Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group

A comprehensive analysis of the differential expression in the hippocampus of depression induced by gut microbiota compared to traditional stress

Depression, which is a disease of heterogeneous etiology, is characterized by high disability and mortality rates. Gut microbiota are associated with the development of depression. To further explore any differences in the mechanisms of depression induced by gut microbiota and traditional stresses, as well as facilitate the development of microbiota-based interventions, a fecal microbiota transplantation (FMT) depression model was made. This was achieved by transplanting feces from major depressive disorder (MDD) patients into germ-free mice. Second, the mechanisms of the depression induced by gut microbiota were analyzed in comparison with those of the depression caused by different forms of stress. It turned out that mice exhibited depressive-like behavior after FMT. Then, PCR array analysis was performed on the hippocampus of the depressed mice to identify differentially expressed genes (DEGs). The KEGG analysis revealed that the pathways of depression induced by gut microbes are closely associated with immuno-inflammation. To determine the pathogenic pathways of physiological stress and psychological stress-induced depression, raw data was extracted from several databases and KEGG analysis was performed. The results from the analysis revealed that the mechanisms of depression induced by physiological and psychological stress are closely related to the regulation of neurotransmitters and energy metabolism. Interestingly, the immunoinflammatory response was distinct across different etiologies that induced depression. The findings showed that gut microbiota dysbiosis-induced depression was mainly associated with adaptive immunity, while physiological stress-induced depression was more linked to innate immunity. This study compared the pathogenesis of depression caused by gut microbiota dysbiosis, and physiological and psychological stress. We explored new intervention methods for depression and laid the foundation for precise treatment.

Unraveling the causal relationships between depression and brain structural imaging phenotypes: A bidirectional Mendelian Randomization study

BACKGROUND

Previous studies have revealed structural brain abnormalities in individuals with depression, but the causal relationship between depression and brain structure remains unclear.

METHODS

A genetic correlation analysis was conducted using summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 1,265 brain structural imaging-derived phenotypes (IDPs, N = 33,224). Subsequently, a bidirectional two-sample Mendelian Randomization (MR) approach was employed to explore the causal relationships between depression and the IDPs that showed genetic correlations with depression. The main MR results were obtained using the inverse variance weighted (IVW) method, and other MR methods were further employed to ensure the reliability of the findings.

RESULTS

Ninety structural IDPs were identified as being genetically correlated with depression and were included in the MR analyses. The IVW MR results indicated that reductions in the volume of several brain regions, including the bilateral subcallosal cortex, right medial orbitofrontal cortex, and right middle-posterior part of the cingulate cortex, were causally linked to an increased risk of depression. Additionally, decreases in surface area of the right middle temporal visual area, right middle temporal cortex, right inferior temporal cortex, and right middle-posterior part of the cingulate cortex were causally associated with a heightened risk of depression. Validation and sensitivity analyses supported the robustness of these findings. However, no evidence was found for a causal effect of depression on structural IDPs.

CONCLUSIONS

Our findings reveal the causal influence of specific brain structures on depression, providing evidence to consider brain structural changes in the etiology and treatment of depression.

Discriminative analysis of schizophrenia and major depressive disorder using fNIRS

BACKGROUND

Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions.

METHODS

A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists.

RESULTS

Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively.

CONCLUSION

This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.

Neurodevelopmental signature of a transcriptome-based polygenic risk score for depression

Disentangling the molecular underpinnings of major depressive disorder (MDD) is necessary for identifying new treatment and prevention targets. The functional impact of depression-related transcriptomic changes on the brain remains relatively unexplored. We recently developed a novel transcriptome-based polygenic risk score (tPRS) composed of genes transcriptionally altered in MDD. Here, we sought to investigate effects of tPRS on brain structure in a developmental cohort (Adolescent Brain Cognitive Development study; n = 5124; 2387 female) at baseline (9-10 years) and 2-year follow-up (11-12 years). We tested associations between tPRS and Freesurfer-derived measures of cortical thickness, cortical surface area, and subcortical volume. Across the whole sample, higher tPRS was significantly associated with thicker left posterior cingulate cortex at both baseline and 2-year follow-up. In females only, tPRS was associated with lower right hippocampal volume at baseline and 2-year follow-up, and lower right pallidal volume at baseline. Furthermore, regional subcortical volume significantly mediated an indirect effect of tPRS on depressive symptoms in females at both timepoints. Conversely, tPRS did not have significant effects on cortical surface area. These findings suggest the existence of a sex-specific neurodevelopmental signature associated with shifts towards a more depression-like brain transcriptome, and highlight novel pathways of developmentally mediated MDD risk.

The neoliberal leaning of the neuroscience discourse when it deals with mental health and learning disorders

Neuroscience attracted increasing attention in mass media during the last decades. Indeed, neuroscience advances raise high expectations in society concerning major societal issues such as mental health and learning difficulties. Unfortunately, according to leading experts, neuroscience advances have not yet benefited patients, students and socially deprived families. Yet, neuroscience findings are widely overstated and misrepresented in the media. Academic studies, briefly described here, showed that most data misrepresentations were already present in the neuroscience literature before spreading in mass media. This triumphalist neuroscience discourse reinforces a neuro-essentialist conception of mental disorders and of learning difficulties. By emphasizing brain plasticity, this discourse fuels the neoliberal ethics that overvalue autonomy, rationality, flexibility and individual responsibility. According to this unrealistic rhetoric, neuroscience-based techniques will soon bring inexpensive private solutions to enduring social problems. When considering the social consequences of this rhetoric, neuroscientists should refrain from overstating the interpretation of their observations in their scientific publications and in their exchanges with journalists.

Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity

BACKGROUND

Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample.

METHODS

We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging.

RESULTS

The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = -0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05).

LIMITATIONS

This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression).

CONCLUSIONS

This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

The association between abdominal obesity and depressive symptoms among Chinese adults: Evidence from national and regional communities

BACKGROUND

The association between body shape and depressive symptoms has been reported in adults. The present study aimed to investigate the association between body shape-specific abdominal obesity and depressive symptoms among multi-regional Asian adults.

METHODS

The 2011-2012 China Health and Retirement Longitudinal Study and 2022-2023 Hangzhou study were used as the discovery and validation datasets, respectively. Body shape was assessed by body mass index categories. Abdominal obesity was defined as a body shape index (ABSI) ≥ 75th centile. Depression was measured using 10-item Centre for Epidemiological Studies Depression Scale and Geriatric Depression Scale short 15-item version, respectively. General linear and multinomial logistic models were used to explore the association of ABSI, abdominal obesity with depressive scores and presence, respectively.

RESULTS

A total of 12,229 and 1210 participants were included in the discovery and validation datasets, respectively. A non-linear reverse L-shaped association was found between ABSI and depressive scores. Participants with abdominal obesity had higher depressive scores (β = 0.05, 95%CI = 0.01-0.09; and β = 0.13, 95%CI = 0.01-0.24; respectively). Stratified analyses showed that abdominal obesity was associated with higher depressive scores (β = 0.09, 95%CI = 0.00-0.17; and β = 0.25, 95%CI = 0.05-0.46; respectively) and presence (OR = 1.46, 95%CI = 1.02-2.10; and OR = 3.95, 95%CI = 1.58-9.84; respectively) in overweight adults. Furthermore, abdominal obesity was associated with depressive symptoms among overweight females, but not among males.

