Differentiating bipolar disorder from unipolar depression in youth: A systematic literature review of neuroimaging research studies

Study on exosomes for identifying bipolar disorder in early stage: A cross-sectional and validation study protocol

BACKGROUND

The difficulty is remained to accurately distinguish bipolar disorder (BD) from major depressive disorder (MDD) in early stage, with a delayed diagnosis for 5-10 years. BD patients are often treated with antidepressants systematically due to being diagnosed with MDD, affecting the disease course and clinical outcomes. The current study aims to explore the role of plasma exosomes as biomarker to distinguish BD from MDD in early stage.

METHODS

Two stages are included. The first stage is a cross-sectional study, comparing the concentrations of plasma exosome microRNA and related proteins among BD group, MDD group, and healthy controls (HC) group (n = 40 respectively), to identify target biomarkers preliminarily. The "Latent Class Analysis" and "Receiver Operating Characteristic" analysis will be performed to determine the optimal concentration range for each biomarker. The second stage is to validate target markers in subjects, coming from an ongoing study focusing on patients with a first depressive episode. All target biomarkers will be test in plasma samples reserved at the initial stage to detect whether the diagnosis indicated by biomarker level is consistent with the diagnosis by DSM-5. Furthermore, the correlation between specific biomarkers and the manic episode, suicidal ideation, and adverse reactions will also be observed.

DISCUSSION

Exosome-derived microRNA and related proteins have potential in serving as a good medium for exploring mental disorders because it can pass through the blood-brain barrier bidirectionally and convey a large amount of information stably. Improving the early diagnosis of BD would help implement appropriate intervention strategy as early as possible and significantly reduce the burden of disease.

Genetics, epigenetics, and neurobiology of childhood-onset depression: an umbrella review

Depression is a serious and persistent psychiatric disorder that commonly first manifests during childhood. Depression that starts in childhood is increasing in frequency, likely due both to evolutionary trends and to increased recognition of the disorder. In this umbrella review, we systematically searched the extant literature for genetic, epigenetic, and neurobiological factors that contribute to a childhood onset of depression. We searched PubMed, EMBASE, OVID/PsychInfo, and Google Scholar with the following inclusion criteria: (1) systematic review or meta-analysis from a peer-reviewed journal; (2) inclusion of a measure assessing early age of onset of depression; and (3) assessment of neurobiological, genetic, environmental, and epigenetic predictors of early onset depression. Findings from 89 systematic reviews of moderate to high quality suggest that childhood-onset depressive disorders have neurobiological, genetic, environmental, and epigenetic roots consistent with a diathesis-stress theory of depression. This review identified key putative markers that may be targeted for personalized clinical decision-making and provide important insights concerning candidate mechanisms that might underpin the early onset of depression.

Common and unique alterations of functional connectivity in major depressive disorder and bipolar disorder

OBJECTIVE

Major depressive disorder (MDD) and bipolar disorder (BD) are considered whole-brain disorders with some common clinical and neurobiological features. It is important to investigate neural mechanisms to distinguish between the two disorders. However, few studies have explored the functional dysconnectivity between the two disorders from the whole brain level.

METHODS

In this study, 117 patients with MDD, 65 patients with BD, and 116 healthy controls completed resting-state functional magnetic resonance imaging (R-fMRI) scans. Both edge-based network construction and large-scale network analyses were applied.

RESULTS

Results found that both the BD and MDD groups showed decreased FC in the whole brain network. The shared aberrant network across patients involves the visual network (VN), sensorimotor network (SMN), dorsal attention network (DAN), and ventral attention network (VAN), which is related to the processing of external stimuli. The default mode network (DMN) and the limbic network (LN) abnormalities were only found in patients with MDD. Furthermore, results showed the highest decrease in edges of patients with MDD in between-network FC in SMN-VN, whereas in VAN-VN of patients with BD.

CONCLUSIONS

Our findings indicated that both MDD and BD are extensive abnormal brain network diseases, mainly aberrant in those brain networks correlated to the processing of external stimuli, especially the attention network. Specific altered functional connectivity also was found in MDD and BD groups, respectively. These results may provide possible trait markers to distinguish the two disorders.

Differences in intracellular protein levels in monocytes and CD4+ lymphocytes between bipolar depressed patients and healthy controls: A pilot study with tyramine-based signal-amplified flow cytometry

BACKGROUND

Molecular biomarkers for bipolar disorder (BD) that distinguish it from other manifestations of depressive symptoms remain unknown. The aim of this study was to determine if a very sensitive tyramine-based signal-amplification technology for flow cytometry (CellPrint™) could facilitate the identification of cell-specific analyte expression profiles of peripheral blood cells for bipolar depression (BPD) versus healthy controls (HCs).

