Discovering imaging endophenotypes for major depression

Multimodal integration of neuroimaging and genetic data for the diagnosis of mood disorders based on computer vision models

Mood disorders, particularly major depressive disorder (MDD) and bipolar disorder (BD), are often underdiagnosed, leading to substantial morbidity. Harnessing the potential of emerging methodologies, we propose a novel multimodal fusion approach that integrates patient-oriented brain structural magnetic resonance imaging (sMRI) scans with DNA whole-exome sequencing (WES) data. Multimodal data fusion aims to improve the detection of mood disorders by employing established deep-learning architectures for computer vision and machine-learning strategies. We analyzed brain imaging genetic data of 321 East Asian individuals, including 147 patients with MDD, 78 patients with BD, and 96 healthy controls. We developed and evaluated six fusion models by leveraging common computer vision models in image classification: Vision Transformer (ViT), Inception-V3, and ResNet50, in conjunction with advanced machine-learning techniques (XGBoost and LightGBM) known for high-dimensional data analysis. Model validation was performed using a 10-fold cross-validation. Our ViT ⊕ XGBoost fusion model with MRI scans, genomic Single Nucleotide polymorphism (SNP) data, and unweighted polygenic risk score (PRS) outperformed baseline models, achieving an incremental area under the curve (AUC) of 0.2162 (32.03% increase) and 0.0675 (+8.19%) and incremental accuracy of 0.1455 (+25.14%) and 0.0849 (+13.28%) compared to SNP-only and image-only baseline models, respectively. Our findings highlight the opportunity to refine mood disorder diagnostics by demonstrating the transformative potential of integrating diverse, yet complementary, data modalities and methodologies.

Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder

INTRODUCTION

The dynamics of large-scale networks, which are known as distributed sets of functionally synchronized brain regions and include the visual network (VIN), somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network (FPN), and default mode network (DMN), play important roles in emotional and cognitive processes in humans. Although disruptions in these large-scale networks are considered critical for the pathophysiological mechanisms of psychiatric disorders, their role in psychiatric disorders remains unknown. We aimed to elucidate the aberrant dynamics across large-scale networks in patients with schizophrenia (SZ) and mood disorders.

METHODS

We performed energy-landscape analysis to investigate the aberrant brain dynamics of seven large-scale networks across 50 healthy controls (HCs), 36 patients with SZ, and 42 patients with major depressive disorder (MDD) recruited at Wakayama Medical University. We identified major patterns of brain activity using energy-landscape analysis and estimated their duration, occurrence, and ease of transition.

RESULTS

We identified four major brain activity patterns that were characterized by the activation patterns of the DMN and VIN (state 1, DMN (-) VIN (-); state 2, DMN (+) VIN (+); state 3, DMN (-) VIN (+); and state 4, DMN (+) VIN (-)). The duration of state 1 and the occurrence of states 1 and 2 were shorter in the SZ group than in HCs and the MDD group, and the duration of state 3 was longer in the SZ group. The ease of transition between states 3 and 4 was larger in the SZ group than in the HCs and the MDD group. The ease of transition from state 3 to state 4 was negatively associated with verbal fluency in patients with SZ. The current study showed that the brain dynamics was more disrupted in SZ than in MDD.

CONCLUSIONS

Energy-landscape analysis revealed aberrant brain dynamics across large-scale networks between SZ and MDD and their associations with cognitive abilities in SZ, which cannot be captured by conventional functional connectivity analyses. These results provide new insights into the pathophysiological mechanisms underlying SZ and mood disorders.

Exploring the Therapeutic Potential of Gamma-Aminobutyric Acid in Stress and Depressive Disorders through the Gut-Brain Axis

Research conducted on individuals with depression reveals that major depressive disorders (MDDs) coincide with diminished levels of the inhibitory neurotransmitter γ-aminobutyric acid (GABA) in the brain, as well as modifications in the subunit composition of the primary receptors (GABAA receptors) responsible for mediating GABAergic inhibition. Furthermore, there is substantial evidence supporting the significant role of GABA in regulating stress within the brain, which is a pivotal vulnerability factor in mood disorders. GABA is readily available and approved as a food supplement in many countries. Although there is substantial evidence indicating that orally ingested GABA may affect GABA receptors in peripheral tissues, there is comparatively less evidence supporting its direct action within the brain. Emerging evidence highlights that oral GABA intake may exert beneficial effects on the brain and psyche through the gut-brain axis. While GABA enjoys wide consumer acceptance in Eastern Asian markets, with many consumers reporting favorable effects on stress regulation, mood, and sleep, rigorous independent research is still largely lacking. Basic research, coupled with initial clinical findings, makes GABA an intriguing neuro-nutritional compound deserving of clinical studies in individuals with depression and other psychological problems.

Lateralized grey matter volume changes in adolescents versus adults with major depression: SDM-PSI meta-analysis

The current study is the first meta-analysis to examine grey matter volume (GMV) changes in adolescents and across the lifespan in major depressive disorder (MDD). Seed-based d mapping-with permutation of subject images (SDM-PSI) has advantages over previous coordinate-based meta-analytical methods (CBMA), such as reducing bias (via the MetaNSUE algorithm) and including non-statistically significant unreported effects. SDM-PSI was used to analyze 105 whole-brain GMV voxel-based morphometry (VBM) studies comparing 6,530 individuals with MDD versus 6,821 age-matched healthy controls (HC). A laterality effect was observed in which adults with MDD showed lower GMV than adult HC in left fronto-temporo-parietal structures (superior temporal gyrus, insula, Rolandic operculum, and inferior frontal gyrus). However, these abnormalities were not statistically significant for adolescent MDD versus adolescent HC. Instead, adolescent MDD showed lower GMV than adult MDD in right temporo-parietal structures (angular gyrus and middle temporal gyrus). These regional differences may be used as potential biomarkers to predict and monitor treatment outcomes as well as to choose the most effective treatments in adolescents versus adults. Finally, due to the paucity of youth, older adult, and longitudinal studies, future studies should attempt to replicate these GMV findings and examine whether they correlate with treatment response and illness severity.

Biotypes of major depressive disorder identified by a multiview clustering framework

BACKGROUND

The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy.

METHODS

We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls).

RESULTS

Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed.

LIMITATIONS

The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes.

CONCLUSIONS

Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.

The Wistar Kyoto Rat: A Model of Depression Traits

There is an ongoing debate about the value of animal research in psychiatry with valid lines of reasoning stating the limits of individual animal models compared to human psychiatric illnesses. Human depression is not a homogenous disorder; therefore, one cannot expect a single animal model to reflect depression heterogeneity. This limited review presents arguments that the Wistar Kyoto (WKY) rats show intrinsic depression traits. The phenotypes of WKY do not completely mirror those of human depression but clearly indicate characteristics that are common with it. WKYs present despair- like behavior, passive coping with stress, comorbid anxiety, and enhanced drug use compared to other routinely used inbred or outbred strains of rats. The commonly used tests identifying these phenotypes reflect exploratory, escape-oriented, and withdrawal-like behaviors. The WKYs consistently choose withdrawal or avoidance in novel environments and freezing behaviors in response to a challenge in these tests. The physiological response to a stressful environment is exaggerated in WKYs. Selective breeding generated two WKY substrains that are nearly isogenic but show clear behavioral differences, including that of depression-like behavior. WKY and its substrains may share characteristics of subgroups of depressed individuals with social withdrawal, low energy, weight loss, sleep disturbances, and specific cognitive dysfunction. The genomes of the WKY and WKY substrains contain variations that impact the function of many genes identified in recent human genetic studies of depression. Thus, these strains of rats share characteristics of human depression at both phenotypic and genetic levels, making them a model of depression traits.