LIMITATION

Causal links weren't addressed because of the observational study design.

CONCLUSION

Abdominal obesity exhibited a positive association with depressive symptoms among Asian overweight adults, particularly in females. Prevention and early diagnosis of depressive symptoms should focus on overweight females.

Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies

The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.

Rumination and Overrecruitment of Cognitive Control Circuits in Depression

BACKGROUND

Rumination is associated with greater cognitive dysfunction and treatment resistance in major depressive disorder (MDD), but its underlying neural mechanisms are not well understood. Because rumination is characterized by difficulty in controlling negative thoughts, the current study investigated whether rumination was associated with aberrant cognitive control in the absence of negative emotional information.

METHODS

Individuals with MDD (n = 176) and healthy control individuals (n = 52) completed the stop signal task with varied stop signal difficulty during functional magnetic resonance imaging. In the task, a longer stop signal asynchrony made stopping difficult (hard stop), whereas a shorter stop signal asynchrony allowed more time for stopping (easy stop).

RESULTS

In participants with MDD, higher rumination intensity was associated with greater neural activity in response to difficult inhibitory control in the frontoparietal regions. Greater activation for difficult inhibitory control associated with rumination was also positively related to state fear. The imaging results provide compelling evidence for the neural basis of inhibitory control difficulties in individuals with MDD with high rumination.

CONCLUSIONS

The association between higher rumination intensity and greater neural activity in regions involved in difficult inhibitory control tasks may provide treatment targets for interventions aimed at improving inhibitory control and reducing rumination in this population.

Cortical and Subcortical Brain Alterations in Specific Phobia and Its Animal and Blood-Injection-Injury Subtypes: A Mega-Analysis From the ENIGMA Anxiety Working Group

OBJECTIVE

Specific phobia is a common anxiety disorder, but the literature on associated brain structure alterations exhibits substantial gaps. The ENIGMA Anxiety Working Group examined brain structure differences between individuals with specific phobias and healthy control subjects as well as between the animal and blood-injection-injury (BII) subtypes of specific phobia. Additionally, the authors investigated associations of brain structure with symptom severity and age (youths vs. adults).

METHODS

Data sets from 31 original studies were combined to create a final sample with 1,452 participants with phobia and 2,991 healthy participants (62.7% female; ages 5-90). Imaging processing and quality control were performed using established ENIGMA protocols. Subcortical volumes as well as cortical surface area and thickness were examined in a preregistered analysis.

RESULTS

Compared with the healthy control group, the phobia group showed mostly smaller subcortical volumes, mixed surface differences, and larger cortical thickness across a substantial number of regions. The phobia subgroups also showed differences, including, as hypothesized, larger medial orbitofrontal cortex thickness in BII phobia (N=182) compared with animal phobia (N=739). All findings were driven by adult participants; no significant results were observed in children and adolescents.

CONCLUSIONS

Brain alterations associated with specific phobia exceeded those of other anxiety disorders in comparable analyses in extent and effect size and were not limited to reductions in brain structure. Moreover, phenomenological differences between phobia subgroups were reflected in diverging neural underpinnings, including brain areas related to fear processing and higher cognitive processes. The findings implicate brain structure alterations in specific phobia, although subcortical alterations in particular may also relate to broader internalizing psychopathology.

Regional gray matter changes in steatotic liver disease provide a neurobiological link to depression: A cross-sectional UK Biobank cohort study

BACKGROUND

Steatotic liver disease (SLD) is characterized by excessive accumulation of lipids in the liver. It is associated with elevated risk of hepatic and cardiometabolic diseases, as well as mental disorders such as depression. Previous studies revealed global gray matter reduction in SLD. To investigate a possible shared neurobiology with depression, we examined liver fat-related regional gray matter alterations in SLD and its most significant clinical subgroup metabolic dysfunction-associated steatotic liver disease (MASLD).

METHODS

We analyzed regional cortical thickness and area obtained from brain MRI in 29,051 participants in UK Biobank. Liver fat amount was computed as proton density fat fraction (PDFF) from liver MRI scans. We examined the relationship between brain structure and PDFF, adjusting for sociodemographic, physical, lifestyle, and environmental factors, as well as alcohol intake and a spectrum of cardiometabolic covariates. Finally, we compared patterns of brain alterations in SLD/MASLD and major depressive disorder (MDD) using previously published results.

RESULTS

PDFF-related gray matter alterations were region-specific, involving both increases and decreases in cortical thickness, and increased cortical area. In several regions, PDFF effects on gray matter could also be attributed to cardiometabolic covariates. However, PDFF was consistently associated with lower cortical thickness in middle and superior temporal regions and higher cortical thickness in pericalcarine and right frontal pole regions. PDFF-related alterations for the SLD and the MASLD group correlated with those observed in MDD (Pearson r = 0.45-0.54, p < 0.01).

CONCLUSION

These findings suggest the presence of shared biological mechanisms linking MDD to SLD and MASLD. They might explain the well-known elevated risk of depression in these groups and support early lifestyle interventions and treatment of metabolic risk factors for the successful management of the interconnected diseases depression and SLD/MASLD.

Multimodal Neuroimaging Summary Scores as Neurobiological Markers of Psychosis

BACKGROUND AND HYPOTHESIS

Structural brain alterations are well-established features of schizophrenia but they do not effectively predict disease/disease risk. Similar to polygenic risk scores in genetics, we integrated multifactorial aspects of brain structure into a summary "Neuroscore" and examined its potential as a marker of disease.

STUDY DESIGN

We extracted measures from T1-weighted scans and diffusion tensor imaging (DTI) models from three studies with schizophrenia and healthy individuals. We calculated individual-level summary scores (Neuroscores) for T1-weighted and DTI measures and a combined score (Multimodal Neuroscore-MM). We assessed each score's ability to differentiate schizophrenia cases from controls and its relationship to clinical symptomatology, intelligence quotient (IQ), and medication dosage. We assessed Neuroscore specificity by performing all analyses in a more inclusive psychosis sample and by using scores generated from MDD effect sizes.

STUDY RESULTS

All Neuroscores significantly differentiated schizophrenia cases from controls (T1 d = 0.56, DTI d = 0.29, MM d = 0.64) to a greater degree than individual brain regions. Higher Neuroscores (ie, increased liability) were associated with lower IQ (T1 β = -0.26, DTI β = -0.15, MM β = -0.30). Higher T1-weighted Neuroscores were associated with higher positive and negative symptom severity (Positive β = 0.21, Negative β = 0.16); Higher Multimodal Neuroscores were associated with higher positive symptom severity (β = 0.30). SZ Neuroscores outperformed MDD Neuroscores in predicting IQ (T1: z = 3.5, q = 0.0007; MM: z = 1.8, q = 0.05).

CONCLUSIONS

Neuroscores are a step toward leveraging widespread structural brain alterations in psychosis to identify robust neurobiological markers of disease. Future studies will assess ways to improve neuroscore calculation, including developing the optimal methods to calculate neuroscores and considering disorder overlap.

