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

Neural correlates of anhedonia in young adults with subthreshold depression: A graph theory approach for cortical-subcortical structural covariance

BACKGROUND

Anhedonia is an enduring symptom of subthreshold depression (StD) and predict later onset of major depressive disorder (MDD). Brain structural covariance describes the inter-regional distribution of morphological changes compared to healthy controls (HC) and reflects brain maturation and disease progression. We investigated neural correlates of anhedonia from the structural covariance.

METHODS

T1-weighted brain magnetic resonance images were acquired from 79 young adults (26 StD, 30 MDD, and 23 HC). Intra-individual structural covariance networks of 68 cortical surface area (CSAs), 68 cortical thicknesses (CTs), and 14 subcortical volumes were constructed. Group-level hubs and principal edges were defined using the global and regional graph metrics, compared between groups, and examined for the association with anhedonia severity.

RESULTS

Global network metrics were comparable among the StD, MDD, and HC. StD exhibited lower centralities of left pallidal volume than HC. StD showed higher centralities than HC in the CSAs of right rostral anterior cingulate cortex (ACC) and pars triangularis, and in the CT of left pars orbitalis. Less anhedonia was associated with higher centralities of left pallidum and right amygdala, higher edge betweenness centralities in the structural covariance (EBSC) of left postcentral gyrus-parahippocampal gyrus and LIPL-right amygdala. More anhedonia was associated with higher centralities of left inferior parietal lobule (LIPL), left postcentral gyrus, left caudal ACC, and higher EBSC of LIPL-left postcentral gyrus, LIPL-right lateral occipital gyrus, and left caudal ACC-parahippocampal gyrus.

LIMITATIONS

This study has a cross-sectional design.

CONCLUSIONS

Structural covariance of brain morphologies within the salience and limbic networks, and among the salience-limbic-default mode-somatomotor-visual networks, are possible neural correlates of anhedonia in depression.

Multimodal Associations of FKBP5 Methylation With Emotion-Regulatory Brain Circuits

BACKGROUND

Understanding the biological processes that underlie individual differences in emotion regulation and stress responsivity is a key challenge for translational neuroscience. The gene FKBP5 is a core regulator in molecular stress signaling that is implicated in the development of psychiatric disorders. However, it remains unclear how FKBP5 DNA methylation in peripheral blood is related to individual differences in measures of neural structure and function and their relevance to daily-life stress responsivity.

METHODS

Here, we characterized multimodal correlates of FKBP5 DNA methylation by combining epigenetic data with neuroimaging and ambulatory assessment in a sample of 395 healthy individuals.

RESULTS

First, we showed that FKBP5 demethylation as a psychiatric risk factor was related to an anxiety-associated reduction of gray matter volume in the ventromedial prefrontal cortex, a brain area that is involved in emotion regulation and mental health risk and resilience. This effect of epigenetic upregulation of FKBP5 on neuronal structure is more pronounced where FKBP5 is epigenetically downregulated at baseline. Leveraging 208 functional magnetic resonance imaging scans during a well-established emotion-processing task, we found that FKBP5 DNA methylation in peripheral blood was associated with functional differences in prefrontal-limbic circuits that modulate affective responsivity to daily stressors, which we measured using ecological momentary assessment in daily life.

CONCLUSIONS

Overall, we demonstrated how FKBP5 contributes to interindividual differences in neural and real-life affect regulation via structural and functional changes in prefrontal-limbic brain circuits.

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.

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.

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.

Novel treatments for anorexia nervosa: Insights from neuroplasticity research

OBJECTIVE

Treatment for anorexia nervosa (AN) remains challenging; there are no approved psychopharmacological interventions and psychotherapeutic strategies have variable efficacy. The investigation of evidence-based treatments has so far been compounded by an underdeveloped understanding into the neurobiological changes associated with the acute stages of AN. There is converging evidence of deficiencies in neuroplasticity in AN.

