Research progress of brain magnetic resonance imaging related to suicide in bipolar disorder patients

Bipolar disorder, as a major mental illness, has a high lifetime suicide attempt rate in patients, and suicidal behavior is most likely to occur during depressive episodes. Therefore, in-depth study of its mechanism is essential for prevention, early detection and intervention of suicide. With the development of magnetic resonance imaging (MRI) technology, it has been found that there are abnormalities in the brain structure and function in suicidal patients with bipolar disorder. This article reviews the studies on suicide in bipolar disorder patients by MRI from four aspects: structure, function, structure-function, and central metabolism and cerebral blood flow perfusion, and summarizes the suicide-related changes. This review focuses on distinguishing the brain MRI changes under different mood states and diverse definitions of suicide, aiming to provide reference for further exploration of the pathophysiological mechanism of suicide in bipolar disorder

Evaluation of circadian rhythms in depression by using actigraphy: a systematic review and meta-analysis

Objective·To systematically review the effectiveness of actigraphy on the evaluation of circadian rhythm characteristics in patients with depression.Methods·A systematic literature search was conducted in PubMed, Embase, Web of Science, Cochrane Library, PsycINFO, CNKI, WanFang Data, and Chinese biomedical literature database (CBM), from the inception of each database to May 5th, 2023. Case control studies that used actigraphy to evaluate circadian rhythms in patients with depression and compared them with healthy controls were collected. Literature was screened according to the inclusion and exclusion criteria, and the quality of the included literature was evaluated by using the Newcastle-Ottawa Scale. The meta-analysis was performed by using RevMan 5.4 software.Results·A total of 9 articles were included, including 390 patients with depression and 288 healthy controls. The meta-analysis showed that the MESOR (midline statistic of rhythm) (SMD=-0.29, 95% CI -0.51 ‒ -0.07, P=0.009) of the circadian cosine function in patients with depression was lower than that in healthy controls; sleep onset (MD=33.06, 95% CI 14.90 ‒ 51.23, P=0.000) and sleep offset (MD=53.80, 95% CI 22.38 ‒ 85.23, P=0.000) were later in patients with depression than those in healthy controls; no statistical difference was found in the activity level of the most active 10 hours (SMD=-0.26, 95% CI -0.52 ‒ 0.01, P=0.060) between patients with depression and healthy controls, although there was a trend for lower activity in patients with depression; no statistical difference was found in the acrophase (MD=25.33, 95% CI -12.41 ‒ 63.06, P=0.190) of the circadian cosine function between patients with depression and healthy controls; no clear statistical significance of the difference was found in the amplitude (SMD=-0.14, 95% CI -0.42 ‒ 0.14, P=0.340) and the activity level of the least active 5 hours (SMD=0.31, 95% CI -0.10 ‒ 0.71, P=0.140) between patients with depression and healthy controls.Conclusion·Actigraphy can reflect circadian rhythm disruption in patients with depression to some extent, but the limited number of included studies and inconsistencies in the study populations and methodologies have affected the quality and results of the analyses. More high-quality clinical trials are needed to provide evidence

Divergent and Convergent Imaging Markers Between Bipolar and Unipolar Depression Based on Machine Learning

Distinguishing bipolar depression (BD) from unipolar depression (UD) based on symptoms only is challenging. Brain functional connectivity (FC), especially dynamic FC, has emerged as a promising approach to identify possible imaging markers for differentiating BD from UD. However, most of such studies utilized conventional FC and group-level statistical comparisons, which may not be sensitive enough to quantify subtle changes in the FC dynamics between BD and UD. In this paper, we present a more effective individualized differentiation model based on machine learning and the whole-brain "high-order functional connectivity (HOFC)" network. The HOFC, capturing temporal synchronization among the dynamic FC time series, a more complex "chronnectome" metric compared to the conventional FC, was used to classify 52 BD, 73 UD, and 76 healthycontrols (HC). We achieved a satisfactory accuracy (70.40%) in BD vs. UD differentiation. The resultant contributing features revealed the involvement of the coordinated flexible interactions among sensory (e.g., olfaction, vision, and audition), motor, and cognitive systems. Despite sharing common chronnectome of cognitive and affective impairments, BD and UD also demonstrated unique dynamic FC synchronization patterns. UD is more associated with abnormal visual-somatomotor inter-network connections, while BD is more related to impaired ventral attention-frontoparietal inter-network connections. Moreover, we found that the illness duration modulated the BD vs. UD separation, with the differentiation performance hampered by the secondary disease effects. Our findings suggest that BD and UD may have divergent and convergent neural substrates, which further expand our knowledge of the two different mental disorders

Altered brain network modules induce helplessness in major depressive disorder

The abnormal brain functional connectivity (FC) has been assumed to be a pathophysiological aspect of major depressive disorder (MDD). However, it is poorly understood, regarding the underlying patterns of global FC network and their relationships with the clinical characteristics of MDD