METHODS

The diagnosis of psychiatric disorders was ascertained with Mini International Neuropsychiatric Interview for DSM-5. Expression levels for eighteen protein analytes previously shown to be related to bipolar disorder were assessed with CellPrint™ in CD4⁺ T cells and monocytes of bipolar patients and HCs. Implementation of protein-protein interaction (PPI) network and pathway analysis was subsequently used to identify new analytes and pathways for subsequent interrogations.

RESULTS

Fourteen drug-naïve or -free patients with bipolar I or II depression and 17 healthy controls (HCs) were enrolled. The most distinguishable changes in analyte expression based on t-tests included GSK3β, HMGB1, IRS2, phospho-GSK3αβ, phospho-RELA, and TSPO in CD4⁺ T cells and calmodulin, GSK3β, IRS2, and phospho-HS1 in monocytes. Subsequent PPI and pathway analysis indicated that prolactin, leptin, BDNF, and interleukin-3 signal pathways were significantly different between bipolar patients and HCs.

LIMITATION

The sample size of the study was small and 2 patients were on medications.

CONCLUSION

In this pilot study, CellPrint™ was able to detect differences in cell-specific protein levels between BPD patients and HCs. A subsequent study including samples from patients with BPD, major depressive disorder, and HCs is warranted.

Cross-sectional analysis of whole-brain microstructural changes in adult patients with bipolar and unipolar depression by diffusion kurtosis imaging

RATIONALE AND OBJECTIVES

More than half of the bipolar depression (BD) subjects are misdiagnosed as unipolar depression (UD) due to lack of objective diagnostic criteria. We aimed to identify microstructural neuronal changes differentiating BD from UD groups using diffusion kurtosis imaging (DKI). The objective of the study is to identify an objective neuro-imaging marker to differentiate BD from UD.

MATERIALS AND METHODS

A prospective, cross-sectional study included total of 62 subjects with diagnosis of bipolar depression (n = 21), unipolar depression (n = 21), and healthy controls (n = 20). All subjects underwent diffusion-weighted imaging (b = 0,1000,2000) of the whole brain on 3-Tesla MR scanner. DKI data was analyzed using 189 region whole-brain atlas. Eight diffusion and kurtosis metrics including mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), fractional anisotropy (FA), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), and kurtosis fractional anisotropy (FKA) were measured against these 189 regions. Principle component analysis (PCA) was utilized to identify the most significant regions of the brain. ANOVA with post hoc tests was used for analyzing these regions.

RESULTS

BD group showed increased MD, RD, decreased AK at the left amygdala and decreased MK and RK at the right hemi-cerebellum. UD group showed increased MK and RK at the right external capsule; and increased AK, MK, and RK at the right amygdala.

CONCLUSION

The right and left amygdala, right external capsule, and right hemi-cerebellum showed microstructural abnormalities capable of differentiating BD and UD groups. Diffusion imaging especially DKI can aid in management of depression patients.

Neurostructural and neurocognitive correlates of APOE ε4 in youth bipolar disorder

BACKGROUND

Bipolar disorder (BD) is a clinical risk factor for Alzheimer's disease (AD). Apolipoprotein E ε4 (APOE ε4), a genetic risk factor for AD, has been associated with brain structure and neurocognition in healthy youth.

AIMS

We evaluated whether there was an association between APOE ε4 with neurostructure and neurocognition in youth with BD.

METHODS

Participants included 150 youth (78 BD:19 ε4-carriers, 72 controls:17 ε4-carriers). 3T-magnetic resonance imaging yielded measures of cortical thickness, surface area, and volume. Regions-of-interest (ROI) and vertex-wise analyses of the cortex were conducted. Neurocognitive tests of attention and working memory were examined.

RESULTS

Vertex-wise analyses revealed clusters with a diagnosis-by-APOE ε4 interaction effect for surface area (p = 0.002) and volume (p = 0.046) in pars triangularis (BD ε4-carriers > BD noncarriers), and surface area (p = 0.03) in superior frontal gyrus (controls ε4-carriers > other groups). ROI analyses were not significant. A significant interaction effect for working memory (p = 0.001) appeared to be driven by nominally poorer performance in BD ε4-carriers but not control ε4-carriers; however, post hoc contrasts were not significant.