Molecular mechanism of Chang Shen Hua volatile oil modulating brain cAMP-PKA-CREB pathway to improve depression-like behavior in rats

BACKGROUND

Depression is a common and complex mental illness that manifests as persistent episodes of sadness, loss of interest, and decreased energy, which might lead to self-harm and suicide in severe cases. Reportedly, depression affects 3.8 % of the world's population and has been listed as one of the major global public health concerns. In recent years, aromatherapy has been widely used as an alternative and complementary therapy in the prevention and treatment of depression; people can relieve anxiety and depression by sniffing plant aromatic essential oils. Acorus tatarinowii and Panax ginseng essential oils in Chang Shen Hua volatile oil (CSHVO) are derived from Acorus tatarinowii and Panax ginseng, respectively, the main medicines in the famous Chinese medicine prescription Kai Xin San (KXS), Then, these oils are combined with the essential oil of Albizia julibrissin flower to form a new Chinese medicine inhalation preparation, CSHVO. KXS has been widely used in the treatment of depression; however, whether CSHVO can ameliorate depression-like behavior, its pharmacological effects, and the underlying mechanisms of action are yet to be elucidated.

STUDY DESIGN AND METHODS

A rat model of chronic and unpredictable mild stimulation (CUMS) combined with orphan rearing was treated with CSHVO for 4 weeks. Using behavioral tests (sucrose preference, force swimming, tail suspension, and open field), the depression-like degree was evaluated. Concurrently, brain homogenate and serum biochemistry were analyzed to assess the changes in the neurotransmitters and inflammatory and neurotrophic factors. Furthermore, tissue samples were collected for histological and protein analyses. In addition, network pharmacology and molecular docking analyses of the major active compounds, potential therapeutic targets, and intervention pathways predicted a role of CSHVO in depression relief. Subsequently, these predictions were confirmed by in vitro experiments using a corticosterone (CORT)-induced PC12 cell damage model.

RESULTS

CSHVO inhalation can effectively improve the weight and depression-like behavior of depressed rats and regulate the expression of inflammatory factors and neurotransmitters. Hematoxylin-eosin, Nissl, and immunofluorescence staining indicated that compared to the model group, the pathological damage to the brain tissues of rats in the CSHVO groups was improved. The network pharmacological analysis revealed that 144 CSHVO active compounds mediate 71 targets relevant to depression treatment, most of which are rich in the cAMP signaling and inflammatory cytokine pathways. Protein-protein interaction analysis showed that TNF, IL6, and AKT are the core anti-depressive targets of CSHVO. Molecular docking analysis showed an adequate binding between the active ingredients and the key targets. In vitro experiments showed that compared to the model group, the survival rate of PC12 cells induced by CSHVO intervention was increased, the apoptosis rate was decreased, and the expression of inflammatory cytokines in the cell supernatant was improved. Western blot analysis and immunofluorescence staining confirmed that CSHVO regulates PC12 cells in the CORT model through the cAMP-PKA-CREB signaling pathway, and pretreatment with PKA blocker H89 eliminates the protective effect of CSHVO on CORT-induced PC12 cells.

CONCLUSIONS

CSHVO improves CORT-induced injury in the PC12 cell model and CUMS combined with orphan rearing-induced depression model in rats. The antidepressant mechanism of CSHVO is associated with the modulation of the cAMP-PKA-CREB signaling pathway.

BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data

Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratifications of predictor variables, leading to algorithms that are both more accurate and generalizable. However, the wider availability of machine learning tools capable of incorporating such biological knowledge remains limited. Addressing this gap, we introduce BioM2, a novel R package designed for biologically informed multistage machine learning. BioM2 uniquely leverages biological information to effectively stratify and aggregate high-dimensional biological data in the context of machine learning. Demonstrating its utility with genome-wide DNA methylation and transcriptome-wide gene expression data, BioM2 has shown to enhance predictive performance, surpassing traditional machine learning models that operate without the integration of biological knowledge. A key feature of BioM2 is its ability to rank predictor variables within biological categories, specifically Gene Ontology pathways. This functionality not only aids in the interpretability of the results but also enables a subsequent modular network analysis of these variables, shedding light on the intricate systems-level biology underpinning the predictive outcome. We have proposed a biologically informed multistage machine learning framework termed BioM2 for phenotype prediction based on omics data. BioM2 has been incorporated into the BioM2 CRAN package (https://cran.r-project.org/web/packages/BioM2/index.html).

Challenges and rewards of in vivo synaptic density imaging, and its application to the study of depression

The development of novel radiotracers for Positron Emission Tomography (PET) imaging agents targeting the synaptic vesicle glycoprotein 2 A (SV2A), an integral glycoprotein present in the membrane of all synaptic vesicles throughout the central nervous system, provides a method for the in vivo quantification of synaptic density. This is of particular interest in neuropsychiatric disorders given that synaptic alterations appear to underlie disease progression and symptom severity. In this review, we briefly describe the development of these SV2A tracers and the evaluation of quantification methods. Next, we discuss application of SV2A PET imaging to the study of depression, including a review of our findings demonstrating lower SV2A synaptic density in people with significant depressive symptoms and the use of a ketamine drug challenge to examine synaptogenesis in vivo. We then highlight the importance of performing translational PET imaging in animal models in conjunction with clinical imaging. We consider the ongoing challenges, possible solutions, and present preliminary findings from our lab demonstrating the translational benefit and potential of in vivo SV2A imaging in animal models of chronic stress. Finally, we discuss methodological improvements and future directions for SV2A imaging, potentially in conjunction with other neural markers.

Macrostructural Brain Morphology as Moderator of the Relationship Between Pandemic-Related Stress and Internalizing Symptomology During COVID-19 in High-Risk Adolescents

BACKGROUND

According to person-by-environment models, individual differences in traits may moderate the association between stressors and the development of psychopathology; however, findings in the literature have been inconsistent and little literature has examined adolescent brain structure as a moderator of the effects of stress on adolescent internalizing symptoms. The COVID-19 pandemic presented a unique opportunity to examine the associations between stress, brain structure, and psychopathology. Given links of cortical morphology with adolescent depression and anxiety, the present study investigated whether cortical morphology moderates the relationship between stress from the COVID-19 pandemic on the development of internalizing symptoms in familial high-risk adolescents.

METHODS

Prior to the COVID-19 pandemic, 72 adolescents (27M) completed a measure of depressive and anxiety symptoms and underwent magnetic resonance imaging. T1-weighted images were acquired to assess cortical thickness and surface area. Approximately 6-8 months after COVID-19 was declared a global pandemic, adolescents reported their depressive and anxiety symptoms and pandemic-related stress.

RESULTS

Adjusting for pre-pandemic depressive and anxiety symptoms and stress, increased pandemic-related stress was associated with increased depressive but not anxiety symptoms. This relationship was moderated by cortical thickness and surface area in the anterior cingulate and cortical thickness in the medial orbitofrontal cortex such that increased stress was only associated with increased depressive and anxiety symptoms among adolescents with lower cortical surface area and higher cortical thickness in these regions.

CONCLUSIONS

Results further our understanding of neural vulnerabilities to the associations between stress and internalizing symptoms in general, and during the COVID-19 pandemic in particular.

Investigating grey matter volumetric trajectories through the lifespan at the individual level

Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to limited large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages, and the neurobiological basis underlying individual heterogeneity remains poorly understood. Here we identify, using the IMAGEN adolescent cohort followed up over 9 years (14-23 y), three groups of adolescents characterized by distinct developmental patterns of whole-brain gray matter volume (GMV). Group 1 show continuously decreasing GMV associated with higher neurocognitive performances than the other two groups during adolescence. Group 2 exhibit a slower rate of GMV decrease and lower neurocognitive performances compared with Group 1, which was associated with epigenetic differences and greater environmental burden. Group 3 show increasing GMV and lower baseline neurocognitive performances due to a genetic variation. Using the UK Biobank, we show these differences may be attenuated in mid-to-late adulthood. Our study reveals clusters of adolescent neurodevelopment based on GMV and the potential long-term impact.