METHOD

This paper provides an overview of neuroimaging, neuropsychological, molecular and qualitative findings relating to neuroplasticity in AN, translating these findings to the identification of novel biological and psychotherapeutic strategies.

RESULTS

Novel psychopharmacological approaches that may ameliorate deficiencies in neuroplasticity include medications such as ketamine, psilocybin and human recombinant leptin. Anti-inflammatory medications and brain-derived neurotrophic factor mimetics may emerge as potential treatments following further research. Psychotherapeutic strategies that may target neuroplastic deficiencies, as well as having wider effects on identity, include imagery rescripting, memory specificity training, cognitive remediation therapy, exposure therapies, narrative therapies, cultural interventions (e.g. music and arts therapies) and yoga/mindfulness-based interventions.

CONCLUSIONS

Treatments specifically targeted towards mitigating the neurobiological sequalae of AN are warranted, and emerging neurobiological and neuropsychological research utilising longitudinal designs and large sample sizes, as well as initial feasibility studies, are necessitated to bolster translational efforts.

Altered brain function in treatment-resistant depression patients: A resting-state functional magnetic resonance imaging study

BACKGROUND

It has been established that there are functional changes in the brain of treatment-resistant depression (TRD) patients, but previous studies of functional connectivity (FC) usually involved selection of regions of interest based on accumulated a priori knowledge of the disorder. In this study, we combine amplitude of low-frequency fluctuation (ALFF) and FC; this approach, based on the abnormal ALFF, may provide some insights into the neural basis of the disease in terms of fMRI signals of low-frequency fluctuations.

METHODS

A total of 16 TRD patients, who visited the Qingdao Mental Health Center, Shandong Province, China between March 2023 and January 2024, along with 16 normal subjects, were enrolled into this study for functional imaging. In this study, we first explored the ALFF changes of TRD patients at a baseline resting state. Second, we selected the regions that were significantly changed in the ALFF as seeds and calculated the regional activity and functional connectivity (FC) of these regions using a seed-based approach. We also calculated correlations between the percent change in the PDQ-5D scores and ALFF values in brain regions with differing activity for TRD patients.

RESULTS

During the baseline resting state, by using the ALFF, we found a significantly decreased or increased ALFF in the TRD patients relative to the controls. These regions were located in the left/right postcentral gyrus (PoCG.L/PoCG.R), right cuneus(CUN.R). We found that the ALFF values of the right hippocampus (HIP.R) in the TRD group were negatively correlated with the PDQ-5D score. Then, we selected these brain regions as seeds to investigate the FC changes in brains of TRD patients. We found abnormal functional connectivity in left/right middle frontal gyrus(MFG.L/MFG.R), the right Inferior frontal gyrus, opercular part (IFGoperc.R), the left/right Anterior cingulate and paracingulate gyri (ACC.L/ACC.R), the right supramarginal gyrus (SMG.R), and the right Calcarine fissure and surrounding cortex (CAL.R).

CONCLUSION

We found a larger range of altered brain regions in TRD patients compared to healthy controls, especially in the central executive network (CEN), salience network (SN) and default mode network (DMN).

Adult mental health outcomes of adolescent depression and co-occurring alcohol use disorder: a longitudinal cohort study

