Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data.

BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presentations. Despite extensive machine learning studies in psychiatric diagnosis, a reliable tool integrating multi-modality data is still lacking. METHODS: In this study, blood samples from 100 MDD and 100 HC were analyzed, along with MRI images from 46 MDD and 49 HC. Here, we devised a novel algorithm, integrating graph neural networks and attention modules, for MDD diagnosis based on inflammatory cytokines, neurotrophic factors, and Orexin A levels in the blood samples. Model performance was assessed via accuracy and F1 value in 3-fold cross-validation, comparing with 9 traditional algorithms. We then applied our algorithm to a dataset containing both the aforementioned protein quantifications and neuroimages, evaluating if integrating neuroimages into the model improves performance. RESULTS: Compared to HC, MDD showed significant alterations in plasma protein levels and gray matter volume revealed by MRI. Our new algorithm exhibited superior performance, achieving an F1 value and accuracy of 0.9436 and 94.08 %, respectively. Integration of neuroimaging data enhanced our novel algorithm's performance, resulting in an improved F1 value and accuracy, reaching 0.9543 and 95.06 %. LIMITATIONS: This single-center study with a small sample size requires future evaluations on a larger test set for improved reliability. CONCLUSIONS: In comparison to traditional machine learning models, our newly developed MDD diagnostic model exhibited superior performance and showed promising potential for inclusion in routine clinical diagnosis for MDD.

Comparisons of Accelerated Continuous and Intermittent Theta Burst Stimulation for Treatment-Resistant Depression and Suicidal Ideation.

BACKGROUND: Suicidal ideation is a substantial clinical challenge in treatment-resistant depression (TRD). Recent work demonstrated promising antidepressant effects in TRD patients with no or mild suicidal ideation using a specific protocol termed intermittent theta burst stimulation (iTBS). Here, we examined the clinical effects of accelerated schedules of iTBS and continuous TBS (cTBS) in patients with moderate to severe suicidal ideation. METHODS: Patients with TRD and moderate to severe suicidal ideation (n = 44) were randomly assigned to receive accelerated iTBS or cTBS treatment. Treatments were delivered in 10 daily TBS sessions (1800 pulses/session) for 5 consecutive days (total of 90,000 pulses). Neuronavigation was employed to target accelerated iTBS and cTBS to the left and right dorsolateral prefrontal cortex (DLPFC), respectively. Clinical outcomes were evaluated in a 4-week follow-up period. RESULTS: Accelerated cTBS was superior to iTBS in the management of suicidal ideation (pweek 1 = .027) and anxiety symptoms (pweek 1 = .01). Accelerated iTBS and cTBS were comparable in antidepressant effects (p < .001; accelerated cTBS: mean change at weeks 1, 3, 5 = 49.55%, 54.99%, 53.11%; accelerated iTBS: mean change at weeks 1, 3, 5 = 44.52%, 48.04%, 51.74%). No serious adverse events occurred during the trial. One patient withdrew due to hypomania. The most common adverse event was discomfort at the treatment site (22.73% in both groups). CONCLUSIONS: These findings provide the first evidence that accelerated schedules of left DLPFC iTBS and right DLPFC cTBS are comparably effective in managing antidepressant symptoms and indicate that right DLPFC cTBS is potentially superior in reducing suicidal ideation and anxiety symptoms.

[Spatial Prediction Modeling for Soil pH Based on Multiscale Geographical Weighted Regression(MGWR) and Its Influencing Factors].

Anhui, Henan, Jiangsu, and Shandong provinces were selected as the study area. A total of 599 soil samples and nine environmental factors of soil pH were collected. The spatial distribution of soil pH was modeled based on multi-scale geographically weighted regression(MGWR), mixed geographically weighted regression(Mixed GWR), geographically weighted regression(GWR), and multiple linear regression(MLR) models. Then, the spatial difference in the effect of environmental factors on soil pH was revealed using MGWR and quantile regression models. The results showed that:① soil pH showed significant global and local spatial autocorrelation at different spatial distances, and the clustering characteristics were obvious. ② The MGWR model was the best among the four models, and the Radj2 of MGWR, Mixed GWR, GWR, and MLR were 0.64, 0.62, 0.59, and 0.48, respectively. The residual of MGWR had the strongest independent distribution and the weakest spatial autocorrelation with a global Moran's I of 0.07. ③ Three types of GWR predictions showed that the spatial distribution of soil pH decreased gradually from north to south in the study area, with the highest in northern Henan and the lowest in southern Anhui. ④ MGWR modeling results showed that there was strong spatial heterogeneity of mean annual precipitation(MAP), multi-resolution valley bottom flatness(MRVBF), and elevation affecting soil pH. MAP had a stronger effect on soil pH in northern Jiangsu and most parts of Shandong. The positive effect of MRVBF on soil pH was stronger in northern Jiangsu and western Shandong. The negative effect of elevation on soil pH was stronger in northern and central Jiangsu. ⑤ The quantile regression analysis showed that the mean annual precipitation had a significant negative effect on soil pH at different quantile levels of soil pH, and influence intensity decreased with the increase in pH quantile level. MRVBF had a significant negative effect on soil pH at a low quantile level(θ=0.1 to 0.4) but had no significant effect on soil pH at a high quantile level(θ=0.5 to 0.9). These results can provide an important reference for mapping soil properties and analyzing its influence factors based on the MGWR model in large regions.

The roles of extracellular vesicles in major depressive disorder.

Major depressive disorder (MDD) is a serious mental disease characterized by depressed mood, loss of interest and suicidal ideation. Its rising prevalence has rendered MDD one of the largest contributors to the global disease burden. However, its pathophysiological mechanism is still unclear, and reliable biomarkers are lacking. Extracellular vesicles (EVs) are widely considered important mediators of intercellular communication, playing an important role in many physiological and pathological processes. Most preclinical studies focus on the related proteins and microRNAs in EVs, which can regulate energy metabolism, neurogenesis, neuro-inflammation and other pathophysiological processes in the development of MDD. The purpose of this review is to describe the current research progress of EVs in MDD and highlight their potential roles as biomarkers, therapeutic indicators and drug delivery carriers for the treatment of MDD.