Accelerated biological brain aging in major depressive disorder

Major depressive disorder (MDD) patients commonly encounter multiple types of functional disabilities, such as social, physical, and role functioning. MDD is related to an accreted risk of brain atrophy, aging-associated brain diseases, and mortality. Based on recently available studies, there are correlations between notable biological brain aging and MDD in adulthood. Despite several clinical and epidemiological studies that associate MDD with aging phenotypes, the underlying mechanisms in the brain remain unknown. The key areas in the study of biological brain aging in MDD are structural brain aging, impairment in functional connectivity, and the impact on cognitive function and age-related disorders. Various measurements have been used to determine the severity of brain aging, such as the brain age gap estimate (BrainAGE) or brain-predicted age difference (BrainPAD). This review summarized the current results of brain imaging data on the similarities between the manifestation of brain structural changes and the age-associated processes in MDD. This review also provided recent evidence of BrainPAD or BrainAGE scores in MDD, brain structural abnormalities, and functional connectivity, which are commonly observed between MDD and age-associated processes. It serves as a basis of current reference for future research on the potential areas of investigation for diagnostic, preventive, and potentially therapeutic purposes for brain aging in MDD.

Prenatal depression outcomes in the next generation: A critical review of recent DOHaD studies and recommendations for future research

Prenatal depression, a common pregnancy-related risk with a prevalence of 10-20 %, may affect in utero development and socioemotional and neurodevelopmental outcomes in the next generation. Although there is a growing body of work that suggests prenatal depression has an independent and long-lasting effect on offspring outcomes, important questions remain, and findings often do not converge. The present review examines work carried out in the last decade, with an emphasis on studies focusing on mechanisms and leveraging innovative technologies and study designs to fill in gaps in research. Overall, the past decade of research continues to suggest that prenatal depression increases risk for offspring socioemotional problems and may alter early brain development by affecting maternal-fetal physiology during pregnancy. However, important limitations remain; lack of diversity in study samples, inconsistent consideration of potential confounders (e.g., genetics, postnatal depression, parenting), and restriction of examination to narrow time windows and single exposures. On the other hand, exciting work has begun uncovering potential mechanisms underlying transmission, including alterations in mitochondria functioning, epigenetics, and the prenatal microbiome. We review the evidence to date, identify limitations, and suggest strategies for the next decade of research to detect mechanisms as well as sources of plasticity and resilience to ensure this work translates into meaningful, actionable science that improves the lives of families.

Multiple cognition associated multimodal brain networks in major depressive disorder

Major depressive disorder frequently leads to cognitive impairments, significantly affecting patients' quality of life. However, the neurobiological mechanisms underlying cognitive deficits remain unclear. This study aimed to explore multimodal imaging biomarkers associated with cognitive function in major depressive disorder. Five cognitive scores (sustained attention, visual recognition memory, pattern recognition memory, executive function, and working memory) were used as references to guide the fusion of gray matter volume and amplitude of the low frequency fluctuation. Social function was assessed after 2 yr. Linear regression analysis was performed to identify brain features that were associated with social function of patients with major depressive disorder. Finally, we included 131 major depressive disorder and 145 healthy controls. A multimodal frontal-insula-occipital network associated with sustained attention was found to be associated with social functioning in major depressive disorders. Analysis across different cognitive domains revealed that gray matter volume exhibited greater sensitivity to differences, while amplitude of the low frequency fluctuation consistently decreased in the right temporal-occipital-hippocampus circuit. The consistent functional changes across the 5 cognitive domains were related to symptom severity. Overall, these findings provide insights into biomarkers associated with multiple cognitive domains in major depressive disorder. These results may contribute to the development of effective treatment targeting cognitive deficits and social function.

Multiparameter cortical surface morphology in former amateur contact sport athletes

The lifetime effects of repetitive head impacts have captured considerable public and scientific interest over the past decade, yet a knowledge gap persists in our understanding of midlife neurological well-being, particularly in amateur level athletes. This study aimed to identify the effects of lifetime exposure to sports-related head impacts on brain morphology in retired, amateur athletes. This cross-sectional study comprised of 37 former amateur contact sports athletes and 21 age- and sex-matched noncontact athletes. High-resolution anatomical, T1 scans were analyzed for the cortical morphology, including cortical thickness, sulcal depth, and sulcal curvature, and cognitive function was assessed using the Dementia Rating Scale-2. Despite no group differences in cognitive functions, the contact group exhibited significant cortical thinning particularly in the bilateral frontotemporal regions and medial brain regions, such as the cingulate cortex and precuneus, compared to the noncontact group. Deepened sulcal depth and increased sulcal curvature across all four lobes of the brain were also notable in the contact group. These data suggest that brain morphology of middle-aged former amateur contact athletes differs from that of noncontact athletes and that lifetime exposure to repetitive head impacts may be associated with neuroanatomical changes.

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling

MAIN PROBLEM

Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia.

METHODS

A predictive model used connectome-based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre-treatment functional connectivity (FC) data from 59 patients with MDD. Node-based FC analysis was employed to compare differences in FC patterns between melancholic and non-melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients.

RESULTS

CPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non-melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_bonferroni = 0.0303). This distinct FC pattern effectively discriminated between melancholic and non-melancholic MDD patients, achieving a sensitivity of 93.54%, specificity of 67.86%, and an overall accuracy of 81.36% using the SVM method.

CONCLUSIONS

This study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non-melancholic MDD patients. These findings provide guidance for clinical treatment.

Maternal prenatal depressive symptoms and child brain responses to affective touch at two years of age

BACKGROUND

Touch is an essential form of mother-child interaction, instigating better social bonding and emotional stability.

METHODS

We used diffuse optical tomography to explore the relationship between total haemoglobin (HbT) responses to affective touch in the child's brain at two years of age and maternal self-reported prenatal depressive symptoms (EPDS). Affective touch was implemented via slow brushing of the child's right forearm at 3 cm/s and non-affective touch via fast brushing at 30 cm/s and HbT responses were recorded on the left hemisphere.

RESULTS

We discovered a cluster in the postcentral gyrus exhibiting a negative correlation (Pearson's r = -0.84, p = 0.015 corrected for multiple comparisons) between child HbT response to affective touch and EPDS at gestational week 34. Based on region of interest (ROI) analysis, we found negative correlations between child responses to affective touch and maternal prenatal EPDS at gestational week 14 in the precentral gyrus, Rolandic operculum and secondary somatosensory cortex. The responses to non-affective touch did not correlate with EPDS in these regions.

LIMITATIONS

The number of mother-child dyads was 16. However, by utilising high-density optode arrangements, individualised anatomical models, and video and accelerometry to monitor movement, we were able to minimize methodological sources of variability in the data.

CONCLUSIONS

The results show that maternal depressive symptoms during pregnancy may be associated with reduced child responses to affective touch in the temporoparietal cortex. Responses to affective touch may be considered as potential biomarkers for psychosocial development in children. Early identification of and intervention in maternal depression may be important already during early pregnancy.