Depression and alcohol use disorder (AUD) are frequently co-occurring in adolescence, which often goes undetected in routine care. While this may potentially compromise treatment effectiveness and lead to a less favourable long-term prognosis, few longitudinal studies have followed this group into adulthood. The aim of this study was to explore the risk for adult depression, anxiety disorders, suicidality, and AUD in adolescents with concurrent depression and AUD. The study was based on the Uppsala Longitudinal Adolescent Depression Study (ULADS), a Swedish prospective cohort study. Diagnostic interviews were conducted in adolescence (age 16-17) and adulthood (around age 30). Adolescents with concurrent depression and AUD (n = 38) were compared with peers having only depression (n = 189) or neither of the conditions (n = 144). Logistic regression was used to calculate adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Adolescents with concurrent depression and AUD were more likely than their non-affected peers to experience adult depressive episodes (aOR, 5.33; 95% CI, 2.22-12.83), anxiety disorders (4.05; 1.77-9.27), suicidality (5.37; 2.28-12.66), and AUD (7.68; 2.59-22.81). Notably, 34% of adolescents with both depression and AUD subsequently experienced both these conditions as adults, compared to 7% of adolescents with only depression. Adolescents suffering only from depression were less likely than those with both conditions to experience suicidality (0.44; 0.21-0.95) and AUD in adulthood (0.18; 0.07-0.44). These findings underscore the clinical imperative to identify adolescents with this comorbidity. Recognition of the poor long-term prognosis can inform targeted interventions for this vulnerable group, ultimately improving health and well-being throughout the life course.

The mediation role of gray matter volume in the relationship between childhood maltreatment and psychological resilience in adolescents with first-episode major depressive disorder

Previous studies have revealed morphologic alterations in patients with major depressive disorder (MDD) with experiences of childhood trauma. However, the underlying neural mechanisms remain largely unknown. This study aims to explore the brain structural changes and their possible mediation role in the relationship between childhood maltreatment and psychological resilience in drug-naïve adolescents with first-episode MDD. A total of 57 adolescents with first-episode MDD and 36 healthy controls (HCs) completed the T1-weighted magnetic resonance imaging scan. The adverse childhood experiences and current psychological resilience were assessed using the Childhood Trauma Questionnaire-Short Form and the Connor Davidson Resilience Scale, respectively. The voxel-based morphometry approach was applied to examine changes in the gray matter volume (GMV). Compared with the HCs, adolescents with MDD had significantly reduced GMV volumes in the left fusiform gyrus, right orbitofrontal gyrus, right superior temporal gyrus, right calcarine cortex, right middle frontal gyrus, left angular gyrus, right precuneus, right posterior cingulate gyrus, and right posterior central gyrus, as well as significantly increased GMV volumes in the left lenticular putamen and right lenticular pallidum. The GMV of the right calcarine cortex was found to be negatively correlated with the severity of emotional abuse and positively correlated with the level of psychological resilience. Moreover, the GMV of the right calcarine cortex might partially mediate the relationship between childhood maltreatment and psychological resilience. The present study provided further evidence for structural impairments in adolescents with MDD. Our findings also confirmed the important role of depression-related GMV changes in childhood growth experiences and psychological resilience characteristics during adolescent brain maturation.

Structural alterations in a rumination-related network in patients with major depressive disorder

Rumination is a common symptom in major depressive disorder (MDD). Previous work has connected individual differences in rumination to structural properties in various brain regions. Some of these, such as the dorsolateral prefrontal cortex (dlPFC), have also been highlighted as being altered in MDD, suggesting a connection between structural changes and ruminative symptoms. Although informative, such localised relations have limitations in the context of a network view of the brain. To further investigate rumination-related structural changes in depression, and to situate these within potential functional networks, we acquired T1-weighted structural MRI data from patients with MDD (n = 32) and controls (n = 69). Rumination was measured with the Rumination Response Scale. Surface-based, whole-brain analysis of cortical grey-matter identified group differences in the dlPFC that were, however, not related to rumination. Instead, rumination was correlated with grey-matter properties in the right precuneus. Using normative functional connectivity analysis on an independent sample (n = 100), we show these two regions to be interconnected. Further developing a network-based perspective, it was shown that the rumination-related precuneus region is connected with networks associated with processes such as executive function, autobiographical memory, and visual perception. Notably, these processes have been connected to rumination. These results suggest that rumination in depression may be linked to focal structural changes. The effects of these focal changes on rumination may then be connected to their influence on distributed functional networks.