Sequential fear generalization and network connectivity in trauma exposed humans with and without psychopathology

While impaired fear generalization is known to underlie a wide range of psychopathology, the extent to which exposure to trauma by itself results in deficient fear generalization and its neural abnormalities is yet to be studied. Similarly, the neural function of intact fear generalization in people who endured trauma and did not develop significant psychopathology is yet to be characterized. Here, we utilize a generalization fMRI task, and a network connectivity approach to clarify putative behavioral and neural markers of trauma and resilience. The generalization task enables longitudinal assessments of threat discrimination learning. Trauma-exposed participants (TE; N = 62), compared to healthy controls (HC; N = 26), show lower activity reduction in salience network (SN) and right executive control network (RECN) across the two sequential generalization stages, and worse discrimination learning in SN measured by linear deviation scores (LDS). Comparison of resilient, trauma-exposed healthy control participants (TEHC; N = 31), trauma exposed individuals presenting with psychopathology (TEPG; N = 31), and HC, reveals a resilience signature of network connectivity differences in the RECN during generalization learning measured by LDS. These findings may indicate a trauma exposure phenotype that has the potential to advance the development of innovative treatments by targeting and engaging specific neural dysfunction among trauma-exposed individuals, across different psychopathologies.

Combinatorial panel with endophenotypes from multilevel information of diffusion tensor imaging and lipid profile as predictors for depression

OBJECTIVE

Clinical heterogeneity in major depressive disorder likely reflects the range of etiology and contributing factors in the disorder, such as genetic risk. Identification of more refined subgroups based on biomarkers such as white matter integrity and lipid-related metabolites could facilitate precision medicine in major depressive disorder.

METHODS

A total of 148 participants (15 genetic high-risk participants, 57 patients with first-episode major depressive disorder and 76 healthy controls) underwent diffusion tensor imaging and plasma lipid profiling. Alterations in white matter integrity and lipid metabolites were identified in genetic high-risk participants and patients with first-episode major depressive disorder. Then, shared alterations between genetic high-risk and first-episode major depressive disorder were used to develop an imaging x metabolite diagnostic panel for genetically based major depressive disorder via factor analysis and logistic regression. A fivefold cross-validation test was performed to evaluate the diagnostic panel.

RESULTS

Alterations of white matter integrity in corona radiata, superior longitudinal fasciculus and the body of corpus callosum and dysregulated unsaturated fatty acid metabolism were identified in both genetic high-risk participants and patients with first-episode major depressive disorder. An imaging x metabolite diagnostic panel, consisting of measures for white matter integrity and unsaturated fatty acid metabolism, was identified that achieved an area under the receiver operating characteristic curve of 0.86 and had a significantly higher diagnostic performance than that using either measure alone. And cross-validation confirmed the adequate reliability and accuracy of the diagnostic panel.

CONCLUSION

Combining white matter integrity in corpus callosum, superior longitudinal fasciculus and corona radiata, and unsaturated fatty acid profile may improve the identification of genetically based endophenotypes in major depressive disorder to advance precision medicine strategies.

Alterations of default mode and cingulo-opercular salience network and frontostriatal circuit: A candidate endophenotype of obsessive-compulsive disorder

Background It is gradually becoming clear that obsessive-compulsive disorder (OCD) patients have aberrant resting-state large-scale intrinsic networks of cingulo-opercular salience (SN), default mode (DMN), and front-parietal network (FPN). However, it remains unknown whether unaffected first-degree relatives of OCD patients have these alterations as a vulnerability marker to the disorder. Methods We performed resting-state functional magnetic resonance imaging (rsfMRI) scans of 47 medication-free OCD patients, 21 unaffected healthy first-degree relatives of OCD patients, and 62 healthy control (HC) participants. We explored differences between the three groups in the functional connectivity from SN (seeds: anterior-insula (AI) and dorsal anterior cingulate cortex (dACC)), DMN (seeds: medial prefrontal cortex (MPFC) and posterior parietal cortex (PCC)), and FPN (seeds: dorsolateral prefrontal cortex (DLPFC)). Results Compared to HC, both OCD patients and first-degree relatives showed significantly greater functional connectivity between AI and PCC and between DLPFC and the thalamus. Compared to first-degree relatives and HC, OCD patients showed reduced functional connectivity between PCC and DLPFC, and this altered functional connectivity was negatively correlated with anxiety and depressive symptom within OCD group. Conclusions OCD patients and unaffected first-degree relatives of OCD patients showed overlapping alterations in resting state functional connectivity between the regions of SN and DMN and between DLPFC and the thalamus. Our results suggested that alterations between large-scale intrinsic networks and within the dorsal cognitive cortico-striato-thalamo-cortical (CSTC) circuit could represent endophenotype markers of OCD.

Increased prefrontal cortex connectivity associated with depression vulnerability and relapse

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent mood disorder, characterized by depressed mood, reduced capabilities to concentrate, impaired cognition, as well as a high risk of relapse. Unaffected siblings who have high risks for MDD development and yet without clinical symptoms may be helpful for understanding the neural mechanisms of MDD traits.

METHODS

We investigated both regional fluctuation and inter-regional synchronization in 31 fully remitted MDD patients, 29 unaffected siblings and 43 age, gender, and educational level matched helathy controls (HCs) using resting-state functional magnetic resonance imaging (rs-fMRI). The 17-item HAMD and neurocognitive scales were performed. Fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) strength were investigated.

RESULTS

Compared with healthy control group, patients with remitted MDD and unaffected siblings showed increased fALFF in the left dorsomedial prefrontal cortex (dmPFC) and increased FC between the left dmPFC and the right ventromedial prefrontal cortex (vmPFC). In addition, a negative correlation was observed between the fALFF value in the left dmPFC and the speed of Trail Making Test in the remitted MDD patients. Higher vmPFC-dmPFC FC was positively correlated with Wisconsin Card Sorting Test (WCST) total correct, and negatively correlated with WCST random errors.

CONCLUSIONS

In the absence of clinical symptoms, individuals with remitted MDD and unaffected siblings showed increased fALFF in left dmPFC as well as the vmPFC-dmPFC connectivity. These results suggest a specific trait abnormality in the default mode network associated with vulnerability to MDD, which may have implications for developing effective therapies using this network as a target.

Investigating data-driven biological subtypes of psychiatric disorders using specification-curve analysis

BACKGROUND

Cluster analyses have become popular tools for data-driven classification in biological psychiatric research. However, these analyses are known to be sensitive to the chosen methods and/or modelling options, which may hamper generalizability and replicability of findings. To gain more insight into this problem, we used Specification-Curve Analysis (SCA) to investigate the influence of methodological variation on biomarker-based cluster-analysis results.

METHODS

Proteomics data (31 biomarkers) were used from patients (n = 688) and healthy controls (n = 426) in the Netherlands Study of Depression and Anxiety. In SCAs, consistency of results was evaluated across 1200 k-means and hierarchical clustering analyses, each with a unique combination of the clustering algorithm, fit-index, and distance metric. Next, SCAs were run in simulated datasets with varying cluster numbers and noise/outlier levels to evaluate the effect of data properties on SCA outcomes.

RESULTS

The real data SCA showed no robust patterns of biological clustering in either the MDD or a combined MDD/healthy dataset. The simulation results showed that the correct number of clusters could be identified quite consistently across the 1200 model specifications, but that correct cluster identification became harder when the number of clusters and noise levels increased.

CONCLUSION

SCA can provide useful insights into the presence of clusters in biomarker data. However, SCA is likely to show inconsistent results in real-world biomarker datasets that are complex and contain considerable levels of noise. Here, the number and nature of the observed clusters may depend strongly on the chosen model-specification, precluding conclusions about the existence of biological clusters among psychiatric patients.

Altered brain network in first-episode, drug-naive patients with major depressive disorder

BACKGROUND

Resting-state functional magnetic resonance imaging has been widely used for the assessment of brain functional network, yet with inconsistent results. The present study aimed to investigate intranetwork and internetwork connectivity differences between patients with major depressive disorder (MDD) and healthy controls at the integrity, network and edge levels of 8 well-defined resting state networks.

METHODS

Thirty patients with MDD and sixty-three healthy control subjects were recruited in this study.

RESULTS

Compared with healthy controls, patients with MDD showed increased node degree in the right amygdala and putamen, increased connectivity strength in the deep gray matter network (DGN) and increased functional connectivity in intranetwork and internetwork. Meanwhile, MDD showed decreased connectivity strength in visual network-DGN pair.

LIMITATIONS

The sample size was small, and all patients in this study were of Asian ethnicity, especially Han individuals.