Neurophysiological and neuroimaging markers of repetitive transcranial magnetic stimulation treatment response in major depressive disorder: A systematic review and meta-analysis of predictive modeling studies

Predicting repetitive transcranial magnetic stimulation (rTMS) treatment outcomes in major depressive disorder (MDD) could reduce the financial and psychological risks of treatment failure. We systematically reviewed and meta-analyzed studies that leveraged neurophysiological and neuroimaging markers to predict rTMS response in MDD. Five databases were searched from inception to May 25, 2023. The primary meta-analytic outcome was predictive accuracy pooled from classification models. Regression models were summarized qualitatively. A promising marker was identified if it showed a sensitivity and specificity of 80% or higher in at least two independent studies. Searching yielded 36 studies. Twenty-two classification modeling studies produced an estimated area under the summary receiver operating characteristic curve of 0.87 (95% CI = 0.83-0.92), with 86.8% sensitivity (95% CI = 80.6-91.2%) and 81.9% specificity (95% CI = 76.1-86.4%). Frontal theta cordance measured by electroencephalography is closest to proof of concept. Predicting rTMS response using neurophysiological and neuroimaging markers is promising for clinical decision-making. However, replications by different research groups are needed to establish rigorous markers.

Potential correlations between abnormal homogeneity of default mode network and personality or lipid level in major depressive disorder

BACKGROUND

Default mode network (DMN) is one of the most recognized resting-state networks in major depressive disorder (MDD). However, the homogeneity of this network in MDD remains incompletely explored. Therefore, this study aims to determine whether there is abnormal network homogeneity (NH) of the DMN in MDD patients. At the same time, correlations between clinical variables and brain functional connectivity are examined.

METHODS

We enrolled 42 patients diagnosed with MDD and 42 HCs. A variety of clinical variables were collected, and data analysis was conducted using the NH and independent component analysis methods.

RESULTS

The study shows that MDD patients have higher NH values in the left superior medial prefrontal cortex (MPFC) and left posterior cingulate cortex (PCC) compared to HCs. Additionally, there is a positive correlation between NH values of the left superior MPFC and Eysenck Personality Questionnaire values. NH values of the left PCC are positively linked to CHOL levels, LDL levels, and utilization scores. However, these correlations lose significance after the Bonferroni correction.

CONCLUSION

Our findings indicate the presence of abnormal DMN homogeneity in MDD, underscoring the significance of DMN in the pathophysiology of MDD. Simultaneously, the study provides preliminary evidence for the correlation between clinical variables and brain functional connectivity.

Programmed cell death factor 4-mediated hippocampal synaptic plasticity is involved in early life stress and susceptibility to depression

Early life stress (ELS) increases the risk of depression later in life. Programmed cell death factor 4 (PDCD4), an apoptosis-related molecule, extensively participates in tumorigenesis and inflammatory diseases. However, its involvement in a person's susceptibility to ELS-related depression is unknown. To examine the effects and underlying mechanisms of PDCD4 on ELS vulnerability, we used a "two-hit" stress mouse model: an intraperitoneal injection of lipopolysaccharide (LPS) into neonatal mice was performed on postnatal days 7-9 (P7-P9) and inescapable foot shock (IFS) administration in adolescent was used as a later-life challenge. Our study shows that compared with mice that were only exposed to the LPS or IFS, the "two-hit" stress mice developed more severe depression/anxiety-like behaviors and social disability. We detected the levels of PDCD4 in the hippocampus of adolescent mice and found that they were significantly increased in "two-hit" stress mice. The results of immunohistochemical staining and Sholl analysis showed that the number of microglia in the hippocampus of "two-hit" stress mice significantly increased, with morphological changes, shortened branches, and decreased numbers. However, knocking down PDCD4 can prevent the number and morphological changes of microglia induced by ELS. In addition, we confirmed through the Golgi staining and immunohistochemical staining results that knocking down PDCD4 can ameliorate ELS-induced synaptic plasticity damage. Mechanically, the knockdown of PDCD4 exerts neuroprotective effects, possibly via the mediation of BDNF/AKT/CREB signaling. Combined, these results suggest that PDCD4 may play an important role in the ELS-induced susceptibility to depression and, thus, may become a therapeutic target for depressive disorders.

Quantitative MRI at 7-Tesla reveals novel frontocortical myeloarchitecture anomalies in major depressive disorder

Whereas meta-analytical data highlight abnormal frontocortical macrostructure (thickness/surface area/volume) in Major Depressive Disorder (MDD), the underlying microstructural processes remain uncharted, due to the use of conventional MRI scanners and acquisition techniques. We uniquely combined Ultra-High Field MRI at 7.0 Tesla with Quantitative Imaging to map intracortical myelin (proxied by longitudinal relaxation time T1) and iron concentration (proxied by transverse relaxation time T2*), microstructural processes deemed particularly germane to cortical macrostructure. Informed by meta-analytical evidence, we focused specifically on orbitofrontal and rostral anterior cingulate cortices among adult MDD patients (N = 48) and matched healthy controls (HC; N = 10). Analyses probed the association of MDD diagnosis and clinical profile (severity, medication use, comorbid anxiety disorders, childhood trauma) with aforementioned microstructural properties. MDD diagnosis (p's < 0.05, Cohen's D = 0.55-0.66) and symptom severity (p's < 0.01, r = 0.271-0.267) both related to decreased intracortical myelination (higher T1 values) within the lateral orbitofrontal cortex, a region tightly coupled to processing negative affect and feelings of sadness in MDD. No relations were found with local iron concentrations. These findings allow uniquely fine-grained insights on frontocortical microstructure in MDD, and cautiously point to intracortical demyelination as a possible driver of macroscale cortical disintegrity in MDD.

Genetic influences on brain and cognitive health and their interactions with cardiovascular conditions and depression

Approximately 40% of dementia cases could be prevented or delayed by modifiable risk factors related to lifestyle and environment. These risk factors, such as depression and vascular disease, do not affect all individuals in the same way, likely due to inter-individual differences in genetics. However, the precise nature of how genetic risk profiles interact with modifiable risk factors to affect brain health is poorly understood. Here we combine multiple data resources, including genotyping and postmortem gene expression, to map the genetic landscape of brain structure and identify 367 loci associated with cortical thickness and 13 loci associated with white matter hyperintensities (P < 5×10-8), with several loci also showing a significant association with cognitive function. We show that among 220 unique genetic loci associated with cortical thickness in our genome-wide association studies (GWAS), 95 also showed evidence of interaction with depression or cardiovascular conditions. Polygenic risk scores based on our GWAS of inferior frontal thickness also interacted with hypertension in predicting executive function in the Canadian Longitudinal Study on Aging. These findings advance our understanding of the genetic underpinning of brain structure and show that genetic risk for brain and cognitive health is in part moderated by treatable mid-life factors.

Cortical morphology variations during the menstrual cycle in individuals with and without premenstrual dysphoric disorder

BACKGROUND

Premenstrual dysphoric disorder (PMDD) is hypothesized to stem from maladaptive neural sensitivity to ovarian steroid hormone fluctuations. Recently, we found thinner cortices in individuals with PMDD, compared to healthy controls, during the symptomatic phase. Here, we aimed at investigating whether such differences illustrate state-like characteristics specific to the symptomatic phase, or trait-like features defining PMDD.