The mediation effect of the inferior-parietal cortex and globus pallidus on the relationship between family conflict and major depressive disorder

OBJECTIVE

Family conflict is an important risk factor for major depressive disorder (MDD) and is associated with structural alterations in the brain. However, it is unclear whether structural alterations associated with family conflict would contribute to depression. This study aims to investigate the neuroimaging characteristics that connect family conflict with depression.

METHODS

This study included 54 healthy controls and 53 antidepressant-free patients with MDD. Both groups completed the Beck Depression Inventory-II, the simplified Chinese version of the Family Environment Scale, and the Childhood Trauma Questionnaire. Structural Magnetic Resonance Imaging data was collected to measure cortical thickness and regional gray matter volumes.

RESULTS

Family conflict has a significant effect on depression diagnosis. Higher levels of family conflict were positively associated with symptoms of sadness, guilty feelings, and punishment feelings in patients, as well as with cortical thickness in the right inferior-parietal cluster and the volumes of the left globus pallidus in all participants. In the patient group, cortical thickness in the right inferior-parietal cluster and volume of the left globus pallidus were negatively related to symptoms of sadness and guilty feelings, respectively. The structural alteration in the right inferior-parietal cluster mediated the relationship of family conflict and sadness, whereas changes in the globus pallidus mediated the associations between family conflict and both depression and guilty feelings in patients.

CONCLUSION

Findings revealed the relationships between family conflict and depression, including both depression diagnosis and specific symptoms. Cortical thickness in the right inferior-parietal cortex and the volume of the left globus pallidus played mediating roles in these relationships, indicating the important contributions of these brain regions to the effect of family conflict on depression.

The different impacts of pain-related negative emotion and trait negative emotion on brain function in patients with inflammatory bowel disease

Inflammatory bowel diseases (IBD) are a group of chronic, non-specific intestinal diseases that could comorbid with varieties of negative emotional constructs, including pain-related negative emotions and trait negative emotions; however, the link between brain functions and different dimensions of negative emotions remains largely unknown. Ninety-eight patients with IBD and forty-six healthy subjects were scanned using a 3.0-T functional magnetic resonance imaging scanner. The amplitudes of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) were used to assess resting-state brain activity. Partial least squares (PLS) correlation was employed to assess the relationship among abnormal brain activities, pain-related and trait negative emotions. Compared to controls, patients with IBD exhibited higher values of ALFF in the right anterior cingulate cortex (ACC), lower values of ALFF in the left postcentral gyrus, and higher values of DC in the bilateral ACC. Multivariate PLS correlation analysis revealed the brain scores of the ACC were correlated with pain-related negative emotions, the brain salience in the left postcentral gyrus was associated with the higher-order trait depression. These findings can enhance our comprehension of how pain-related negative emotion and trait negative emotion affect the brains of patients with IBD in distinct ways.

Enhancing Conceptual Clarity regarding the Construct of Moral Injury

BACKGROUND

The construct of "moral injury" is used widely in the research literature and media to broadly describe the impact of events involving perceived violations of one's sense of right and wrong (herein referred to as "potentially morally injurious events" [PMIEs]).

SUMMARY

In this theoretical review, we provided a brief overview of the "moral injury" construct and its limitations including the lack of consensus-drawn boundaries and operational definitions to guide hypothesis-driven research. We discussed whether this construct can be reliably distinguished from established psychiatric diagnoses and psychological constructs and the inherent challenges in separating or classifying the impact of high-magnitude stressful life events that likely form the majority of PMIEs. Assessments that purportedly measure "moral injury" are reviewed and limitations are discussed such as shared measurement variance with established psychological instruments.

KEY MESSAGES

We identified conceptual strategies for investigating behavioral and neurobiological features of PMIEs that could be used to inform the field of traumatic stress. We concluded that the construct of "moral injury" may provide an interpretive framework for positing why someone may be beset by guilt, shame, and/or rage whereas existing psychiatric diagnoses such as post-traumatic stress disorder and depression provide comprehensive descriptions regarding what someone might experience following extremely stressful events. We proposed directions to better clarify the boundaries of "moral injury" versus established psychiatric categories that could be used to enhance the conceptualization and assessment of this construct.