CONCLUSIONS

These findings demonstrate that MDD cases and healthy controls may have divergent intranetwork and internetwork connectivity at an early stage without confounding influence of medication. These differences may underlie cognitive and behavioral alterations in patients with MDD. And these differences may help with the discrimination of patients and healthy people at an early stage of MDD. More studies in the future are warranted to assist in the diagnosis of this burdensome disease.

Glutamatergic System in Depression and Its Role in Neuromodulatory Techniques Optimization

Depressive disorders are among the most common psychiatric conditions and contribute to significant morbidity. Even though the use of antidepressants revolutionized the management of depression and had a tremendous positive impact on the patient's outcome, a significant proportion of patients with major depressive disorder (MDD) show no or partial or response even with adequate treatment. Given the limitations of the prevailing monoamine hypothesis-based pharmacotherapy, glutamate and glutamatergic related pathways may offer an alternative and a complementary option for designing novel intervention strategies. Over the past few decades, there has been a growing interest in understanding the neurobiological underpinnings of glutamatergic dysfunctions in the pathogenesis of depressive disorders and the development of new pharmacological and non-pharmacological treatment options. There is a growing body of evidence for the efficacy of neuromodulation techniques, including transcranial magnetic stimulation, transcutaneous direct current stimulation, transcranial alternating current stimulation, and photo-biomodulation on improving connectivity and neuroplasticity associated with depression. This review attempts to revisit the role of glutamatergic neurotransmission in the etiopathogenesis of depressive disorders and review the current neuroimaging, neurophysiological and clinical evidence of these neuromodulation techniques in the pathophysiology and treatment of depression.

Lower Levels of GABAergic Function Markers in Corticotropin-Releasing Hormone-Expressing Neurons in the sgACC of Human Subjects With Depression

RATIONALE

A previous transcriptome meta-analysis revealed significantly lower levels of corticotropin-releasing hormone (CRH) mRNA in corticolimbic brain regions in major depressive disorder (MDD) subjects, suggesting that cortical CRH-expressing (CRH+) cells are affected in MDD. Rodent studies show that cortical CRH is mostly expressed in GABAergic interneurons; however, the characteristic features of CRH+ cells in human brain cortex and their association with MDD are largely unknown.

METHODS

Subgenual anterior cingulate cortex (sgACC) of human subjects without brain disorders were labeled using fluorescent in situ hybridization (FISH) for CRH and markers of excitatory (SLC17A7), inhibitory (GAD1) neurons, as well as markers of other interneuron subpopulations (PVALB, SST, VIP). MDD-associated changes in CRH+ cell density and cellular CRH expression (n = 6/group) were analyzed. RNA-sequencing was performed on sgACC CRH+ interneurons from comparison and MDD subjects (n = 6/group), and analyzed for group differences. The effect of reduced BDNF on CRH expression was tested in mice with blocked TrkB function.

RESULTS

About 80% of CRH+ cells were GABAergic and 17.5% were glutamatergic. CRH+ GABAergic interneurons co-expressed VIP (52%), SST (7%), or PVALB (7%). MDD subjects displayed lower CRH mRNA levels in GABAergic interneurons relative to comparison subjects without changes in cell density. CRH+ interneurons show transcriptomic profile suggesting lower excitability and less GABA release and reuptake. Further analyses suggested that these molecular changes are not mediated by altered glucocorticoid feedback and potentially occur downstream for a common modulator of neurotrophic function.

SUMMARY

CRH+ cells in human sgACC are a heterogeneous population of GABAergic interneurons, although largely co-expressing VIP. Our data suggest that MDD is associated with reduced markers of inhibitory function in sgACC CRH+ interneurons, and provide further evidence for impaired GABAergic function in the cortex in MDD.

Blood-derived deoxyribonucleic acid methylation clusters associate with adverse social exposures and endophenotypes of stress-related psychiatric illness in a trauma-exposed cohort of women

Adverse social exposures (ASEs) such as low income, low educational attainment, and childhood/adult trauma exposure are associated with variability in brain region measurements of gray matter volume (GMV), surface area (SA), and cortical thickness (CT). These CNS morphometries are associated with stress-related psychiatric illnesses and represent endophenotypes of stress-related psychiatric illness development. Epigenetic mechanisms, such as 5-methyl-cytosine (5mC), may contribute to the biological embedding of the environment but are understudied and not well understood. How 5mC relates to CNS endophenotypes of psychiatric illness is also unclear. In 97 female, African American, trauma-exposed participants from the Grady Trauma Project, we examined the associations of childhood trauma burden (CTQ), adult trauma burden, low income, and low education with blood-derived 5mC clusters and variability in brain region measurements in the amygdala, hippocampus, and frontal cortex subregions. To elucidate whether peripheral 5mC indexes central nervous system (CNS) endophenotypes of psychiatric illness, we tested whether 73 brain/blood correlated 5mC clusters, defined by networks of correlated 5mC probes measured on Illumina's HumanMethylation Epic Beadchip, mediated the relationship between ASEs and brain measurements. CTQ was negatively associated with rostral middle frontal gyrus (RMFG) SA (β =-0.231, p = 0.041). Low income and low education were also associated with SA or CT in a number of brain regions. Seven 5mC clusters were associated with CTQ (pmin = 0.002), two with low education (pmin = 0.010), and three with low income (pmin = 0.007). Two clusters fully mediated the relation between CTQ and RMFG SA, accounting for 47 and 35% of variability, respectively. These clusters were enriched for probes falling in DNA regulatory regions, as well as signal transduction and immune signaling gene ontology functions. Methylome-network analyses showed enrichment of macrophage migration (p = 9 × 10^-8), T cell receptor complex (p = 6 × 10^-6), and chemokine-mediated signaling (p = 7 × 10^-4) pathway enrichment in association with CTQ. Our results support prior work highlighting brain region variability associated with ASEs, while informing a peripheral inflammation-based epigenetic mechanism of biological embedding of such exposures. These findings could also serve to potentiate increased investigation of understudied populations at elevated risk for stress-related psychiatric illness development.

Deletion of Crtc1 leads to hippocampal neuroenergetic impairments associated with depressive-like behavior

Mood disorders (MD) are a major burden on society as their biology remains poorly understood, challenging both diagnosis and therapy. Among many observed biological dysfunctions, homeostatic dysregulation, such as metabolic syndrome (MeS), shows considerable comorbidity with MD. Recently, CREB-regulated transcription coactivator 1 (CRTC1), a regulator of brain metabolism, was proposed as a promising factor to understand this relationship. Searching for imaging biomarkers and associating them with pathophysiological mechanisms using preclinical models can provide significant insight into these complex psychiatric diseases and help the development of personalized healthcare. Here, we used neuroimaging technologies to show that deletion of Crtc1 in mice leads to an imaging fingerprint of hippocampal metabolic impairment related to depressive-like behavior. By identifying a deficiency in hippocampal glucose metabolism as the underlying molecular/physiological origin of the markers, we could assign an energy-boosting mood-stabilizing treatment, ebselen, which rescued behavior and neuroimaging markers. Finally, our results point toward the GABAergic system as a potential therapeutic target for behavioral dysfunctions related to metabolic disorders. This study provides new insights on Crtc1's and MeS's relationship to MD and establishes depression-related markers with clinical potential.

Predicting treatment selections for individuals with major depressive disorder according to functional connectivity subgroups

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent and disabling disease. Currently, patients' treatment choices depend on their clinical symptoms observed by clinicians, which are subjective. Rich evidence suggests that different functional networks' dysfunctions correspond to different intervention preferences. Here, we aimed to develop a prediction model based on data-driven subgroups to provide treatment recommendations.

METHODS

All 630 participants enrolled from four sites underwent functional magnetic resonances imaging at baseline. In the discovery dataset (n=228), we firstly identified MDD subgroups by the hierarchical clustering method using the canonical variates of resting-state functional connectivity (FC) through canonical correlation analyses. The demographic, symptom improvement and FC were compared among subgroups. The preference intervention for each subgroup was also determined. Next, we predicted the individual treatment strategy. Specifically, a patient was assigned into predefined subgroups based on FC similarities and then his/her treatment strategy was determined by the subgroups' preferred interventions.