CONCLUSIONS

APOE ε4 was associated with larger neurostructural metrics in BD and controls, however, the regional association of APOE ε4 with neurostructure differed between groups. The role of APOE ε4 on neurodevelopmental processes is a plausible explanation for the observed differences. Future studies should evaluate the association of APOE ε4 with pars triangularis and its neurofunctional implications among youth with BD.

Determinants and predictors of mental health during and after COVID-19 lockdown among university students in Malaysia

BACKGROUND

Young adults, particularly university students might be at greater risk of developing psychological distress, and exhibiting symptoms of anxiety and depression during the COVID-19 pandemic. The primary objective of this study was to explore and compare the determinants and predictors of mental health (anxiety and depression) during and after the COVID-19 lockdown among university students.

METHODS

This was an observational, cross-sectional study with a sample size of 417 students. An online survey utilizing International Physical Activity Questionnaire-Short Form (IPAQ-SF), General Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) was distributed to Universiti Tunku Abdul Rahman students via Google forms.

RESULTS

During lockdown, family income [χ2 (1, n = 124) = 5.155, p = 0.023], and physical activity (PA) [χ2 (1, n = 134) = 6.366, p = 0.012] were associated with anxiety, while depression was associated with gender [χ2 (1, n = 75) = 4.655, p = 0.031]. After lockdown, family income was associated with both anxiety [χ2 (1, n = 111) = 8.089, p = 0.004], and depression [χ2 (1, n = 115) = 9.305, p = 0.002]. During lockdown, family income (OR = 1.60, p = 0.018), and PA (OR = 0.59, p = 0.011) were predictors for anxiety, while gender (OR = 0.65, p = 0.046) was a predictor for depression. After lockdown, family income was a predictor for both anxiety (OR = 1.67, p = 0.011), and depression (OR = 1.70, p = 0.009).

CONCLUSION

Significant negative effects attributed to the COVID-19 lockdown, and certain factors predisposed to the worsening of mental health status in university students. Low family income, PA, and female gender were the major determinants and predictors linked to anxiety and depression.

A graph theory neuroimaging approach to distinguish the depression of bipolar disorder from major depressive disorder in adolescents and young adults

BACKGROUND

Markers to differentiate depressions of bipolar disorder (BD-Dep) from depressions of major depressive disorder (MDD-Dep), and for more targeted treatments, are critically needed to decrease current high rates of misdiagnosis that can lead to ineffective or potentially deleterious treatments. Distinguishing, and specifically treating the depressions, during the adolescent/young adult epoch is especially important to decrease illness progression and improve prognosis, and suicide, as it is the epoch when suicide thoughts and behaviors often emerge. With differences in functional connectivity patterns reported when BD-Dep and MDD-Dep have been studied separately, this study used a graph theory approach aimed to identify functional connectivity differences in their direct comparison.

METHODS

Functional magnetic resonance imaging whole-brain functional connectivity (Intrinsic Connectivity Distribution, ICD) measures were compared across adolescents/young adults with BD-Dep (n = 28), MDD-Dep (n = 20) and HC (n = 111). Follow-up seed-based connectivity was conducted on regions of significant ICD differences. Relationships with demographic and clinical measures were assessed.

RESULTS

Compared to the HC group, both the BD-Dep and MDD-Dep groups exhibited left-sided frontal, insular, and medial temporal ICD increases. The BD-Dep group had additional right-sided ICD increases in frontal, basal ganglia, and fusiform areas. In seed-based analyses, the BD-Dep group exhibited increased interhemispheric functional connectivity between frontal areas not seen in the MDD-Dep group.

LIMITATIONS

Modest sample size; medications not studied systematically.

CONCLUSIONS

This study supports bilateral and interhemispheric functional dysconnectivity as features of BD-Dep that may differentiate it from MDD-Dep in adolescents/young adults and serve as a target for early diagnosis and treatment strategies.

Brain grey and white matter structural associations with future suicidal ideation and behaviors in adolescent and young adult females with mood disorders

Background

To reduce suicide in females with mood disorders, it is critical to understand brain substrates underlying their vulnerability to future suicidal ideation and behaviors (SIBs) in adolescence and young adulthood. In an international collaboration, grey and white matter structure was investigated in adolescent and young adult females with future suicidal behaviors (fSB) and ideation (fSI), and without SIBs (fnonSIB).