Prediction of adolescent depression from prenatal and childhood data from ALSPAC using machine learning

Depression is a major cause of disability and mortality for young people worldwide and is typically first diagnosed during adolescence. In this work, we present a machine learning framework to predict adolescent depression occurring between ages 12 and 18 years using environmental, biological, and lifestyle features of the child, mother, and partner from the child's prenatal period to age 10 years using data from 8467 participants enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC). We trained and compared several cross-sectional and longitudinal machine learning techniques and found the resulting models predicted adolescent depression with recall (0.59 ± 0.20), specificity (0.61 ± 0.17), and accuracy (0.64 ± 0.13), using on average 39 out of the 885 total features (4.4%) included in the models. The leading informative features in our predictive models of adolescent depression were female sex, parental depression and anxiety, and exposure to stressful events or environments. This work demonstrates how using a broad array of evidence-driven predictors from early in life can inform the development of preventative decision support tools to assist in the early detection of risk for mental illness.

An overview of major depression disorder: The endocannabinoid system as a potential target for therapy

Major depressive disorder is the psychiatric disease with the highest global prevalence, impacting social functioning and decreasing the quality of life. The partial pathophysiological knowledge of the disease, the economic burden and the low remission rates are sufficient justification to carry out an update on the subject in the search for new therapeutic approaches and targets. The endocannabinoid system has been linked to the development of depression, and its stimulation or antagonism is a promising approach in the treatment of major depressive disorder. Cannabidiol (CBD) and its properties have been widely studied recently; its analgesic, anti-inflammatory, antineoplastic and neuroprotective roles have even been reported in animal models and clinical trials, achieving its approved use for certain neurodegenerative pathologies. The use of CBD in depression biomodels and clinical trials has not been the exception, and here we contrast the current evidence of its administration and pharmacology against the pathological mechanisms of major depressive disorder.

Associations between antagonistic SNPs for neuropsychiatric disorders and human brain structure

A previously published genome-wide association study (GWAS) meta-analysis across eight neuropsychiatric disorders identified antagonistic single-nucleotide polymorphisms (SNPs) at eleven genomic loci where the same allele was protective against one neuropsychiatric disorder and increased the risk for another. Until now, these antagonistic SNPs have not been further investigated regarding their link to brain structural phenotypes. Here, we explored their associations with cortical surface area and cortical thickness (in 34 brain regions and one global measure each) as well as the volumes of eight subcortical structures using summary statistics of large-scale GWAS of brain structural phenotypes. We assessed if significantly associated brain structural phenotypes were previously reported to be associated with major neuropsychiatric disorders in large-scale case-control imaging studies by the ENIGMA consortium. We further characterized the effects of the antagonistic SNPs on gene expression in brain tissue and their association with additional cognitive and behavioral phenotypes, and performed an exploratory voxel-based whole-brain analysis in the FOR2107 study (n = 754 patients with major depressive disorder and n = 847 controls). We found that eight antagonistic SNPs were significantly associated with brain structural phenotypes in regions such as anterior parts of the cingulate cortex, the insula, and the superior temporal gyrus. Case-control differences in implicated brain structural phenotypes have previously been reported for bipolar disorder, major depressive disorder, and schizophrenia. In addition, antagonistic SNPs were associated with gene expression changes in brain tissue and linked to several cognitive-behavioral traits. In our exploratory whole-brain analysis, we observed significant associations of gray matter volume in the left superior temporal pole and left superior parietal region with the variants rs301805 and rs1933802, respectively. Our results suggest that multiple antagonistic SNPs for neuropsychiatric disorders are linked to brain structural phenotypes. However, to further elucidate these findings, future case-control genomic imaging studies are required.

The status of MRI databases across the world focused on psychiatric and neurological disorders

Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders.