RESULTS

Three subgroups with specific treatment recommendations were emerged including: (1) a selective serotonin reuptake inhibitors-oriented subgroup with early improvements in working and activities. (2) a stimulation-oriented subgroup with more alleviation in suicide. (3) a selective serotonin noradrenaline reuptake inhibitors-oriented subgroup with more alleviation in hypochondriasis. Through cross-dataset testing respectively conducted on three testing datasets, results showed an overall accuracy of 72.83%.

CONCLUSIONS

Our works revealed the correspondences between subgroups and their treatment preferences and predicted individual treatment strategy based on such correspondences. Our model has the potential to support psychiatrists in early clinical decision making for better treatment outcomes.

Classification of emotion categories based on functional connectivity patterns of the human brain

Neurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity patterns differ across emotion categories. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We thus show preliminary evidence for consistently different sustained functional connectivity patterns for instances of emotion categories particularly within the default mode system.

Resting state functional connectivity subtypes predict discrete patterns of cognitive-affective functioning across levels of analysis among patients with treatment-resistant depression

Resting state functional connectivity (RSFC) in ventral affective (VAN), default mode (DMN) and cognitive control (CCN) networks may partially underlie heterogeneity in depression. The current study used data-driven parsing of RSFC to identify subgroups of patients with treatment-resistant depression (TRD; n = 70) and determine if subgroups generalized to transdiagnostic measures of cognitive-affective functioning relevant to depression (indexed across self-report, behavioral, and molecular levels of analysis). RSFC paths within key networks were characterized using Subgroup-Group Iterative Multiple Model Estimation. Three connectivity-based subgroups emerged: Subgroup A, the largest subset and containing the fewest pathways; Subgroup B, containing unique bidirectional VAN/DMN negative feedback; and Subgroup C, containing the most pathways. Compared to other subgroups, subgroup B was characterized by lower self-reported positive affect and subgroup C by higher self-reported positive affect, greater variability in induced positive affect, worse response inhibition, and reduced striatal tissue iron concentration. RSFC-based categorization revealed three TRD subtypes associated with discrete aberrations in transdiagnostic cognitive-affective functioning that were largely unified across levels of analysis and were maintained after accounting for the variability captured by a disorder-specific measure of depressive symptoms. Findings advance understanding of transdiagnostic brain-behavior heterogeneity in TRD and may inform novel treatment targets for this population.

BNCPL: Brain-Network-based Convolutional Prototype Learning for Discriminating Depressive Disorders

Deep learning has shown great potential to adaptively learn hidden patterns from high dimensional neuroimaging data, so as to extract subtle group differences. Motivated by the convolutional neural networks and prototype learning, we developed a brain-network-based convolutional prototype learning model (BNCPL), which can learn representations that simultaneously maximize inter-class separation while minimize within-class distance. When applying BNCPL to distinguish 208 depressive disorders from 210 healthy controls using resting-state functional connectivity (FC), we achieved an accuracy of 71.0% in multi-site pooling classification (3 sites), with 2.4-7.2% accuracy increase compared to 3 traditional classifiers and 2 alternative deep neural networks. Saliency map was also used to examine the most discriminative FCs learned by the model; the prefrontal-subcortical circuits were identified, which were also correlated with disease severity and cognitive ability. In summary, by integrating convolutional prototype learning and saliency map, we improved both the model interpretability and classification performance, and found that the dysregulation of the functional prefrontal-subcortical circuit may play a pivotal role in discriminating depressive disorders from healthy controls.

Diffusion tensor imaging brain structural clustering patterns in major depressive disorder

Using magnetic resonance diffusion tensor imaging data from 45 patients with major depressive disorder (MDD) and 41 healthy controls (HCs), network indices based on a 246-region Brainnetcome Atlas were investigated in the two groups, and in the MDD subgroups that were subgrouped based on their duration of the disease. Correlation between the network indices and the duration of illness was also examined. Differences were observed between the MDDS subgroup (short disease duration) and the HC group, but not between the MDD and HC groups. Compared with the HCs, the clustering coefficient (CC) values of MDDS were higher in precentral gyrus, and caudal lingual gyrus; the CC of MDDL subgroup (long disease duration) was higher in postcentral gyrus and dorsal granular insula in the right hemisphere. Network resilience analyses showed that the MDDS group was higher than the HC group, representing relatively more randomized networks in the diseased brains. The correlation analyses showed that the caudal lingual gyrus in the right hemisphere and the rostral lingual gyrus in the left hemisphere were particularly correlated with disease duration. The analyses showed that duration of the illness appears to have an impact on the networking patterns. Networking abnormalities in MDD patients could be blurred or hidden by the heterogeneity of the MDD clinical subgroups. Brain plasticity may introduce a recovery effect to the abnormal network patterns seen in patients with a relative short term of the illness, as the abnormalities may disappear in MDDL .

Reduced frontostriatal response to expected value and reward prediction error in remitted monozygotic twins with mood disorders and their unaffected high-risk co-twins

BACKGROUND

Depressive episodes experienced in unipolar (UD) and bipolar (BD) disorders are characterized by anhedonia and have been associated with abnormalities in reward processes related to reward valuation and error prediction. It remains however unclear whether these deficits are associated with familial vulnerability to mood disorders.

METHODS

In a functional magnetic resonance imaging study, we evaluated differences in the expected value (EV) and reward prediction error (RPE) signals in ventral striatum (VS) and prefrontal cortex between three groups of monozygotic twins: affected twins in remission for either UD or BD (n = 53), their high-risk unaffected co-twins (n = 34), and low-risk twins with no family history of mood disorders (n = 25).

RESULTS

Compared to low-risk twins, affected twins showed lower EV signal bilaterally in the frontal poles and lower RPE signal bilaterally in the VS, left frontal pole and superior frontal gyrus. The high-risk group did not show a significant change in the EV or RPE signals in frontostriatal regions, yet both reward signals were consistently lower compared with low-risk twins in all regions where the affected twins showed significant reductions.

CONCLUSION

Our findings strengthen the notion that reduced valuation of expected rewards and reduced error-dependent reward learning may underpin core symptom of depression such as loss of interest in rewarding activities. The trend reduction in reward-related signals in unaffected co-twins warrants further investigation of this effect in larger samples and prospective follow-up to confirm possible association with increased familial vulnerability to mood disorders.

Associations between childhood family emotional health, fronto-limbic grey matter volume, and saliva 5mC in young adulthood

Background

Poor family emotional health (FEH) during childhood is prevalent and impactful, and likely confers similar neurodevelopmental risks as other adverse social environments. Pointed FEH study efforts are underdeveloped, and the mechanisms by which poor FEH are biologically embedded are unclear. The current exploratory study examined whether variability in 5-methyl-cytosine (5mC) and fronto-limbic grey matter volume may represent pathways through which FEH may become biologically embedded.

Results

In 98 university students aged 18–22 years, retrospective self-reported childhood FEH was associated with right hemisphere hippocampus (b = 10.4, p = 0.005), left hemisphere amygdala (b = 5.3, p = 0.009), and right hemisphere amygdala (b = 5.8, p = 0.016) volumes. After pre-processing and filtering to 5mC probes correlated between saliva and brain, analyses showed that childhood FEH was associated with 49 5mC principal components (module eigengenes; MEs) (p_range = 3 × 10^–6 to 0.047). Saliva-derived 5mC MEs partially mediated the association between FEH and right hippocampal volume (Burlywood ME indirect effect b = − 111, p = 0.014), and fully mediated the FEH and right amygdala volume relationship (Pink4 ME indirect effect b = − 48, p = 0.026). Modules were enriched with probes falling in genes with immune, central nervous system (CNS), cellular development/differentiation, and metabolic functions.

Conclusions

Findings extend work highlighting neurodevelopmental variability associated with adverse social environment exposure during childhood by specifically implicating poor FEH, while informing a mechanism of biological embedding. FEH-associated epigenetic signatures could function as proxies of altered fronto-limbic grey matter volume associated with poor childhood FEH and inform further investigation into primarily affected tissues such as endocrine, immune, and CNS cell types.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01056-y.

