Response to Commentaries on Article: "Differences in resting-state functional connectivity between depressed bipolar and major depressive disorder patients: A machine learning study".

Differences in resting-state functional connectivity between depressed bipolar and major depressive disorder patients: A machine learning study.

Reduced Glucocorticoid Receptor α Expression in Mood Disorder Patients and First-Degree Relatives

Bipolar Disorder: Imaging State Versus Trait

Letter to "Differences in resting-state functional connectivity between depressed bipolar and major depressive disorder patients: A machine learning study".

We investigated for the first time the proteomic profiles both in the dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC) of major depressive disorder (MDD) and bipolar disorder (BD) patients. Cryostat sections of DLPFC and ACC of MDD and BD patients with their respective well-matched controls were used for study. Proteins were quantified by tandem mass tag and high-performance liquid chromatography-mass spectrometry system. Gene Ontology terms and functional cluster alteration were analyzed through bioinformatic analysis. Over 3000 proteins were accurately quantified, with more than 100 protein expressions identified as significantly changed in these two brain areas of MDD and BD patients as compared to their respective controls. These include OGDH, SDHA and COX5B in the DLPFC in MDD patients; PFN1, HSP90AA1 and PDCD6IP in the ACC of MDD patients; DBN1, DBNL and MYH9 in the DLPFC in BD patients. Impressively, depending on brain area and distinct diseases, the most notable change we found in the DLPFC of MDD was ‘suppressed energy metabolism’; in the ACC of MDD it was ‘suppressed tissue remodeling and suppressed immune response’; and in the DLPFC of BD it was differentiated ‘suppressed tissue remodeling and suppressed neuronal projection’. In summary, there are distinct proteomic changes in different brain areas of the same mood disorder, and in the same brain area between MDD and BD patients, which strengthens the distinct pathogeneses and thus treatment targets.

A machine learning pipeline for efficient differentiation between bipolar and major depressive disorder based on multimodal structural neuroimaging

Resting-state functional brain connectivity in female adolescents with first-onset anorexia nervosa.

To test if onset age in major affective illnesses is younger in bipolar disorder (BPD) than unipolar-major depressive disorder (UP-MDD), and is a useful measure. We evaluated onset-age for DSM-IV-TR major illnesses in 3014 adults (18.5% BP-I, 12.5% BP-II, 69.0% UP-MDD; 64% women) at a mood-disorders center. Median and interquartile range (IQR) onset-age ranked: BP-I = 24 (19-32) < BP-II = 29 (20-40) < UP-MDD = 32 (23-47) years (P < 0.0001), and has remained stable since the 1970s. In BP-I patients, onset was latest for hypomania, and depression presented earlier than in BP-II or UP-MDD cases. Factors associated with younger onset included: i) being unmarried, ii) more education, iii) BPD-diagnosis, iv) family-history, v) being employed, vi) ever-suicidal, vii) substance-abuse and viii) ever-hospitalized. Onset-age distinguished BP-I from UP-MDD depressive onsets with weak sensitivity and specificity. Onset age was younger among BPD than MDD patients, and very early onset may distinguish BPD vs. UP-MDD with depressive-onset.

Background: Deficits in reward processing, such as approaching motivation, reward learning and effort-based decision-making, have been observed in patients with schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). However, little is known about the nature of reward-processing deficits in these 3 diagnostic groups. The present study aimed to compare and contrast amotivation in these 3 diagnostic groups using an effort-based decision-making task. Methods: Sixty patients (19 SCZ patients, 18 BD patients and 23 MDD patients) and 27 healthy controls (HC) were recruited for the present study. The Effort Expenditure for Reward Task (EEfRT) was administered to evaluate their effort allocation pattern. This task required participants to choose easy or hard tasks in response to different levels of reward magnitude and reward probability. Results: Results showed that SCZ, BD, and MDD patients chose fewer hard tasks compared to HC. As reward magnitude increased, MDD patients made the least effort to gain reward compared to the other groups. When reward probability was intermediate, MDD patients chose fewer hard tasks than SCZ patients, whereas BD patients and HC chose more hard tasks than MDD and SCZ patients. When the reward probability was high, all 3 groups of patients tried fewer hard tasks than HC. Moreover, SCZ and MDD patients were less likely to choose hard tasks than BD patients and HC in the intermediate estimated value conditions. However, in the highest estimated value condition, there was no group difference in hard task choices between these 3 clinical groups, and they were all less motivated than HC. Conclusion: SCZ, BD, and MDD patients shared common deficits in gaining reward if the reward probability and estimated value were high. SCZ and MDD patients showed less motivation than BD patients in gaining reward when the reward probability and estimated value was intermediate.

BackgroundWe analyzed the correlation of the clinical data with retinal nerve fiber layer (RNFL) thickness and macular thickness in bipolar disorder patients and major depression patients. The aim of this study is to explore factors that affect RNFL thickness in bipolar disorder patients and major depression patients, with a view to providing a new diagnostic strategy.MethodsEighty-two bipolar disorder patients, 35 major depression patients and 274 people who were age and gender matched with the patients were enrolled. Demographic information and metabolic profile of all participants were collected. Best-corrected visual acuity of each eye, intraocular pressure (IOP), fundus examination was performed. RNFL and macular thickness were measured by optical coherence tomography (OCT). Correlations between RNFL and macular thickness and other data were analyzed.ResultsRNFL and macula lutea in bipolar dipolar patients and major depression patients are thinner than normal people. Triglyceride and UA levels are the highest in the bipolar disorder group, while alanine aminotransferase (ALT) and glutamic oxalacetic transaminase (AST) levels in the depression group are the highest. Age onset and ALT are positively while uric acid (UA) is negatively correlated with RNFL thickness in bipolar dipolar patients. Cholesterol level is positively correlated with RNFL thickness while the duration of illness is correlated with RNFL thickness of left eye in major depression patients.ConclusionsRNFL and macula lutea in bipolar dipolar patients and major depression patients are thinner than normal people. In bipolar disorder patients, age-onset and ALT are potential protective factors in the progress of RNFL thinning, while UA is the pathological factor.

Changes in thalamic functional connectivity in post-Covid patients with and without fatigue

State-dependent changes in the expression levels of NCAM-140 and L1 in the peripheral blood cells of bipolar disorders, but not in the major depressive disorders

The mediating role of family functioning between childhood trauma and depression severity in major depressive disorder and bipolar disorder

Similarities of biochemical abnormalities between major depressive disorder and bipolar depression: A proton magnetic resonance spectroscopy study

Award for excellence in catalysis