The importance of timing of socioeconomic disadvantage throughout development for depressive symptoms and brain structure

Prior studies have reported associations between socioeconomic disadvantage, brain structure and mental health outcomes, but the timing of these relations is not well understood. Using prospective longitudinal data from the Avon Longitudinal Study of Parents and Children (ALSPAC), this preregistered study examined whether socioeconomic disadvantage related differentially to depressive symptoms (n=3012-3530) and cortical and subcortical structures (n=460-733) in emerging adults, depending on the timing of exposure to socioeconomic disadvantage. Family income in early childhood and own income measured concurrently were both significantly related to depressive symptoms in emerging adulthood. Similar results were observed for perceived financial strain. In contrast, only family income in early childhood was associated with brain structure in emerging adulthood, with positive associations with intracranial volume and total and regional cortical surface area. The findings suggest that both objective and subjective aspects of one's financial standing throughout development relate to depressive symptoms in adulthood, but that specifically early life family income is related to brain structural features in emerging adulthood. This suggests that associations between socioeconomic disadvantage and brain structure originate early in neurodevelopment, highlighting the role of timing of socioeconomic disadvantage.

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.

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.

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.

Dissecting depression symptoms: Multi-omics clustering uncovers immune-related subgroups and cell-type specific dysregulation

In a subset of patients with mental disorders, such as depression, low-grade inflammation and altered immune marker concentrations are observed. However, these immune alterations are often assessed by only one data type and small marker panels. Here, we used a transdiagnostic approach and combined data from two cohorts to define subgroups of depression symptoms across the diagnostic spectrum through a large-scale multi-omics clustering approach in 237 individuals. The method incorporated age, body mass index (BMI), 43 plasma immune markers and RNA-seq data from peripheral mononuclear blood cells (PBMCs). Our initial clustering revealed four clusters, including two immune-related depression symptom clusters characterized by elevated BMI, higher depression severity and elevated levels of immune markers such as interleukin-1 receptor antagonist (IL-1RA), C-reactive protein (CRP) and C-C motif chemokine 2 (CCL2 or MCP-1). In contrast, the RNA-seq data mostly differentiated a cluster with low depression severity, enriched in brain related gene sets. This cluster was also distinguished by electrocardiography data, while structural imaging data revealed differences in ventricle volumes across the clusters. Incorporating predicted cell type proportions into the clustering resulted in three clusters, with one showing elevated immune marker concentrations. The cell type proportion and genes related to cell types were most pronounced in an intermediate depression symptoms cluster, suggesting that RNA-seq and immune markers measure different aspects of immune dysregulation. Lastly, we found a dysregulation of the SERPINF1/VEGF-A pathway that was specific to dendritic cells by integrating immune marker and RNA-seq data. This shows the advantages of combining different data modalities and highlights possible markers for further stratification research of depression symptoms.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

Dynamic functional connections analysis with spectral learning for brain disorder detection

Dynamic functional connections (dFCs), can reveal neural activities, which provides an insightful way of mining the temporal patterns within the human brain and further detecting brain disorders. However, most existing studies focus on the dFCs estimation to identify brain disorders by shallow temporal features and methods, which cannot capture the inherent temporal patterns of dFCs effectively. To address this problem, this study proposes a novel method, named dynamic functional connections analysis with spectral learning (dCSL), to explore inherently temporal patterns of dFCs and further detect the brain disorders. Concretely, dCSL includes two components, dFCs estimation module and dFCs analysis module. In the former, dFCs are estimated via the sliding window technique. In the latter, the spectral kernel mapping is first constructed by combining the Fourier transform with the non-stationary kernel. Subsequently, the spectral kernel mapping is stacked into a deep kernel network to explore higher-order temporal patterns of dFCs through spectral learning. The proposed dCSL, sharing the benefits of deep architecture and non-stationary kernel, can not only calculate the long-range relationship but also explore the higher-order temporal patterns of dFCs. To evaluate the proposed method, a set of brain disorder classification tasks are conducted on several public datasets. As a result, the proposed dCSL achieves 5% accuracy improvement compared with the widely used approaches for analyzing sequence data, 1.3% accuracy improvement compared with the state-of-the-art methods for dFCs. In addition, the discriminative brain regions are explored in the ASD detection task. The findings in this study are consistent with the clinical performance in ASD.