Shared and Distinct Fractional Amplitude of Low-Frequency Fluctuation Patterns in Major Depressive Disorders With and Without Gastrointestinal Symptoms

Objective: Gastrointestinal (GI) symptoms are fairly common somatic symptoms in depressed patients. The purpose of this study was to explore the influence of concomitant GI symptoms on the fractional amplitude of low-frequency fluctuation (fALFF) patterns in patients with major depressive disorder (MDD) and investigate the connection between aberrant fALFF and clinical characteristics. Methods: This study included 35 MDD patients with GI symptoms (GI-MDD patients), 17 MDD patients without GI symptoms (nGI-MDD patients), and 28 healthy controls (HCs). The fALFF method was used to analyze the resting-state functional magnetic resonance imaging data. Correlation analysis and pattern classification were employed to investigate the relationship of the fALFF patterns with the clinical characteristics of patients. Results: GI-MDD patients exhibited higher scores in the HRSD-17 and suffered more severe insomnia, anxiety/somatization, and weight loss than nGI-MDD patients. GI-MDD patients showed higher fALFF in the right superior frontal gyrus (SFG)/middle frontal gyrus (MFG) and lower fALFF in the left superior medial prefrontal cortex (MPFC) compared with nGI-MDD patients. A combination of the fALFF values of these two clusters could be applied to discriminate GI-MDD patients from nGI-MDD patients, with accuracy, sensitivity, and specificity of 86.54, 94.29, and 70.59%, respectively. Conclusion: GI-MDD patients showed more severe depressive symptoms. Increased fALFF in the right SFG/MFG and decreased fALFF in the left superior MPFC might be distinctive neurobiological features of MDD patients with GI symptoms.

Glutamate and GABA Homeostasis and Neurometabolism in Major Depressive Disorder

Major depressive disorder (MDD) is a leading cause of distress, disability, and suicides. As per the latest WHO report, MDD affects more than 260 million people worldwide. Despite decades of research, the underlying etiology of depression is not fully understood. Glutamate and γ-aminobutyric acid (GABA) are the major excitatory and inhibitory neurotransmitters, respectively, in the matured central nervous system. Imbalance in the levels of these neurotransmitters has been implicated in different neurological and psychiatric disorders including MDD. ¹H nuclear magnetic resonance (NMR) spectroscopy is a powerful non-invasive method to study neurometabolites homeostasis in vivo. Additionally, ¹³C-NMR spectroscopy together with an intravenous administration of non-radioactive ¹³C-labeled glucose or acetate provides a measure of neural functions. In this review, we provide an overview of NMR-based measurements of glutamate and GABA homeostasis, neurometabolic activity, and neurotransmitter cycling in MDD. Finally, we highlight the impact of recent advancements in treatment strategies against a depressive disorder that target glutamate and GABA pathways in the brain.

"Common Currency" Between Experience and Brain: Spatiotemporal Psychopathology of the Resting State in Depression

The aim of this contribution is to introduce Spatiotemporal Psychopathology and the way it may complement and extent Phenomenological Psychopathology by bridging the methodological gap between the brain and experience. In the first part, I will provide examples for spatiotemporal correspondence between neuronal and psychopathological features. Specifically, I will discuss how spatial changes in the brain's spontaneous activity translate into abnormal experience of the self in major depressive disorder (MDD). Finally, I will briefly discuss the method of such Spatiotemporal Psychopathology and distinguish it from the methods relied on in other forms of Psychopathology with a special focus on showing the continuity between Spatiotemporal and Phenomenological Psychopathology.

Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients

BACKGROUND

Major depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom clusters and networks have mostly been studied using cross-sectional designs, temporal dynamics of symptoms within patients may yield information that facilitates personalized medicine. Here, we aim to cluster depressive symptom dynamics through dynamic time warping (DTW) analysis.

METHODS

The 17-item Hamilton Rating Scale for Depression (HRSD-17) was administered every 2 weeks for a median of 11 weeks in 255 depressed inpatients. The DTW analysis modeled the temporal dynamics of each pair of individual HRSD-17 items within each patient (i.e., 69,360 calculated "DTW distances"). Subsequently, hierarchical clustering and network models were estimated based on similarities in symptom dynamics both within each patient and at the group level.

RESULTS

The sample had a mean age of 51 (SD 15.4), and 64.7% were female. Clusters and networks based on symptom dynamics markedly differed across patients. At the group level, five dynamic symptom clusters emerged, which differed from a previously published cross-sectional network. Patients who showed treatment response or remission had the shortest average DTW distance, indicating denser networks with more synchronous symptom trajectories.

CONCLUSIONS

Symptom dynamics over time can be clustered and visualized using DTW. DTW represents a promising new approach for studying symptom dynamics with the potential to facilitate personalized psychiatric care.

[Non-motor conditions in patients with cervical dystonia]

OBJECTIVE

To study non-motor conditions in people with diabetes in comparison with patients with cervicalgia.

MATERIAL AND METHODS

The study included 170 people. The main group consisted of 120 respondents with cervical dystonia (CD) aged 27 to 82 years. The diagnosis of CD was based on the Clinical guidelines for the diagnosis and treatment of dystonia adopted by the European Federation of Neurological Societies, the Society for Movement Disorders and the All-Russian Society of Neurologists. The control group included 50 patients, aged 25 to 82 years, with pain in the cervical spine due to muscle-tonic and myofascial syndromes. A Visual Analogue scale, the Hospital Anxiety and Depression Scale (HADS), the Multidimensional Fatigue Inventory (MFI-20), the Pittsburgh Sleep Quality Index (PSQI) were administered to study the asthenic syndrome in all patients.

RESULTS AND CONCLUSION

Pain, anxiety, depression, asthenic syndrome, insomnia are statistically significant non-motor conditions in patients with CD compared with patients with cervicalgia. CD significantly affects the physical and psychological aspects, worsening the quality of life of these patients. The following gender differences are identified: in women with CD, non-motor disorders (anxiety, depression, general and physical asthenia, insomnia) are significantly more pronounced and the quality of life is significantly reduced compared to men with CD. For the successful treatment of CD, a multimodal approach is needed that provides the treatment of not only motor, but also non-motor disorders. Early detection and treatment of comorbid conditions is an important step in the treatment of CD.

Longitudinal changes of amplitude of low-frequency fluctuations in MDD patients: A 6-month follow-up resting-state functional magnetic resonance imaging study

OBJECTIVE

The aim of this study includes: (1) using resting-state functional magnetic resonance imaging (rsfMRI) to explore the aberrant brain regional spontaneous brain activities in acute major depressive disorder (MDD) patients; (2) to determine whether the abnormalities could be restored after 6 months of antidepressant treatment; (3) to investigate whether the differences in regional spontaneous brain activities are associated with clinical variables in MDD.

METHOD

RsfMRI scanning was performed in 149 MDD patients and 122 healthy control (HC) subjects at baseline. After 6 months of antidepressant treatment, rsfMRI scanning was reperformed in remitted MDD patients (MDD-R) (n=63). The characteristics of the amplitude of low-frequency fluctuations (ALFF), and the relationship between the fMRI representatives and clinical variables in the MDD group were analyzed.

RESULTS

(1) Compared to healthy controls, significantly decreased ALFF in the right precuneus/posterior cingulate cortex (PCUN/PCC) was detected in MDD. (2) The ALFF value of precuneus in MDD-R group did not change significantly after a 6-month antidepressant treatment and was still lower than the HC group when remission was achieved (P = 0.002). (3) No correlations were found between ALFF in the right PCUN/PCC and Hamilton Depression Rating Scale(HAMD) total score, illness duration, age of onset, and the number of episodes in the baseline MDD group. The ALFF change was not correlated with depressive symptom improvement in MDD-R group.

CONCLUSIONS

The reduction of ALFF in the precuneus persisted in MDD who achieved clinical remission, suggesting that the decreased ALFF in PCUN/PCC may be a trait marker of MDD.

Using resting-state intrinsic network connectivity to identify suicide risk in mood disorders

BACKGROUND

Little is known about the neural substrates of suicide risk in mood disorders. Improving the identification of biomarkers of suicide risk, as indicated by a history of suicide-related behavior (SB), could lead to more targeted treatments to reduce risk.

METHODS

Participants were 18 young adults with a mood disorder with a history of SB (as indicated by endorsing a past suicide attempt), 60 with a mood disorder with a history of suicidal ideation (SI) but not SB, 52 with a mood disorder with no history of SI or SB (MD), and 82 healthy comparison participants (HC). Resting-state functional connectivity within and between intrinsic neural networks, including cognitive control network (CCN), salience and emotion network (SEN), and default mode network (DMN), was compared between groups.