METHODS

Patients and controls were scanned using structural magnetic resonance imaging during the mid-follicular and late-luteal phase of the menstrual cycle. Group-by-phase interaction effects on cortical architecture metrics (cortical thickness, gyrification index, cortical complexity, and sulcal depth) were assessed using surface-based morphometry.

RESULTS

Independently of menstrual cycle phase, a main effect of diagnostic group on surface metrics was found, primarily illustrating thinner cortices (0.3 < Cohen's d > 1.1) and lower gyrification indices (0.4 < Cohen's d > 1.0) in patients compared to controls. Furthermore, menstrual cycle-specific effects were detected across all participants, depicting a decrease in cortical thickness (0.4 < Cohen's d > 1.7) and region-dependent changes in cortical folding metrics (0.4 < Cohen's d > 2.2) from the mid-follicular to the late luteal phase.

LIMITATIONS

Small effects (d = 0.3) require a larger sample size to be accurately characterized.

CONCLUSIONS

These findings provide initial evidence of trait-like cortical characteristics of the brain of individuals with premenstrual dysphoric disorder, together with indications of menstrual cycle-related variations in cortical architecture in patients and controls. Further investigations exploring whether these differences constitute stable vulnerability markers or develop over the years may help understand PMDD etiology.

Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia

Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca's area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual's response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia.

Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations

Motor performance (MP) is essential for functional independence and well-being, particularly in later life. However, the relationship between behavioural aspects such as sleep quality and depressive symptoms, which contribute to MP, and the underlying structural brain substrates of their interplay remains unclear. This study used three population-based cohorts of younger and older adults (n=1,950) from the Human Connectome Project-Young Adult (HCP-YA), HCP-Aging (HCP-A), and enhanced Nathan Kline Institute-Rockland sample (eNKI-RS). Several canonical correlation analyses were computed within a machine learning framework to assess the associations between each of the three domains (sleep quality, depressive symptoms, grey matter volume (GMV)) and MP. The HCP-YA analyses showed progressively stronger associations between MP and each domain: depressive symptoms (unexpectedly positive, r=0.13, SD=0.06), sleep quality (r=0.17, SD=0.05), and GMV (r=0.19, SD=0.06). Combining sleep and depressive symptoms significantly improved the canonical correlations (r=0.25, SD=0.05), while the addition of GMV exhibited no further increase (r=0.23, SD=0.06). In young adults, better sleep quality, mild depressive symptoms, and GMV of several brain regions were associated with better MP. This was conceptually replicated in young adults from the eNKI-RS cohort. In HCP-Aging, better sleep quality, fewer depressive symptoms, and increased GMV were associated with MP. Robust multivariate associations were observed between sleep quality, depressive symptoms and GMV with MP, as well as age-related variations in these factors. Future studies should further explore these associations and consider interventions targeting sleep and mental health to test the potential effects on MP across the lifespan.

Causal effects of childhood obesity on neuroticism and subjective well-being: A two-sample Mendelian randomization study

BACKGROUND

Childhood obesity is linked to both neuroticism and subjective wellbeing (SWB); however, the causal relations between them remain unclear.

METHODS

Two-sample Mendelian randomization (MR) analysis was applied to determine the causal effects of childhood BMI (n = 39,620) on neuroticism (n = 366,301) and SWB (n = 298,420) using summary statistics from large scale genome-wide association studies (GWASs). Inverse-variance weighting (IVW), weighted mode, weighted median, and MR-Egger approaches were used to estimate the causal effects. Sensitivity analyses including the Cochran's Q statistics, MR-Egger intercept test, MR-PRESSO global test, and the leave-one-out (LOO) analysis were used to assess potential heterogeneity and horizontal pleiotropy. Two-step MR mediation analysis was employed to explore the potential mediation effects of neuroticism on the causal relationship between childhood BMI and SWB.

RESULTS

Our study revealed that genetically predicted higher childhood BMI was causally associated with increased neuroticism (beta = 0.045, 95%CI = 0.013,0.077, p = 6.066e-03) and reduced SWB (beta = -0.059, 95%CI = -0.093,-0.024, p = 9.585e-04). Sensitivity analyses didn't detect any significant heterogeneity and horizontal pleiotropy (all p > 0.05). Additionally, the two-step MR mediation analysis indicated that the causal relationship between childhood BMI and SWB was partially mediated by neuroticism (proportion of mediation effects in total effects: 21.3 %, 95%CI: 5.4 % to 37.2, p = 0.0088).

CONCLUSION

Genetically proxy for higher childhood BMI was associated with increased neuroticism and reduced SWB. Further studies were warranted to investigate the underlying molecular mechanisms and potential use of weight management for improving personality and SWB.

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 short-interval longitudinal study of associations between psychological distress and hippocampal grey matter in early adolescence

This study of Australian adolescents (N = 88, 12-13-years-old) investigated the relationship between hippocampal grey matter volume (GMV) and self-reported psychological distress (K10) at four timepoints, across 12 months. Participants were divided into two groups; those who had K10 scores between 10 and 15 for all four timepoints were categorised as "low distress" (i.e., control group; n = 38), while participants who had K10 scores of 16 or higher at least once over the year were categorised as "moderate-high distress" (n = 50). Associations were tested by GEE fitting of GMV and K10 measures at the same time point, and in the preceding and subsequent timepoints. Analyses revealed smaller preceding left GMV and larger preceding right GMV were associated with higher subsequent K10 scores in the "moderate-high distress" group. This was not observed in the control group. In contrast, the control group showed significant co-occurring associations (i.e., at the same TP) between GMV and K10 scores. The "moderate-high distress" group experienced greater variability in distress. These results suggest that GMV development in early adolescence is differently associated with psychological distress for those who experience "moderate-high distress" at some point over the year, compared to controls. These findings offer a novel way to utilise short-interval, multiple time-point longitudinal data to explore changes in volume and experience of psychological distress in early adolescents. The results suggest hippocampal volume in early adolescence may be linked to fluctuations in psychological distress.

Childhood adversity modulates structural brain changes in borderline personality but not in major depression disorder

Adverse childhood experiences (ACEs) negatively affect the function and structure of emotion brain circuits, increasing the risk of various psychiatric disorders. It is unclear if ACEs show disorder specificity with respect to their effects on brain structure. We aimed to investigate whether the structural brain effects of ACEs differ between patients with major depression (MDD) and borderline personality disorder (BPD). These disorders share many symptoms but likely have different etiologies. To achieve our goal, we obtained structural 3T-MRI images from 20 healthy controls (HC), 19 MDD patients, and 18 BPD patients, and measured cortical thickness and subcortical gray matter volumes. We utilized the Adverse Childhood Experiences (ACE) questionnaire to quantify self-reported exposure to childhood trauma. Our findings suggest that individuals with MDD exhibit a smaller cortical thickness when compared to those with BPD. However, ACEs showed a significantly affected relationship with cortical thickness in BPD but not in MDD. ACEs were found to be associated with thinning in cortical regions involved in emotional behavior in BPD, whereas HC showed an opposite association. Our results suggest a potential mechanism of ACE effects on psychopathology involving changes in brain structure. These findings highlight the importance of early detection and intervention strategies.

Abnormal intrinsic brain functional network dynamics in first-episode drug-naïve adolescent major depressive disorder

BACKGROUND

Alterations in brain functional connectivity (FC) have been frequently reported in adolescent major depressive disorder (MDD). However, there are few studies of dynamic FC analysis, which can provide information about fluctuations in neural activity related to cognition and behavior. The goal of the present study was therefore to investigate the dynamic aspects of FC in adolescent MDD patients.