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.

Subtyping drug-free first-episode major depressive disorder based on cortical surface area alterations

BACKGROUND

Major depressive disorder (MDD) is recognized as a complex and heterogeneous metal illness, characterized by diverse clinical symptoms and variable treatment outcomes. Previous studies have repeatedly reported alterations in brain morphology in MDD, but findings vary across sample characteristics. Whether this neurobiological substrate could stratify MDD into more homogeneous clinical subgroups thus improving personalized medicine remains unknown.

METHODS

We included 65 drug-free patients with first-episode MDD and 66 healthy controls (HCs) and collected their structural MRI data. We performed the surface reconstruction and calculated cortical surface area using Freesurfer. The surface area of 34 Gy matter regions in each hemisphere based on the Desikan-Killiany atlas were extracted for each participant and subtyping results were obtained with the Louvain community detection algorithm. The demographic and clinical characteristics were then compared between MDD subgroups.

RESULTS

Two subgroups defined by distinct patterns of cortical surface area were identified in first-episode MDD. Subgroup 1 exhibited a significant reduction in surface area across nearly the entire cortex compared to subgroup 2 and HCs, whereas subgroup 2 demonstrated increased surface area than HCs. Further, subgroup 1 exhibited a higher proportion of females, and higher severity of anxiety symptoms compared to subgroup 2.

LIMITATIONS

The relatively small sample size.

CONCLUSIONS

This study identified two neurobiologically subgroups with distinct alterations in cortical surface area among drug-free patients with first-episode MDD. Our results highlight the promise of in delineating morphological heterogeneity within MDD, particularly in relation to the severity of anxiety symptoms.

Medial prefrontal neuroplasticity during extended-release naltrexone treatment of opioid use disorder - a longitudinal structural magnetic resonance imaging study

Opioid use disorder (OUD) has been linked to macroscopic structural alterations in the brain. The monthly injectable, extended-release formulation of μ-opioid antagonist naltrexone (XR-NTX) is highly effective in reducing opioid craving and preventing opioid relapse. Here, we investigated the neuroanatomical effects of XR-NTX by examining changes in cortical thickness during treatment for OUD. Forty-seven OUD patients underwent structural magnetic resonance imaging and subjectively rated their opioid craving ≤1 day before (pre-treatment) and 11 ± 3 days after (on-treatment) the first XR-NTX injection. A sample of fifty-six non-OUD individuals completed a single imaging session and served as the comparison group. A publicly available [¹¹C]carfentanil positron emission tomography dataset was used to assess the relationship between changes in cortical thickness and μ-opioid receptor (MOR) binding potential across brain regions. We found that the thickness of the medial prefrontal and anterior cingulate cortices (mPFC/aCC; regions with high MOR binding potential) was comparable between the non-OUD individuals and the OUD patients at pre-treatment. However, among the OUD patients, mPFC/aCC thickness significantly decreased from pre-treatment to on-treatment. A greater reduction in mPFC/aCC thickness was associated with a greater reduction in opioid craving. Taken together, our study suggests XR-NTX-induced cortical thickness reduction in the mPFC/aCC regions in OUD patients. The reduction in thickness does not appear to indicate a restoration to the non-OUD level but rather reflects XR-NTX's distinct therapeutic impact on an MOR-rich brain structure. Our findings highlight the neuroplastic effects of XR-NTX that may inform the development of novel OUD interventions.