RESULTS

Several fronto-parietal regions (k > 57, p < 0.005) were identified in which individuals with SB demonstrated distinct patterns of connectivity within (in the CCN) and across networks (CCN-SEN and CCN-DMN). Connectivity with some of these same regions also distinguished the SB group when participants were re-scanned after 1-4 months. Extracted data defined SB group membership with good accuracy, sensitivity, and specificity (79-88%).

CONCLUSIONS

These results suggest that individuals with a history of SB in the context of mood disorders may show reliably distinct patterns of intrinsic network connectivity, even when compared to those with mood disorders without SB. Resting-state fMRI is a promising tool for identifying subtypes of patients with mood disorders who may be at risk for suicidal behavior.

Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder

Major depressive disorder is a debilitating condition with diverse neuroimaging correlates, including cortical thinning in medial prefrontal cortex and altered functional connectivity of cortical association networks. However, the molecular bases of these imaging markers remain ambiguous, despite a need for treatment targets and mechanisms. Here, we advance cross-modal approaches to identify cell types and gene transcripts associated with depression-implicated cortex. Across multiple population-imaging datasets (combined N ≥ 23,723) and ex vivo patient cortical tissue, somatostatin interneurons and astrocytes emerge as replicable cell-level correlates of depression and negative affect. These data identify transcripts, cell types, and molecular processes associated with neuroimaging markers of depression and offer a roadmap for integrating in vivo clinical imaging with genetic and postmortem patient transcriptional data.

Major depressive disorder emerges from the complex interactions of biological systems that span genes and molecules through cells, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to depression requires a multiscale approach, encompassing measures of brain structure and function as well as genetic and cell-specific transcriptional data. Here, we examine anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Brain Genomics Superstruct Project, and Enhancing NeuroImaging through Meta Analysis (ENIGMA; combined n ≥ 23,723). Integrative analyses incorporate measures of cortical gene expression, postmortem patient transcriptional data, depression genome-wide association study (GWAS), and single-cell gene transcription. Neuroimaging correlates of depression and negative affect were consistent across three independent datasets. Linking ex vivo gene down-regulation with in vivo neuroimaging, we find that transcriptional correlates of depression imaging phenotypes track gene down-regulation in postmortem cortical samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data reveal somatostatin interneurons and astrocytes to be consistent cell associates of depression, through both in vivo imaging and ex vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS-derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multiscale approaches, the transcriptional correlates of depression-linked brain function and structure were enriched for disorder-relevant molecular pathways. These findings bridge levels to connect specific genes, cell classes, and biological pathways to in vivo imaging correlates of depression.

Chronic postnatal chemogenetic activation of forebrain excitatory neurons evokes persistent changes in mood behavior

Early adversity is a risk factor for the development of adult psychopathology. Common across multiple rodent models of early adversity is increased signaling via forebrain Gq-coupled neurotransmitter receptors. We addressed whether enhanced Gq-mediated signaling in forebrain excitatory neurons during postnatal life can evoke persistent mood-related behavioral changes. Excitatory hM3Dq DREADD-mediated chemogenetic activation of forebrain excitatory neurons during postnatal life (P2–14), but not in juvenile or adult windows, increased anxiety-, despair-, and schizophrenia-like behavior in adulthood. This was accompanied by an enhanced metabolic rate of cortical and hippocampal glutamatergic and GABAergic neurons. Furthermore, we observed reduced activity and plasticity-associated marker expression, and perturbed excitatory/inhibitory currents in the hippocampus. These results indicate that Gq-signaling-mediated activation of forebrain excitatory neurons during the critical postnatal window is sufficient to program altered mood-related behavior, as well as functional changes in forebrain glutamate and GABA systems, recapitulating aspects of the consequences of early adversity.

Stress and adversity in early childhood can have long-lasting effects, predisposing people to mental illness and mood disorders in adult life. The weeks immediately before and after birth are critical for establishing key networks of neurons in the brain. Therefore, any disruption to these neural circuits during this time can be detrimental to emotional development. However, it is still unclear which cellular mechanisms cause these lasting changes in behavior.

Studies in animals suggest that these long-term effects could result from abnormalities in a few signaling pathways in the brain. For example, it has been proposed that overstimulating the cells that activate circuits in the forebrain – also known as excitatory neurons – may contribute to the behavioral changes that persist into adulthood.

To test this theory, Pati et al. used genetic engineering to modulate a signaling pathway in male mice, which is known to stimulate excitatory neurons in the forebrain. The experiments showed that prolonged activation of excitatory neurons in the first two weeks after birth resulted in anxious and despair-like behaviors as the animals aged. The mice also displayed discrepancies in how they responded to certain external sensory information, which is a hallmark of schizophrenia-like behavior. However, engineering the same changes in adolescent and adult mice had no effect on their mood-related behaviors.

This animal study reinforces just how critical the first few weeks of life are for optimal brain development. It provides an insight into a possible mechanism of how disruption during this time could alter emotional behavior. The findings are also relevant to psychiatrists interested in the underlying causes of mental illness after early childhood adversity.

A study on BOLD fMRI of the brain basic activities of MDD and the first-degree relatives

Introduction: The present study aims to explore the characteristics and differences of the ReHo, ALFF and fALFF of brain in the resting state of depression and first-degree relatives, in order to identify candidate central prodromal biomarkers of depression.Method: Three groups of medication-free patients (39-59 years old) was involved in this study, including the patients with major depression disorder (MDD group, n = 15), healthy volunteers with first-degree relatives with MDD (first-degree relatives group, n = 15), healthy volunteers with no personal or family history of MDD (the control group [HC], n = 15). Participants underwent functional MRI while staying in a resting state after a conventional MRI scanning on a clinical 3 T system(Siemens Skyra, Germany).Results: The ReHo, ALFF and fALFF values are different in brain of MDD, first-degree relatives, and HC (p<.05). MDD patients exhibited abnormal spontaneous activity in multiple brain regions which are closely related to emotion regulation and perception. The present findings provide further insight into the pathological mechanisms underlying MDD.Conclusion: With the widespread abnormal values of brain in MDD and first-degree relatives measured, we can get a hypothesis that these abnormalities may be associated with cognitive network disorders and emotional distress in MDD.Key pointsThe fMRI could increase the early validity of MDD as a new diagnostic and disease-monitoring tool.Monitoring ReHo, ALFF, fALFF values using fMRI can provide insight into the presence and evolution of MDD disease and permit objective evaluation of brain abnormalities.It appears that ReHo, ALFF, fALFF could be used as markers for monitoring disease progression and treatment effects in MDD patients in the future.

Reduced tract length of the medial forebrain bundle and the anterior thalamic radiation in bipolar disorder with melancholic depression

BACKGROUND

The supero-lateral medial forebrain bundle (slMFB) and the anterior thalamic radiation (ATR) play a core role in reward anticipation and motivational processes. In this study, the slMFB and the ATR were investigated in a group of depressed bipolar disorder (BD) and in healthy controls (HC) using tract length as a measure of fibre geometry and fractional anisotropy (FA) as a measure of white matter microstructure. We hypothesized reduced tract length and FA of the slMFB and the ATR in BD. We expect alterations to be driven by the melancholic subtype.

METHODS

Nineteen depressed patients with BD and 19 HC matched for age and gender underwent diffusion-weighted magnetic resonance imaging (MRI) scans. Diffusion tensor imaging (DTI) based tractography was used to reconstruct bilateral slMFB and ATR. Mean tract length and FA were computed for the slMFB and the ATR. Mixed-model ANCOVAs and post-hoc ANCOVAs, controlling for age and intracranial volume, were used to compare tract length and FA of bilateral slMFB and ATR between HC and BD and between HC and subgroups with melancholic and non-melancholic symptoms.

RESULTS

In BD we found a significantly shortened tract length of the right slMFB and ATR in BD compared to HC. Subgroup analyses showed that these findings were driven by the melancholic subgroup. Mean-FA did not differ between HC and BD.

LIMITATIONS

Sample size CONCLUSIONS: Tract length of the right slMFB and the right ATR is reduced in BD. Those changes of fibre geometry are driven by the melancholic subtype.