METHODS

Resting-state functional magnetic resonance imaging data were acquired from 94 adolescents with MDD and 78 healthy controls. Independent component analysis, a sliding-window approach, and graph-theory methods were used to investigate the potential differences in dynamic FC properties between the adolescent MDD patients and controls.

RESULTS

Three main FC states were identified, State 1 which was predominant, and State 2 and State 3 which occurred less frequently. Adolescent MDD patients spent significantly more time in the weakly-connected and relatively highly-modularized State 1, spent significantly less time in the strongly-connected and low-modularized State 2, and had significantly higher variability of both global and local efficiency, compared to the controls. Classification of patients with adolescent MDD was most readily performed based on State 1 which exhibited disrupted intra- and inter-network FC involving multiple functional networks.

CONCLUSIONS

Our study suggests local segregation and global integration impairments and segregation-integration imbalance of functional networks in adolescent MDD patients from the perspectives of dynamic FC. These findings may provide new insights into the neurobiology of adolescent MDD.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

The relationship between coffee-related factors and cortical and hippocampal structure: a triangulation of evidence approach and Mendelian randomization research

Objective

Existing studies have reported sustained changes in the cortical structure of rats due to coffee-related factors, which are speculated to occur in the human body. However, there is a lack of research on this topic. Additionally, previous observational studies have found the impact of diseases on cortical structure and the potential therapeutic effects of coffee on these diseases. Our aim was to study the causal effects of coffee-related factors on the human brain using SNPs (single nucleotide polymorphisms). We will connect these discovered causal effects to the impact of diseases on the brain. Through triangulating evidence, we will reveal the potential active areas of coffee in preventing diseases.

Methods

We utilized GWAS data from multiple cohorts and their databases, selecting instrumental variables for genetic prediction of coffee intake and plasma levels of caffeine and its direct metabolites. We applied these instrumental variables to individual data on cortical thickness and surface area, as well as hippocampal volume, from the ENIGMA and CHARGE consortium for Mendelian randomization analysis (MR). Triangular evidence was obtained by integrating existing evidence through a specified retrieval strategy, calculating the overlap between coffee's effects on brain regions and disease-related brain regions to identify potential regions of action.

Results

The MR analysis yielded 93 positive results for 9 exposures, among which theobromine, a metabolite in the caffeine pathway, was found to be associated with increased hippocampal volume. For cortical structure, theobromine in the caffeine pathway was associated with a decrease in total surface area, while theobromine and caffeine in the pathway were associated with an increase in total thickness. The overlap rate of triangular evidence showed no difference in both overall and subgroup analyses, indicating a high overlap between the effects of coffee on brain regions and disease.

Conclusions

From predicted outcomes from causal effects, coffee intake-related factors may have lasting effects on cortical structure. Additionally, theobromine and theophylline have the greatest impact on certain brain gyri, rather than caffeine. Triangulation evidence indicates that disease and coffee intake-related factors act on the same cortical regions, suggesting the presence of potential shared or antagonistic pathways.

Gray matter atrophy is constrained by normal structural brain network architecture in depression

BACKGROUND

There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression.

METHODS

In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis.

RESULTS

In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset.

CONCLUSIONS

These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set

BACKGROUND

Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD.

AIMS

Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes.

METHOD

A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings.

RESULTS

Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms.

CONCLUSIONS

Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.

Changes of structural functional connectivity coupling and its correlations with cognitive function in patients with major depressive disorder

BACKGROUND

Previous neuroimaging studies have reported structural and functional brain abnormalities in major depressive disorder (MDD). This study aimed to explore whether the coherence of structural-functional networks was affected by disease and investigate its correlation with clinical manifestations.

METHODS

The severity of symptoms and cognitive function of 121 MDD patients and 139 healthy controls (HC) were assessed, and imaging data, including diffusion tensor imaging, T1 structural magnetic resonance imaging (MRI) and resting-state functional MRI, were collected. Spearman correlation coefficients of Kullback-Leibler similarity (KLS), fiber number (FN), fractional anisotropy (FA) and functional connectivity (FC) were calculated as coupling coefficients. Double-weight median correlation analysis was conducted to investigate the correlations between differences in brain networks and clinical assessments.

RESULTS

The percentage of total correct response of delayed matching to sample and the percentage of delayed correct response of pattern recognition memory was lower in MDD. Compared with the HC, KLS-FC coupling between the parietal lobe and subcortical area, FA-FC coupling between the temporal and parietal lobe, and FN-FC coupling in the frontal lobe was lower in MDD. Several correlations between structural-functional connectivity and clinical manifestations were identified.

LIMITATIONS

First, our study lacks longitudinal follow-up data. Second, the sample size was relatively small. Moreover, we only used the Anatomical Automatic Labeling template to construct the brain network. Finally, the validation of the causal relationship of neuroimaging-behavior factors was still insufficient.

CONCLUSIONS

The alternation in structural-functional coupling were related to clinical characterization and might be involved in the neuropathology of depression.

Multi-modal morphometric association study of subclinical depressive symptoms using voxel-based morphometry, cortical thickness, and diffusion tensor imaging (DTI)

BACKGROUND

Case-control studies in major depression have established numerous regional grey and white matter effects in fronto-limbic brain regions. Yet, brain structural studies of dimensional depressive psychopathology within the subclinical spectrum are still limited, in particular for multi-modal imaging approaches.

METHODS

Using voxel-based and surface-based morphometry (cortical thickness) in combination with diffusion tensor imaging (DTI) in a large non-clinical sample (N = 300), we correlated grey and white matter structural variation with subclinical depressive symptoms assessed with Beck's Depression inventory (BDI).

RESULTS

We found a significant decrease of axial diffusivity associated with higher BDI scores in the left hippocampal part of the cingulum bundle (p < 0.05, threshold free cluster enhanced [TFCE] p-value) and some grey matter trend results e.g., a non-linear negative correlation of cortical thickness with depressive symptom load in the right pre/postcentral cortex (pFWE = 0.054, family wise error [FWE] peak level corrected) and a trend in grey matter volume decrease in women in the inferior frontal gyrus (pFWE = 0.054).

LIMITATIONS

Since all grey matter effects disappear after FWE correction, we assume more stable effects in a larger, less homogenous sample enriched by help-seeking subjects covering a wider range of subclinical psychopathology.

CONCLUSION

Our study adds correlations between single depressive symptoms and brain structure to a growing literature. Since subclinical depression is increasingly recognised to be relevant in our understanding of manifest depression, early detection and identification of potential brain correlates of minor depressive symptoms has the potential to expand and reveal possible biomarkers and early psychological treatment.

Contributions of Polygenic Risk and Disease Status to Gray Matter Abnormalities in Major Depression

BACKGROUND

Gray matter (GM) abnormalities in depression are potentially attributable to some combination of trait, state, and illness history factors. Here, we sought to determine the contributions of polygenic risk for depression, depressive disease status, and the interaction of these factors to these GM abnormalities.

METHODS

We conducted a cross-sectional comparison using a 2 × 3 factorial design examining effects of polygenic risk for depression (lower vs. upper quartile) and depression status (never depressed, currently depressed, or remitted depression) on regional GM concentration and GM volume. Participants were a subset of magnetic resonance imaging-scanned UK Biobank participants comprising 2682 people (876 men, 1806 women) algorithmically matched on 16 potential confounders.