Aberrant neural activity in patients with bipolar depressive disorder distinguishing to the unipolar depressive disorder: a resting-state functional magnetic resonance imaging study

This study aims to explore the intrinsic patterns of spontaneous activity of bipolar depression (BD) patients by analyzing the fractional amplitude of low frequency fluctuation (fALFF) that help differentiate BD from unipolar depressive disorder(UD). Twenty eight patients with BD, 47 patients with UD and 29 healthy controls were enrolled to receive the resting-state functional magnetic resonance imaging (rs-fMRI) scans. The group differences of fALFF values were calculated among three groups. In addition, the correlations between the clinical variables and mfALFF values were estimated. The brain regions with activation discrepancies among three groups are located in precuneus, the left middle temporal gyrus (MTG) and left inferior parietal lobe (IPL) and lingual gyrus. Compared with HC group, BD group shows decreased fALFF in precuneus, the left IPL and increased fALFF in lingual gyrus remarkably; UD group shows significantly decreased fALFF in precuneus, the left MTG and the left IPL. On the contrast of patients with UD, patients with BD have significantly increased fALFF value in the left precuneus, the left MGT and lingual gyrus. Furthermore, a negative correlation is found between the mfALFF values in precuneus and the scores of cognitive impairment factor in the UD group. The similar pattern of intrinsic activity in PCC suggests depressive state-dependent change. The aberrant patterns of intrinsic activity in precuneus, the IPL and lingual gyrus might be provide quantitative nodes that help to conduct further study for better distinguishing between BD and UD

The Metabolic Factor Kynurenic Acid of Kynurenine Pathway Predicts Major Depressive Disorder

Background: Metabolic factors in the kynurenine pathway (KP) have been widely accepted as being a major mechanism in Major Depressive Disorder (MDD). However, the effects of these metabolites on the degree and pattern of MDD are still poorly understood, partly due to the elusiveness of the level of metabolites when diagnosing depression. This study aimed to explore a novel diagnostic method analyzing peripheral blood with mass spectrometry to assess metabolites from KP in patients with MDD and Bipolar Depression (BD).Methods: Thirty-three patients with MDD, 20 patients with BD, and 23 healthy control participants were enrolled Metabolic factors of KP from plasma including tryptophan (TRP), kynurenine (KYN), kynurenic acid (KYNA), and quinolinic acid (QUIN) were analyzed by UPLC-3Q-MS, and levels compared across three groups. Correlation between HAMD scores and metabolite levels conducted. Receiver operating characteristic (ROC) curve was used to determine the diagnostic value of metabolic factors in MDD.Results: Levels of KYNA, QUIN, KYNA/QUIN, and KYNA/KYN were statistically different across the three groups (P < 0.05); HAMD scores and TRP, KYN, KYNA/QUIN levels were negatively correlated in the MDD group (r = −0.633, −0.477, −0.418, P < 0.05); Accuracy of KYNA diagnosing MDD was 82.5% with the optimal diagnostic value being 15.48 ng/ml. Diagnostic accuracy was increased to 83.6% when KYNA and QUIN levels were used in combination.Conclusion: This results indicate that metabolic factors of KP play a crucial role in the occurrence and development of MDD, supporting the metabolic imbalance hypothesis of MDD. Furthermore, our study also provides a new diagnostic method to study MDD based on plasma KYNA level, and suggests that KYNA would be a potential biomarker in diagnosing depression patients

Correction: Surface Vulnerability of Cerebral Cortex to Major Depressive Disorder

Major depressive disorder (MDD) is accompanied by atypical brain structure. This study first presents the alterations in the cortical surface of patients with MDD using multidimensional structural patterns that reflect different neurodevelopment. Sixteen first-episode, untreated patients with MDD and 16 matched healthy controls underwent a magnetic resonance imaging (MRI) scan. The cortical maps of thickness, surface area, and gyrification were examined using the surface-based morphometry (SBM) approach. Increase of cortical thickness was observed in the right posterior cingulate region and the parietal cortex involving the bilateral inferior, left superior parietal and right paracentral regions, while decreased thickness was noted in the parietal cortex including bilateral pars opercularis and left precentral region, as well as the left rostral-middle frontal regions in patients with MDD. Likewise, increased or decreased surface area was found in five sub-regions of the cingulate gyrus, parietal and frontal cortices (e.g., bilateral inferior parietal and superior frontal regions). In addition, MDD patients exhibited a significant hypergyrification in the right precentral and supramarginal region. This integrated structural assessment of cortical surface suggests that MDD patients have cortical alterations of the frontal, parietal and cingulate regions, indicating a vulnerability to MDD during earlier neurodevelopmental process