Early life stress and glutamate neurotransmission in major depressive disorder

Early life stress (ELS) and glutamate neurotransmission have been implicated in the pathophysiology of major depressive disorder (MDD). In non-human primates, ELS was positively correlated with cortical Glx (i.e., glutamate + glutamine). However, the relationship between ELS and cortical glutamate in adult patients with MDD is not fully known. Using ¹H Magnetic Resonance Spectroscopy (MRS), we conducted exploratory analyses measuring occipital cortical glutamate and glutamine levels in 36 medication-free patients with MDD. In a subsample (n=11), we measured dynamic glutamate/glutamine cycling (V_cycle) using advanced ¹³C MRS methods. ELS history was assessed using Early-life Trauma Inventory (ETI). Exploratory analyses suggest a relationship between ETI and glutamine as reflected by a significant positive correlation between ETI scores and occipital glutamine (r_s=0.39, p=0.017) but not glutamate. Post-hoc analyses showed that the association with glutamine was driven by the ETI emotional abuse (ETI-EA) subscale (r_s=0.39, p=0.02). V_cycle correlation with ETI was at trend level (r_s=0.55, p=0.087) and significantly correlated with ETI-EA (r_s=0.67, p=0.03). In this small sample of patients with MDD, those with childhood emotional abuse appear to have increased occipital glutamate neurotransmission as reflected by increased glutamate/glutamine cycling and glutamine level. Future studies would be needed to confirm this pilot evidence and to examine whether ELS effects on glutamate neurotransmission underlie the relationship between ELS and psychopathology.

Identify depressive phenotypes by applying RDOC domains to the PHQ-9

Major depression consists of multiple phenotypic traits. Our objective was to characterize depressive phenotypes in the patient health questionnaire (PHQ)-9 using the Research Domain Criteria (RDoC) research framework. Cross-sectional data were examined from the 2013-2014 (N = 5397) and 2015-2016 (N = 5164) National Health and Nutrition Examination Survey, a large, nationally representative U.S. sample. Using both factor analysis and qualitative analysis in mapping scale items along RDoC domains, a four factor model was found to be theoretically appropriate and had an excellent model fit for the PHQ-9. The factor structure consisted of phenotypes describing Negative Valence Systems and Externalizing (anhedonia and depression), Negative Valence Systems and Internalizing (depression, guilt and self-harm), Arousal and Regulatory Systems (sleep, fatigue and appetite) and Cognitive and Sensorimotor Systems (concentration and psychomotor). High correlation between these phenotypes did indicate screening and monitoring for depression study population using a single depression score is likely useful in most circumstances. In multiple indicator multiple cause analysis, differences in the means of the phenotypic traits were found by age, race/ethnicity, sex, and number of comorbidities. Future research should explore whether phenotype expression derived from readily available self-rated depression scales can help to inform more personalized care.

Striatal reactivity to reward under threat-of-shock and working memory load in adults at increased familial risk for major depression: A preliminary study

INTRODUCTION

Anhedonia, a core symptom of Major Depressive Disorder (MDD), manifests as a lack or loss of motivation as reflected by decreased reward responsiveness, at both behavioral and neural (i.e., striatum) levels. Exposure to stressful life events is another important risk factor for MDD. However, the mechanisms linking reward-deficit and stress to MDD remain poorly understood. Here, we explore whether the effects of stress exposure on reward processing might differentiate between Healthy Vulnerable adults (HVul, i.e., positive familial MDD) from Healthy Controls (HCon). Furthermore, the well-described reduction in cognitive resources in MDD might facilitate the stress-induced decrease in reward responsiveness in HVul individuals. Accordingly, this study includes a manipulation of cognitive resources to address the latter possibility.

METHODS

16 HVul (12 females) and 16 gender- and age-matched HCon completed an fMRI study, during which they performed a working memory reward task. Three factors were manipulated: reward (reward, no-reward), cognitive resources (working memory at low and high load), and stress level (no-shock, unpredictable threat-of-shock). Only the reward anticipation phase was analyzed. Imaging analyses focused on striatal function.

RESULTS

Compared to HCon, HVul showed lower activation in the caudate nucleus across all conditions. The HVul group also exhibited lower stress-related activation in the nucleus accumbens, but only in the low working memory (WM) load condition. Moreover, while stress potentiated putamen reactivity to reward cues in HVul when the task was more demanding (high WM load), stress blunted putamen reactivity in both groups when no reward was at stake.

CONCLUSION

Findings suggest that HVul might be at increased risk of developing anhedonic symptoms due to weaker encoding of reward value, higher difficulty to engage in goal-oriented behaviors and increased sensitivity to negative feedback, particularly in stressful contexts. These findings open new avenues for a better understanding of the mechanisms underlying how the complex interaction between the systems of stress and reward responsiveness contribute to the vulnerability to MDD, and how cognitive resources might modulate this interaction.

Quantifying the level of difficulty to treat major depressive disorder with antidepressants: Treatment Resistance to Antidepressants Evaluation Scale

BACKGROUND

The present study aimed to develop a new scale to evaluate the level of difficulty in treating major depressive disorder with antidepressants based on the lifetime treatment profile.

METHODS

In addition to evaluating the difficulty of treatment with antidepressants (A subscale), the Treatment Resistance to Antidepressants Evaluation Scale (TRADES) is comprised of a subscale to account for the attributes that compromise the efficacy of treatment (B subscale). One hundred and six participants aged 18 to 65 years with remitted major depressive disorder were enrolled. Eligible cases were those with at least 2 years from disease onset until the scoring date of the TRADES (the index date), with a complete treatment record. Various psychosocial and clinical features, such as neuroticism, harm avoidance, and utilization of psychiatric services, were used to validate the TRADES.

RESULTS

The mean duration of the course before and after the index date were 5.5 ± 3.5 and 3.1 ± 1.7 years, respectively. In a multiple regression analysis, the final total scores of the TRADES independently correlated with higher levels of neuroticism and harm avoidance. Total scores were also associated with a higher utilization of psychiatric outpatient and admission services before the index date. Furthermore, it is thought that total scores could predict a higher number of visits to psychiatric outpatient, emergency, and admission services following the index date.

CONCLUSIONS

The TRADES has acceptable validity and could help to quantify the level of treatment difficulty with antidepressants in major depressive disorder.

Unaltered Brain GABA Concentrations and Resting fMRI Activity in Functional Dyspepsia With and Without Comorbid Depression

BACKGROUND

GABA-deficit characterizes depression (MDD), which is highly comorbid with Functional Dyspepsia (FD). We examined brain GABA concentrations and resting activities in post-prandial distress subtype FD (FD-PDS) patients with and without MDD.

METHODS

24 female age/education-matched FD-PDS with comorbid MDD (FD-PDS-MDD), non-depressed FD-PDS, and healthy controls each were compared on GABA concentrations, resting fMRI (fALFF) in bilateral pregenual anterior cingulate (pgACC), left dorsolateral prefrontal cortex (DLPFC), insula, and somatosensory cortex (SSC).

RESULTS

FD-PDS-MDD patients had mild though elevated depressive symptoms. FD-PDS patients had generally mild dyspeptic symptoms. No significant between-group differences in GABA or fALFF were found. No significant correlations were found between GABA and depressive/dyspeptic symptoms after Bonferroni correction. In patients, GABA correlated positively with left insula fALFF (r = 0.38, Bonferroni-corrected p = .03).

CONCLUSION

We did not find altered GABA concentrations or brain resting activity in FD-PDS or its MDD comorbidity. The neurochemical link between MDD and FD remains elusive.

The depression GWAS risk allele predicts smaller cerebellar gray matter volume and reduced SIRT1 mRNA expression in Chinese population

Major depressive disorder (MDD) is recognized as a primary cause of disability worldwide, and effective management of this illness has been a great challenge. While genetic component is supposed to play pivotal roles in MDD pathogenesis, the genetic and phenotypic heterogeneity of the illness has hampered the discovery of its genetic determinants. In this study, in an independent Han Chinese sample (1824 MDD cases and 3031 controls), we conducted replication analyses of two genetic loci highlighted in a previous Chinese MDD genome-wide association study (GWAS), and confirmed the significant association of a single nucleotide polymorphism (SNP) rs12415800 near SIRT1. Subsequently, using hypothesis-free whole-brain analysis in two independent Han Chinese imaging samples, we found that individuals carrying the MDD risk allele of rs12415800 exhibited aberrant gray matter volume in the left posterior cerebellar lobe compared with those carrying the non-risk allele. Besides, in independent Han Chinese postmortem brain and peripheral blood samples, the MDD risk allele of rs12415800 predicted lower SIRT1 mRNA levels, which was consistent with the reduced expression of this gene in MDD patients compared with healthy subjects. These results provide further evidence for the involvement of SIRT1 in MDD, and suggest that this gene might participate in the illness via affecting the development of cerebellum, a brain region that is potentially underestimated in previous MDD studies.

