Development of Neuroimaging-Based Biomarkers in Major Depression

Altered brain regional homogeneity is associated with cognitive dysfunction in first-episode drug-naive major depressive disorder: A resting-state fMRI study

BACKGROUND

Our study aimed to explore the abnormal spontaneous brain activity by regional homogeneity (ReHo) and its association with cognitive function to understand the neuropathology of major depressive disorder (MDD).

METHODS

ReHo was used to investigate brain activities of 60 patients with first-episode drug-naive MDD and 60 healthy controls (HCs). Partial correlation analysis was conducted on altered ReHo values and the severity of symptoms and cognitive deficits. Moreover, support vector machine analysis was used to evaluate the accuracy of abnormal ReHo values in distinguishing patients with MDD from HCs.

RESULTS

Compared with HCs, patients with MDD showed significantly increased ReHo values in the right cerebellum crus2 and right thalamus and decreased ReHo values in the right angular gyrus (AG) and right precuneus (PCUN). The ReHo values in right cerebellum crus2 and right AG were positively associated with working memory and visual learning, respectively. Furthermore, the combination of ReHo values in the right cerebellum crus2 and right PCUN discriminated the patients with MDD from HCs with specificity, sensitivity, and accuracy of 0.9688, 0.6250, and 0.90, respectively.

LIMITATIONS

The design of repeated cross-sectional surveys does not allow analyses of within individual changes.

CONCLUSIONS

Our study revealed that the pathophysiology mechanism of cognitive deficits in MDD may be related to abnormal spontaneous brain activity. Moreover, the combination of ReHo values in the right cerebellum crus2 and right PCUN can be used to discriminate patients with MDD from HCs effectively.

Affective neural circuits and inflammatory markers linked to depression and anxiety symptoms in patients with comorbid obesity

Although we have effective treatments for depression and anxiety, we lack mechanistic understanding or evidence-based strategies to tailor these treatments in the context of major comorbidities such as obesity. The current feasibility study uses functional neuroimaging and biospecimen data to determine if changes in inflammatory markers, fecal short-chain fatty acids, and neural circuit-based targets can predict depression and anxiety outcomes among participants with comorbid obesity. Blood and stool samples and functional magnetic resonance imaging data were obtained at baseline and 2 months, during the parent ENGAGE-2 trial. From 30 participants with both biospecimen and fMRI data, this subsample study explored the relationship among changes in inflammatory markers and fecal short-chain fatty acids and changes in neural targets, and their joint relationship with depression and anxiety symptoms. Bivariate and partial correlation, canonical correlation, and partial least squares analyses were conducted, with adjustments for age, sex, and treatment group. Initial correlation analyses revealed three inflammatory markers (IL-1RA, IL-6, and TNF-α) and five neural targets (in Negative Affect, Positive Affect, and Default Mode Circuits) with significantly associated changes at 2 months. Partial least squares analyses then showed that changes in IL-1RA and TNF-α and changes in three neural targets (in Negative Affect and Positive Affect Circuits) at 2 months were associated with changes in depression and anxiety symptoms at 6 months. This study sheds light on the plausibility of incorporation of inflammatory and gastrointestinal biomarkers with neural targets as predictors of depression and comorbid anxiety outcomes among patients with obesity.

Integrating functional neuroimaging and serum proteins improves the diagnosis of major depressive disorder

BACKGROUND

The lack of effective objective diagnostic biomarkers for major depressive disorder (MDD) leads to high misdiagnosis. Compared with healthy controls (HC), abnormal brain functions and protein levels are often observed in MDD. However, it is unclear whether combining these changed multidimensional indicators could help improve the diagnosis of MDD.

METHODS

Sixty-three MDD and eighty-one HC subjects underwent resting-state fMRI scans, among whom 37 MDD and 45 HC provided blood samples. Amplitudes of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and serum levels of brain-derived neurotrophic factor (BDNF), cortisol, and multiple cytokines were measured and put into the linear discriminant analysis (LDA) to construct corresponding MDD diagnostic models. The area under the receiver operating characteristic curve (AUC) of 5-fold cross-validation was calculated to evaluate each model's performance.

RESULTS

Compared with HC, MDD patients' spontaneous brain activity, serum BDNF, cortisol, interleukin (IL)-4, IL-6, and IL-10 levels changed significantly. The combinations of unidimensional multi-indicator had better diagnostic performance than a single one. The model consisted of multidimensional multi-indicator further exhibited conspicuously superior diagnostic efficiency than those constructed with unidimensional multi-indicator, and its AUC, sensitivity, specificity, and accuracy of 5-fold cross-validation were 0.99, 92.0 %, 100.0 %, and 96.3 %, respectively.

LIMITATIONS

This cross-sectional study consists of relatively small samples and should be replicated in larger samples with follow-up data to optimize the diagnostic model.

CONCLUSIONS

MDD patients' neuroimaging features and serum protein levels significantly changed. The model revealed by LDA could diagnose MDD with high accuracy, which may serve as an ideal diagnostic biomarker for MDD.