Methods

Structural (n = 91) and diffusion-weighted (n = 88) magnetic resonance imaging scans at baseline and SIB measures at follow-up on average two years later (standard deviation, SD = 1 year) were assessed in 92 females [age(SD) = 16.1(2.6) years] with bipolar disorder (BD, 28.3%) or major depressive disorder (MDD, 71.7%). One-way analyses of covariance comparing baseline regional grey matter cortical surface area, thickness, subcortical grey volumes, or white matter tensor-based fractional anisotropy across fSB (n = 40, 43.5%), fSI (n = 33, 35.9%) and fnonSIB (n = 19, 20.6%) groups were followed by pairwise comparisons in significant regions (p < 0.05).

Results

Compared to fnonSIBs, fSIs and fSBs showed significant decreases in cortical thickness of right inferior frontal gyrus pars orbitalis and middle temporal gyrus, fSIs of left inferior frontal gyrus, pars orbitalis. FSIs and fSBs showed lower fractional anisotropy in left uncinate fasciculus and corona radiata, and fSBs in right uncinate and superior fronto-occipital fasciculi.

Conclusions

The study provides preliminary evidence of grey and white matter alterations in brain regions subserving emotional and behavioral regulation and perceptual processing in adolescent and young adult females with mood disorders with, versus without, future SIBs. Findings suggest potential targets to prevent SIBs in female adolescents and young adults.

Going beyond risk factor: Childhood maltreatment and associated modifiable targets to improve life-long outcomes in mood disorders

Childhood maltreatment increases risk for mood disorders and is associated with earlier onset-and more pernicious disease course following onset-of mood disorders. While the majority of studies to date have been cross-sectional, longitudinal studies are emerging and support the devastating role(s) childhood maltreatment has on development of, and illness course in, mood disorders. This manuscript extends prior reviews to emphasize more recent work, highlighting longitudinal data, and discusses treatment studies that provide clues to mechanisms that mediate disease risk, course, relapse, and treatment response. Evidence suggesting systemic inflammation, alterations in hypothalamic-pituitary-adrenal (HPA) axis function and corticotropin-releasing factor (CRF) neural systems, genetic and other familial factors as mechanisms that mediate risk and onset of, and illness course in, mood disorders following childhood maltreatment is discussed. Risky behaviors following maltreatment, e.g., substance use and unhealthy lifestyles, may further exacerbate alterations in the HPA axis, CRF neural systems, and systematic inflammation to contribute to a more pernicious disease course. More research on sex differences and the impact of maltreatment in vulnerable populations is needed. Future research needs to be aimed at leveraging knowledge on modifiable targets, going beyond childhood maltreatment as a risk factor, to inform prevention and treatment strategies and foster trauma-informed care.

Magnetic resonance imaging features of hippocampus and mechanism of neurocognitive dysfunction for antiepileptic drugs in treatment of depression rats

To explore the effects of antiepileptic drug sodium valproate on magnetic resonance imaging (MRI) images, neurological cognition, and JAK1/STAT3 pathway in hippocampus of rats with depression, 30 Sprague Dawley (SD) rats were included. The depression model (DM) was prepared through the chronic stress restraint test. Some model rats were injected with 10 mg/kg sodium valproate into abdominal cavity before modeling (RT group)), and healthy rats were selected as controls (healthy control (HC) group). Depth of split brain was greatly increased in DM group, and nitrogen-acetyl aspartic acid (NAA)/creatine (Cr), glutamic acid (Glu)/Cr, and choline (Cho)/Cr ratios were greatly reduced (P < 0.05). Behavioral test results showed that sugar water preference rate, escape latency, and divergence index in DM group were greatly reduced (P < 0.05), and cumulative immobility time, target quadrant stay time, and number of crossings in forced swimming and tail suspension were prolonged dramatically (P < 0.05), with no difference between the two groups (P > 0.05). Expression levels of interleukin 1β (IL-1β) and interleukin 6 (IL-6) in hippocampus of DM group were obviously increased (P < 0.05), and expression levels of JAK1 and STAT3 were decreased visibly (P < 0.05), with no difference between the two (P > 0.05). In summary, anti-epileptic drug sodium valproate effectively improves hippocampal volume characteristics and memory and neurocognitive dysfunction of depression models.

Dynamical regional activity in putamen distinguishes bipolar type I depression and unipolar depression

OBJECTIVES

Intrinsic human brain activity is time-varying and dynamic. However, there is still a lack of knowledge about the dynamic regional activity differences between unipolar depression (UD) and bipolar type I depression (BD-I), and whether their differential pattern can help to distinguish these two patient groups who are prone to misdiagnosis in clinical practice.