RESULTS

In women but not men, we observed that elevated polygenic risk for depression was associated with reduced cerebellar GM volume. This deficit occurred in salience and dorsal attention network regions of the cerebellum and was associated with poorer performance on tests of attention and executive function but not fluid intelligence. Moreover, in women with current depression compared to both women with remitted depression and women who never had depression, we observed GM reductions in ventral and medial prefrontal, insular, and medial temporal regions. These state-related abnormalities remained when accounting for antidepressant medication status.

CONCLUSIONS

Neuroanatomical deficits attributed broadly to major depression are more likely due to an aggregation of independent factors. Polygenic risk for depression accounted for cerebellar structural abnormalities that themselves accounted for cognitive deficits observed in this disorder. Medial and ventral prefrontal, insular, and temporal cortex deficits constituted a much larger proportion of the aggregate deficit and were attributable to the depressed state.

Roles of the medial and lateral orbitofrontal cortex in major depression and its treatment

We describe evidence for dissociable roles of the medial and lateral orbitofrontal cortex (OFC) in major depressive disorder (MDD) from structure, functional activation, functional connectivity, metabolism, and neurochemical systems. The reward-related medial orbitofrontal cortex has lower connectivity and less reward sensitivity in MDD associated with anhedonia symptoms; and the non-reward related lateral OFC has higher functional connectivity and more sensitivity to non-reward/aversive stimuli in MDD associated with negative bias symptoms. Importantly, we propose that conventional antidepressants act to normalize the hyperactive lateral (but not medial) OFC to reduce negative bias in MDD; while other treatments are needed to operate on the medial OFC to reduce anhedonia, with emerging evidence suggesting that ketamine may act in this way. The orbitofrontal cortex is the key cortical region in emotion and reward, and the current review presents much new evidence about the different ways that the medial and lateral OFC are involved in MDD.

Reciprocal relationships between stress and depressive symptoms: the essential role of the nucleus accumbens

BACKGROUND

Stress and depression have a reciprocal relationship, but the neural underpinnings of this reciprocity are unclear. We investigated neuroimaging phenotypes that facilitate the reciprocity between stress and depressive symptoms.

METHODS

In total, 22 195 participants (52.0% females) from the population-based UK Biobank study completed two visits (initial visit: 2006-2010, age = 55.0 ± 7.5 [40-70] years; second visit: 2014-2019; age = 62.7 ± 7.5 [44-80] years). Structural equation modeling was used to examine the longitudinal relationship between self-report stressful life events (SLEs) and depressive symptoms. Cross-sectional data were used to examine the overlap between neuroimaging correlates of SLEs and depressive symptoms on the second visit among 138 multimodal imaging phenotypes.

RESULTS

Longitudinal data were consistent with significant bidirectional causal relationship between SLEs and depressive symptoms. In cross-sectional analyses, SLEs were significantly associated with lower bilateral nucleus accumbal volume and lower fractional anisotropy of the forceps major. Depressive symptoms were significantly associated with extensive white matter hyperintensities, thinner cortex, lower subcortical volume, and white matter microstructural deficits, mainly in corticostriatal-limbic structures. Lower bilateral nucleus accumbal volume were the only imaging phenotypes with overlapping effects of depressive symptoms and SLEs (B = -0.032 to -0.023, p = 0.006-0.034). Depressive symptoms and SLEs significantly partially mediated the effects of each other on left and right nucleus accumbens volume (proportion of effects mediated = 12.7-14.3%, p < 0.001-p = 0.008). For the left nucleus accumbens, post-hoc seed-based analysis showed lower resting-state functional connectivity with the left orbitofrontal cortex (cluster size = 83 voxels, p = 5.4 × 10-5) in participants with high v. no SLEs.

CONCLUSIONS

The nucleus accumbens may play a key role in the reciprocity between stress and depressive symptoms.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study

Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion magnetic resonance imaging [MRI]). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N = 34,029) and ABCD Study (N = 8607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI in a genome-wide association study (GWAS) with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.

Chronometric TMS-fMRI of personalized left dorsolateral prefrontal target reveals state-dependency of subgenual anterior cingulate cortex effects

Transcranial magnetic stimulation (TMS) applied to a left dorsolateral prefrontal cortex (DLPFC) area with a specific connectivity profile to the subgenual anterior cingulate cortex (sgACC) has emerged as a highly effective non-invasive treatment option for depression. However, antidepressant outcomes demonstrate significant variability among therapy plans and individuals. One overlooked contributing factor is the individual brain state at the time of treatment. In this study we used interleaved TMS-fMRI to investigate the influence of brain state on acute TMS effects, both locally and remotely. TMS was performed during rest and during different phases of cognitive task processing. Twenty healthy participants were included in this study. In the first session, imaging data for TMS targeting were acquired, allowing for identification of individualized targets in the left DLPFC based on highest anti-correlation with the sgACC. The second session involved chronometric interleaved TMS-fMRI measurements, with 10 Hz triplets of TMS administered during rest and at distinct timings during an N-back task. Consistent with prior findings, interleaved TMS-fMRI revealed significant BOLD activation changes in the targeted network. The precise timing of TMS relative to the cognitive states during the task demonstrated distinct BOLD response in clinically relevant brain regions, including the sgACC. Employing a standardized timing approach for TMS using a task revealed more consistent modulation of the sgACC at the group level compared to stimulation during rest. In conclusion, our findings strongly suggest that acute local and remote effects of TMS are influenced by brain state during stimulation. This study establishes a basis for considering brain state as a significant factor in designing treatment protocols, possibly improving TMS treatment outcomes.

Identifying two distinct neuroanatomical subtypes of first-episode depression using heterogeneity through discriminative analysis

BACKGROUND

Neurobiological heterogeneity in depression remains largely unknown, leading to inconsistent neuroimaging findings.

METHODS

Here, we adopted a novel proposed machine learning method ground on gray matter volumes (GMVs) to investigate neuroanatomical subtypes of first-episode treatment-naïve depression. GMVs were obtained from high-resolution T1-weighted images of 195 patients with first-episode, treatment-naïve depression and 78 matched healthy controls (HCs). Then we explored distinct subtypes of depression by employing heterogeneity through discriminative analysis (HYDRA) with regional GMVs as features.

RESULTS

Two prominently divergent subtypes of first-episode depression were identified, exhibiting opposite structural alterations compared with HCs but no different demographic features. Subtype 1 presented widespread increased GMVs mainly located in frontal, parietal, temporal cortex and partially located in limbic system. Subtype 2 presented widespread decreased GMVs mainly located in thalamus, cerebellum, limbic system and partially located in frontal, parietal, temporal cortex. Subtype 2 had smaller TIV and longer illness duration than Subtype 1. And TIV in Subtype 1 was positively correlated with age of onset while not in Subtype 2, probably implying the different potential neuropathological mechanisms.

LIMITATIONS

Despite results obtained in this study were validated by employing another brain atlas, the conclusions were acquired from a single dataset.

CONCLUSIONS

This study revealed two distinguishing neuroanatomical subtypes of first-episode depression, which provides new insights into underlying biological mechanisms of the heterogeneity in depression and might be helpful for accurate clinical diagnosis and future treatment.