I am Me: Brain systems integrate and segregate to establish a multidimensional sense of self

Humans experience a sense of self, which is proposed to emerge from the integration of intrinsic and extrinsic self-processing through the propagation of information across brain systems. Using a novel functional magnetic resonance imaging (fMRI) paradigm, we tested this hypothesis in a non-clinical sample by modulating the intrinsic and extrinsic self-relatedness of auditory action consequences in terms of identity and agency, respectively. In addition, the relevance of individual traits associated with altered self-experiences (e.g., psychosis-like experiences) was examined. The task-evoked fMRI results showed distinctive associations between the neural coding of identity and negative affect traits, and between agency and psychosis-like experiences. Most importantly, regarding the functional connectivity analysis, graph theoretical measures demonstrated that the simultaneous processing of identity and agency relies on the functional integration and segregation of default mode, sensorimotor, language, and executive brain networks. Finally, cross-network interactions mediated by executive and sensorimotor regions were negatively associated with psychosis-like experiences when the intrinsic and extrinsic self-relatedness of action consequences conflicted. These findings provide evidence that the self is a multidimensional phenomenon rooted in the functional interactions between large-scale neuronal networks. Such interactions may have particular relevance for self-experience alterations.

Effects of Serotonin Transporter Gene Variation on Impulsivity Mediated by Default Mode Network: A Family Study of Depression

Serotonergic neurotransmission, potentially through effects on the brain's default mode network (DMN), may regulate aspects of attention including impulse control. Indeed, genetic variants of the serotonin transporter (5-HTT) have been implicated in impulsivity and related psychopathology. Yet it remains unclear the mechanism by which the 5-HTT genetic variants contribute to individual variability in impulse control. Here, we tested whether DMN connectivity mediates an association between the 5-HTT genetic variants and impulsivity. Participants (N = 92) were from a family cohort study of depression in which we have previously shown a broad distribution of 5-HTT variants. We genotyped for 5-HTTLPR and rs25531 (stratified by transcriptional efficiency: 8 low/low, 53 low/high, and 31 high/high), estimated DMN structural connectivity using diffusion probabilistic tractography, and assessed behavioral measures of impulsivity (from 12 low/low, 48 low/high, and 31 high/high) using the Continuous Performance Task. We found that low transcriptional efficiency genotypes were associated with decreased connection strength between the posterior DMN and the superior frontal gyrus (SFG). Path modeling demonstrated that decreased DMN-SFG connectivity mediated the association between low-efficiency genotypes and increased impulsivity. Taken together, this study suggests a gene-brain-behavior pathway that perhaps underlies the role of the serotonergic neuromodulation in impulse control.

Abnormal resting-state cerebral-limbic functional connectivity in bipolar depression and unipolar depression

Background

Distinctive patterns of functional connectivity (FC) abnormalities in neural circuitry has been reported in patients with bipolar depression (BD) and unipolar depression (UD). However, it is unclear that whether this distinct functional connectivity patterns are diagnosis specific between BD and UD. This study aimed to compare patterns of functional connectivity among BD, UD and healthy controls (HC) and determine the distinct functional connectivity patterns which can differentiate BD from UD.

Method

Totally 23 BD, 22 UD, and 24 HC were recruited to undergo resting-state fMRI scanning. FC between each pair of brain regions was calculated and compared among the three groups, the associations of FC with depressive symptom were also analyzed.

Results

Both patient groups showed significantly decreased cerebral-limbic FC located between the default mode network [posterior cingulated gyrus (PCG) and precuneus] and limbic regions (hippocampus, amygdala and thalamus) than HC. Moreover, the BD group exhibited more decreased FC mainly in the cortical regions (middle temporal gyrus, PCG, medial superior frontal gyrus, inferior occipital gyrus and superior temporal gyrus), but the UD group is more associated with limbic alterations. These decreased FCs were negatively correlated with HAMD scores in both BD and UD patients.

Conclusions

BD and UD patients demonstrate different patterns of abnormal cerebral-limbic FC, reflected by decreased FC within cerebral cortex and limbic regions in BD and UD, respectively. The distinct FC abnormal pattern of the cerebral-limbic circuit might be applied as biomarkers to differentiate these two depressive patient groups.

Treating Depression With Tai Chi: State of the Art and Future Perspectives

Major depressive disorder (MDD) is one of the most prevalent mental illnesses in America. Current treatments for MDD are unsatisfactory given high non-response rates, high relapse rates, and undesirable side effects. Accumulating evidence suggests that Tai Chi, a popular mind-body intervention that originated as a martial art, can significantly regulate emotion and relieve the symptoms of mood disorders. In addition, the availability of instructional videos and the development of more simplified and less structured Tai Chi has made it a promising low-intensity mind-body exercise. In this article, we first examine a number of clinical trials that implemented Tai Chi as a treatment for depression. Then, we explore several mechanisms by which Tai Chi may alleviate depressive symptoms, hypothesizing that the intervention may modulate the activity and connectivity of key brain regions involved in mood regulation, reduce neuro-inflammatory sensitization, modulate the autonomic nervous system, and regulate hippocampal neurogenesis. Finally, we discuss common challenges of the intervention and possible ways to address them. Specifically, we pose developing a simplified and tailored Tai Chi protocol for patients with depression, comparatively investigating Tai Chi with other mind-body interventions such as yoga and Baduanjin, and developing new mind-body interventions that merge the advantages of multiple mind-body exercises.

General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

[Non-motor disorders in patients with muscular dystonia]

Non-motor disturbances represented by sensory, affective, obsessive-compulsive disorders, cognitive dysfunction, sleep disturbances are often found in patients with dystonia and have a negative impact on their quality of life. The prevalence of sensory and affective disorders and sleep disturbances is above 50% in patients with cervical dystonia and is 25% in patients with blepharospasm, writing spasm; cognitive dysfunction is found in more than 25% of patients with focal dystonia. The relationship of non-motor, in particular psychiatric disorders, with the impairment of social and everyday life and worsening of quality of life in whole was shown. Common pathophysiological mechanisms of non-motor disorders as well as approaches to treatment of these disorders are discussed. The authors present the results on the positive effect of botulinum toxin therapy that reduces cognitive dysfunction, sensory disorders and depressive syndrome. Non-medication treatment of non-motor disorders in patients with dystonia is considered.

The Association Between the Sedative Loads and Clinical Severity Indicators in the First-Onset Major Depressive Disorder

Background: High sedative use in a major depressive episode may imply specific clinical features. This study aims to examine the correlation between sedative use and clinical severity indicators in the initial treatment phase of first-onset major depressive disorder. Methods: A study cohort in the first episode of major depressive disorder was used to conduct pharmacological dissection. All participants had at least a 2-year follow-up period with a complete treatment record. The defined daily dose of antidepressants and augmentation agents were calculated as the antidepressant load and augmentation load, respectively. Sedative use, which was calculated as the equivalent dosage of lorazepam, were defined as the sedative load. These psychotropic loads were measured monthly and the averaged psychotropic loads for each day were obtained. Results: A total of 106 individuals (75.5% female) were included. The mean duration of disease course in participants was 5.5 ± 3.5 years. In the multiple regression analysis, after controlling for other classes of psychotropics and comorbid anxiety disorders, the sedative load independently correlated with higher number of antidepressants used, higher number of antidepressant used with an adequate dose and duration, more psychiatric emergency and outpatient visits within 2 years of disease onset. Conclusion: High loading of sedatives correlated with several indicators of clinical severity in major depressive disorder. The sedative load may be used as a specifier to identify subgroups in patients with major depressive disorder.