METHOD

In this study, we used the dynamical fractional amplitude of low-frequency fluctuations (dfALFF) to examine the resting-state dynamical regional activity in 40 BD-I, 42 UD, and 44 healthy controls (HCs). Analysis of covariance was applied to explore the shared and distinct dfALFF pattern among three groups, and machine-learning methods were conducted to classify BD-I from UD by using the detected distinct dfALFF pattern.

RESULTS

Compared with HCs, both BD-I and UD exhibited decreased dfALFF temporal variability in the left inferior temporal gyrus. The BD-I showed significantly decreased dfALFF temporal variability in the left putamen compared to UD. By using the dfALFF variability pattern of the left putamen as features, we achieved the 75.61% accuracy and 0.756 area under curve in classifying BD-I from UD.

LIMITATIONS

The small sample size of the current study may limit the generalizability of the findings.

CONCLUSIONS

The current study demonstrated that the dfALFF temporal variability pattern in the putamen may show a promise as future diagnostic aids for BD-I and UD.

Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study®

Background

Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health.

Methods

We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of structural magnetic resonance imaging (cortical thickness, area, volume, and sulcal depth). We measured the performances of both multimodal and single modality classifiers.

Results

The multimodal classifiers showed outstanding performance compared with all five single modalities, and they are 100% for major depressive disorder versus healthy controls, 100% for bipolar disorder versus healthy control, 98.5% (95% CI: 95.4–100%) for major depressive disorder versus bipolar disorder, 100% for major depressive disorder versus depressed bipolar disorder and the leave-one-site-out analysis results are 77.4%, 63.3%, 79.4%, and 81.7%, separately.

Conclusions

The study shows that multimodal classifiers show high classification performances. Moreover, cuneus may be a potential biomarker to differentiate major depressive disorder, bipolar disorder, and healthy adolescents. Overall, this study can form multimodal diagnostic prediction workflows for clinically feasible to make more precise diagnose at the early stage and potentially reduce loss of personal pain and public society.

Differential Dynamical Pattern of Regional Homogeneity in Bipolar and Unipolar Depression: A Preliminary Resting-State fMRI Study

Background: Bipolar depression (BD) and unipolar depression (UD) are both characterized by depressive moods, which are difficult to distinguish in clinical practice. Human brain activity is time-varying and dynamic. Investigating dynamical pattern alterations of depressed brains can provide deep insights into the pathophysiological features of depression. This study aimed to explore similar and different abnormal dynamic patterns between BD and UD. Methods: Brain resting-state functional magnetic resonance imaging data were acquired from 36 patients with BD type I (BD-I), 38 patients with UD, and 42 healthy controls (HCs). Analysis of covariance was adopted to examine the differential pattern of the dynamical regional homogeneity (dReHo) temporal variability across 3 groups, with gender, age, and education level as covariates. Post-hoc analyses were employed to obtain the different dynamic characteristics between any 2 groups. We further applied the machine-learning methods to classify BD-I from UD by using the detected distinct dReHo pattern. Results: Compared with patients with UD, patients with BD-I demonstrated decreased dReHo variability in the right postcentral gyrus and right parahippocampal gyrus. By using the dReHo variability pattern of these two regions as features, we achieved the 91.89% accuracy and 0.92 area under curve in classifying BD-I from UD. Relative to HCs, patients with UD showed increased dReHo variability in the right postcentral gyrus, while there were no dReHo variability differences in patients with BD-I. Conclusions: The results of this study mainly report the differential dynamic pattern of the regional activity between BD-I and UD, particular in the mesolimbic system, and show its promising potential in assisting the diagnosis of these two depression groups.

Personality and psychopathology: In defense of a practical path toward integrating psychometric and biological approaches to advance a comprehensive model

Personality and psychopathology each reflect patterns of internal experience and outward behavior that differ between people and affect functioning. Drawing strict distinctions between the two concepts is not only difficult, but it may prove unnecessary for advancing an integrated model of psychological experiences associated with mental illness. We argue that developing such a model will be critical for improving treatment outcomes, and we discuss a practical path forward. Proponents of psychometric approaches to developing models of psychological experience focus on observable phenotypes and utilize statistical methods to describe patterns of covariation among a broad range of symptoms and dispositions. Advocates of biologically based approaches emphasize neuroscientific tools for identifying abnormalities in brain function that give rise to an individual's experience. There is substantial evidence that measures of personality and measures of symptoms capture nonoverlapping, clinically important information for understanding how and for whom treatments for mental illness work. In this article, we highlight the importance of combining psychometric and neurobiological approaches in order to understand which features of an individual those measures reflect, which aspects of neurobiology generate and maintain those features, how they relate to each other, and critically, how best to alter them to reduce distress and dysfunction.