The Serum Brain-Derived Neurotrophic Factor Increases in Serotonin Reuptake Inhibitor Responders Patients with First-Episode, Drug-Naïve Major Depression

Brain-derived neurotrophic factor (BDNF) is a growth factor synthesized in the cell bodies of neurons and glia, which affects neuronal maturation, the survival of nervous system, and synaptic plasticity. BDNF play an important role in the pathophysiology of major depression (MD). The serum BDNF levels changed over time, or with the improvement in depressive symptoms. However, the change of serum BDNF during pharmacotherapy remains obscure in MDD. In particular, the changes in serum BDNF associated with pharmacotherapy have not yet been fully elucidated. The present study aimed to compare the changes in serum BDNF concentrations in first-episode, drug-naive patients with MD treated with antidepressants between treatment-response and treatment-nonresponse groups. The study included 35 inpatients and outpatients composed of 15 males and 20 females aged 36.7 ± 6.8 years at the Department of Psychiatry of our University Hospital. All patients met the DSM-5 diagnostic criteria for MD. The antidepressants administered included paroxetine, duloxetine, and escitalopram. Severity of depressive state was assessed using the 17-item HAMD before and 8 weeks after drug administration. Responders were defined as those whose total HAMD scores at 8 weeks had decreased by 50% or more compared to those before drug administration, while non-responders were those whose total HAMD scores had decreased by less than 50%. Here we showed that serum BDNF levels were not significantly different at any point between the two groups. The responder group, but not the non-responder group, showed statistically significant changes in serum BDNF 0 and serum BDNF 8. The results suggest that the changes of serum BDNF might differ between the two groups. The measurement of serum BDNF has the potential to be a useful predictor of pharmacotherapy in patients with first-episode, drug-naïve MD.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

The Effectiveness of Physical Activity Interventions on Depression in Korea: A Systematic Review and Meta-Analysis

Since the onset of the COVID-19 pandemic, Korea has ranked first in the OECD, with a prevalence of 36.8% of depression. Thus, this study aimed to estimate the effect size of physical activity as an alternative tool for depression symptoms using meta-analysis. A meta-analysis on depressive symptoms was performed on 18 studies published in Korean domestic journals. The moderating variables hypothesized in this study included age groups of participants; depressive symptoms; and frequency, intensity, time, type, and duration of the intervention. The overall effect size of physical activity on depression was moderate (0.56 [95% CI: 0.39 to 0.91]). Specifically, physical activity was slightly more effective in reducing depression in participants with an 18–64 age group compared to older people over 65 years old, while it was most effective for participants without depressive symptoms compared to participants with mild and severe symptoms. Further, the subgroup analysis revealed that performing two times a week for 30 to 60 min with progressive intensity for 1–8 weeks may be the most effective for Koreans. The results of this study can provide guidelines for the most effective physical activity program for Koreans.

Possible antidepressant mechanisms of omega-3 polyunsaturated fatty acids acting on the central nervous system

Omega-3 polyunsaturated fatty acids (PUFAs) can play important roles in maintaining mental health and resistance to stress, and omega-3 PUFAs supplementation can display beneficial effects on both the prevention and treatment of depressive disorders. Although the underlying mechanisms are still unclear, accumulated evidence indicates that omega-3 PUFAs can exhibit pleiotropic effects on the neural structure and function. Thus, they play fundamental roles in brain activities involved in the mood regulation. Since depressive symptoms have been assumed to be of central origin, this review aims to summarize the recently published studies to identify the potential neurobiological mechanisms underlying the anti-depressant effects of omega-3 PUFAs. These include that of (1) anti-neuroinflammatory; (2) hypothalamus-pituitary-adrenal (HPA) axis; (3) anti-oxidative stress; (4) anti-neurodegeneration; (5) neuroplasticity and synaptic plasticity; and (6) modulation of neurotransmitter systems. Despite many lines of evidence have hinted that these mechanisms may co-exist and work in concert to produce anti-depressive effects, the potentially multiple sites of action of omega-3 PUFAs need to be fully established. We also discussed the limitations of current studies and suggest future directions for preclinical and translational research in this field.

How Inflammation Affects the Brain in Depression: A Review of Functional and Structural MRI Studies

This narrative review discusses how peripheral and central inflammation processes affect brain function and structure in depression, and reports on recent peripheral inflammatory marker-based functional and structural magnetic resonance imaging (MRI) studies from the perspective of neural-circuit dysfunction in depression. Chronic stress stimulates the activity of microglial cells, which increases the production of pro-inflammatory cytokines in the brain. In addition, microglial activation promotes a shift from the synthesis of serotonin to the synthesis of neurotoxic metabolites of the kynurenine pathway, which induces glutamate-mediated excitotoxicity in neurons. Furthermore, the region specificity of microglial activation is hypothesized to contribute to the vulnerability of specific brain regions in the depression-related neural circuits to inflammation-mediated brain injury. MRI studies are increasingly investigating how the blood levels of inflammatory markers such as C-reactive protein, interleukin (IL)-1β, IL-6, and tumor necrosis factor-α are associated with functional and structural neuroimaging markers in depression. Functional MRI studies have found that peripheral inflammatory markers are associated with aberrant activation patterns and altered functional connectivity in neural circuits involved in emotion regulation, reward processing, and cognitive control in depression. Structural MRI studies have suggested that peripheral inflammatory markers are related to reduced cortical gray matter and subcortical volumes, cortical thinning, and decreased integrity of white matter tracts within depression-related neural circuits. These neuroimaging findings may improve our understanding of the relationships between neuroinflammatory processes at the molecular level and macroscale in vivo neuralcircuit dysfunction in depression.