in the DS group.

RESULTS

Compared with NC group, the DS group showed significantly reduced FC between Crus I, II and the left frontoparietal region, and reduced FC between Crus I and the left temporal gyrus. Reduced FC between right insula (INS), right rolandic operculum (ROL), right precentral gyrus (PreCG) and the Lobule IX, X. Moreover, the negative FC between Crus I, II, Lobule IX and visual regions was reduced in the DS group. The DS group correlation analysis showed a positive correlation between the left Crus I and the right cuneus (CUN) FC and GDS. In addition, the abnormal FC strength correlated with the scores in different dimensions of the RFS, such as the negative FC between the Crus I and the left middle temporal gyrus (MTG) was positively associated with intimacy maintenance, and so on.

CONCLUSION

Older women with DS have anomalous FC between the cerebellum and several regions of the cerebrum, which may be related to the neuropathophysiological mechanism of DS in the DS group.

Enhanced rich club connectivity in mild or moderate depression after nonpharmacological treatment: A preliminary study

INTRODUCTION

It has been suggested that the rich club organization in major depressive disorder (MDD) was altered. However, it remained unclear whether the rich club organization could be served as a biomarker that predicted the improvement of clinical symptoms in MDD.

METHODS

The current study included 29 mild or moderate patients with MDD, who were grouped into a treatment group (receiving cognitive behavioral therapy or real-time fMRI feedback treatment) and a no-treatment group. Resting-state MRI scans were obtained for all participants. Graph theory was employed to investigate the treatment-related changes in network properties and rich club organization.

RESULTS

We found that patients in the treatment group had decreased depressive symptom scores and enhanced rich club connectivity following the nonpharmacological treatment. Moreover, the changes in rich club connectivity were significantly correlated with the changes in depressive symptom scores. In addition, the nonpharmacological treatment on patients with MDD increased functional connectivity mainly among the salience network, default mode network, frontoparietal network, and subcortical network. Patients in the no-treatment group did not show significant changes in depressive symptom scores and rich club organization.

CONCLUSIONS

Those results suggested that the remission of depressive symptoms after nonpharmacological treatment in MDD patients was associated with the increased efficiency of global information processing.

Modulation of resting-state functional connectivity in default mode network is associated with the long-term treatment outcome in major depressive disorder

BACKGROUND

Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD.

METHODS

Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD.

RESULTS

Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up.

CONCLUSION

Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.

The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis

Objective

Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD.

Methods

English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity.

Results

Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: p < 0.01, specificity: p < 0.001), graph theory (sensitivity: p < 0.05, specificity: p < 0.01), n > 100 (sensitivity: p < 0.001, specificity: p < 0.001), simens equipment (sensitivity: p < 0.01, specificity: p < 0.001), 3.0T field strength (Sensitivity: p < 0.001, specificity: p = 0.04), and Beck Depression Inventory (BDI) (sensitivity: p = 0.04, specificity: p = 0.06) might be the sources of heterogeneity. Furthermore, the subgroup analysis showed that the sample size (n > 100: sensitivity: 0.71, specificity: 0.72, n < 100: sensitivity: 0.81, specificity: 0.79), the different levels of disease evaluated by the Hamilton Depression Rating Scale (HDRS/HAMD) (mild vs. moderate vs. severe: sensitivity: 0.52 vs. 0.86 vs. 0.89, specificity: 0.62 vs. 0.78 vs. 0.82, respectively), the depression scales in patients with comparable levels of severity. (BDI vs. HDRS/HAMD: sensitivity: 0.86 vs. 0.87, specificity: 0.78 vs. 0.80, respectively), and the features (graph vs. functional connectivity: sensitivity: 0.84 vs. 0.86, specificity: 0.76 vs. 0.78, respectively) selected might be the causes of heterogeneity.

Conclusion

ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings.

Identification of structural brain alterations in adolescents with depressive symptomatology

INTRODUCTION

Depressive symptoms can emerge as early as childhood and may lead to adverse situations in adulthood. Studies have examined structural brain alternations in individuals with depressive symptoms, but findings remain inconclusive. Furthermore, previous studies have focused on adults or used a categorical approach to assess depression. The current study looks to identify grey matter volumes (GMV) that predict depressive symptomatology across a clinically concerning sample of adolescents.

METHODS

Structural MRI data were collected from 338 clinically concerning adolescents (mean age = 15.30 SD=2.07; mean IQ = 101.01 SD=12.43; 132 F). Depression symptoms were indexed via the Mood and Feelings Questionnaire (MFQ). Freesurfer was used to parcellate the brain into 68 cortical regions and 14 subcortical regions. GMV was extracted from all 82 brain areas. Multiple linear regression was used to look at the relationship between MFQ scores and region-specific GMV parameter. Follow up regressions were conducted to look at potential effects of psychiatric diagnoses and medication intake.

RESULTS

Our regression analysis produced a significant model (R2 = 0.446, F(86, 251) = 2.348, p < 0.001). Specifically, there was a negative association between GMV of the left parahippocampal (B = -0.203, p = 0.005), right rostral anterior cingulate (B = -0.162, p = 0.049), and right frontal pole (B = -0.147, p = 0.039) and a positive association between GMV of the left bank of the superior temporal sulcus (B = 0.173, p = 0.029). Follow up analyses produced results proximal to the main analysis.

CONCLUSIONS

Altered regional brain volumes may serve as biomarkers for the development of depressive symptoms during adolescence. These findings suggest a homogeneity of altered cortical structures in adolescents with depressive symptoms.

Brain rhythmic abnormalities in convalescent patients with anti-NMDA receptor encephalitis: a resting-state EEG study

Objective

Anti-N-methyl-D-aspartate receptor encephalitis (anti-NMDARE) is autoimmune encephalitis with a characteristic neuropsychiatric syndrome and persistent cognition deficits even after clinical remission. The objective of this study was to uncover the potential noninvasive and quantified biomarkers related to residual brain distortions in convalescent anti-NMDARE patients.

Methods

Based on resting-state electroencephalograms (EEG), both power spectral density (PSD) and brain network analysis were performed to disclose the persistent distortions of brain rhythms in these patients. Potential biomarkers were then established to distinguish convalescent patients from healthy controls.

Results

Oppositely configured spatial patterns in PSD and network architecture within specific rhythms were identified, as the hyperactivated PSD spanning the middle and posterior regions obstructs the inter-regional information interactions in patients and thereby leads to attenuated frontoparietal and frontotemporal connectivity. Additionally, the EEG indexes within delta and theta rhythms were further clarified to be objective biomarkers that facilitated the noninvasive recognition of convalescent anti-NMDARE patients from healthy populations.

Conclusion

Current findings contributed to understanding the persistent and residual pathological states in convalescent anti-NMDARE patients, as well as informing clinical decisions of prognosis evaluation.

Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study

BACKGROUND

Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease.

METHODS

We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC.

RESULTS

The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls.

CONCLUSION

The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.