Frontostriatal salience network expansion in individuals in depression

Decades of neuroimaging studies have shown modest differences in brain structure and connectivity in depression, hindering mechanistic insights or the identification of risk factors for disease onset1. Furthermore, whereas depression is episodic, few longitudinal neuroimaging studies exist, limiting understanding of mechanisms that drive mood-state transitions. The emerging field of precision functional mapping has used densely sampled longitudinal neuroimaging data to show behaviourally meaningful differences in brain network topography and connectivity between and in healthy individuals2-4, but this approach has not been applied in depression. Here, using precision functional mapping and several samples of deeply sampled individuals, we found that the frontostriatal salience network is expanded nearly twofold in the cortex of most individuals with depression. This effect was replicable in several samples and caused primarily by network border shifts, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was stable over time, unaffected by mood state and detectable in children before the onset of depression later in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in frontostriatal circuits that tracked fluctuations in specific symptoms and predicted future anhedonia symptoms. Together, these findings identify a trait-like brain network topology that may confer risk for depression and mood-state-dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Dataset factors associated with age-related changes in brain structure and function in neurodevelopmental conditions

With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.

The role of serotonin in depression-A historical roundup and future directions

Depression is one of the most common psychiatric disorders worldwide, affecting approximately 280 million people, with probably much higher unrecorded cases. Depression is associated with symptoms such as anhedonia, feelings of hopelessness, sleep disturbances, and even suicidal thoughts. Tragically, more than 700 000 people commit suicide each year. Although depression has been studied for many decades, the exact mechanisms that lead to depression are still unknown, and available treatments only help a fraction of patients. In the late 1960s, the serotonin hypothesis was published, suggesting that serotonin is the key player in depressive disorders. However, this hypothesis is being increasingly doubted as there is evidence for the influence of other neurotransmitters, such as noradrenaline, glutamate, and dopamine, as well as larger systemic causes such as altered activity in the limbic network or inflammatory processes. In this narrative review, we aim to contribute to the ongoing debate on the involvement of serotonin in depression. We will review the evolution of antidepressant treatments, systemic research on depression over the years, and future research applications that will help to bridge the gap between systemic research and neurotransmitter dynamics using biosensors. These new tools in combination with systemic applications, will in the future provide a deeper understanding of the serotonergic dynamics in depression.

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.

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.

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.

Hippocampal, thalamic, and amygdala subfield morphology in major depressive disorder: an ultra-high resolution MRI study at 7-Tesla

Morphological changes in the hippocampal, thalamic, and amygdala subfields have been suggested to form part of the pathophysiology of major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented in-depth examinations at the subfield level, precluding a fine-grained understanding of these subfields and their involvement in MDD pathophysiology. We uniquely employed ultra-high field MRI at 7.0 Tesla to map hippocampal, thalamic, and amygdala subfields in MDD. Fifty-six MDD patients and 14 healthy controls (HCs) were enrolled in the final analysis. FreeSurfer protocols were used to segment hippocampal, thalamic, and amygdala subfields. Bayesian analysis was then implemented to assess differences between groups and relations with clinical features. While no effect was found for MDD diagnosis (i.e., case-control comparison), clinical characteristics of MDD patients were associated with subfield volumes of the hippocampus, thalamus, and amygdala. Specifically, the severity of depressive symptoms, insomnia, and childhood trauma in MDD patients related to lower thalamic subfield volumes. In addition, MDD patients with typical MDD versus those with atypical MDD showed lower hippocampal, thalamic, and amygdala subfield volumes. MDD patients with recurrent MDD versus those with first-episode MDD also showed lower thalamic subfield volumes. These findings allow uniquely fine-grained insights into hippocampal, thalamic, and amygdala subfield morphology in MDD, linking some of them to the clinical manifestation of MDD.

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.

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.

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).

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 current study investigated whether cortical morphology moderated the relationship between stress from the COVID-19 pandemic and the development of internalizing symptoms in familial high-risk adolescents.

METHODS

Prior to the COVID-19 pandemic, 72 adolescents (27 male) 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 to 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.

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.