The validity and reliability of the PHQ-9 on screening of depression in neurology: a cross sectional study.

BACKGROUND: This study aimed to explore the validity and reliability of the Patient Health Questionnaire-9 (PHQ-9) on screening of depression among patients with neurological disorders, and to explore factors influencing such patients. METHODS: In this study, 277 subjects who were admitted to the department of neurology of our hospital due to different neurological disorders completed the PHQ-9 questionnaire. The Mini-International Neuropsychiatric Interview (MINI) and Hamilton Rating Scale for Depression (HAMD) were employed to evaluate the depressive symptoms of patients who completed the PHQ-9 questionnaire. The internal consistency, criterion validity, structural validity, and optimal cut-off values of PHQ-9 were evaluated, and the consistency assessment was conducted between the depression severity as assessed by PHQ-9, HAMD and MINI. Logistic regression analysis was used to calculate the risk factors of depression. RESULTS: The Cronbach's α coefficient of the PHQ-9 was 0.839. The Pearson's correlation coefficient among the 9 items of the PHQ-9 scale was 0.160 ~ 0.578 (P < 0.01), and the Pearson's correlation coefficient between each item and the total score was at the range of 0.608 ~ 0.773. Taking the results of MINI as the gold standard, the area under the receiver operating characteristic (ROC) curve of the PHQ-9 results for all the subjects (n = 277) was 0.898 (95% confidence interval (CI): 0.859 ~ 0.937, P < 0.01). When the cut-off score was equal to 5, the values of sensitivity, specificity, and the Youden's index were 91.2, 76.6%, and 0.678, respectively. Multivariate logistic regression analysis showed that the influence of unemployment on the occurrence of depression was statistically significant (P = 0.027, OR = 3.080, 95%CI: 1.133 ~ 8.374). CONCLUSIONS: The application of PHQ-9 for screening of depression among Chinese patients with neurological disorders showed a good reliability and validity.

Somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and its neuroimaging mechanisms.

BACKGROUND: Subclinical anxiety, depressive and somatic symptoms appear closely related. However, it remains unclear whether somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and what the underlying neuroimaging mechanisms are for the mediating effect. METHODS: Data of healthy participants (n = 466) and participants in remission of major depressive disorder (n = 53) were obtained from the Human Connectome Project. The Achenbach Adult Self-Report was adopted to assess anxiety, depressive and somatic symptoms. All participants completed four runs of resting-state functional magnetic resonance imaging. Mediation analyses were utilized to explore the interactions among these symptoms and their neuroimaging mechanisms. RESULTS: Somatic symptoms partially mediated the association between subclinical anxiety and depressive symptoms in healthy participants (anxiety→somatic→depression: effect: 0.2785, Boot 95% CI: 0.0958-0.3729; depression→somatic→anxiety: effect: 0.0753, Boot 95% CI: 0.0232-0.1314) and participants in remission of MDD (anxiety→somatic→depression: effect: 0.2948, Boot 95% CI: 0.0357-0.7382; depression→somatic→anxiety: effect: 0.0984, Boot 95% CI: 0.0007-0.2438). Resting-state functional connectivity (FC) between the right medial superior frontal gyrus and the left thalamus and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in healthy participants (effect: 0.0020, Boot 95% CI: 0.0003-0.0043). The mean strength of common FCs of subclinical depressive and somatic symptoms, somatic symptoms, and the mean strength of common FCs of subclinical anxiety and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in remission of MDD (effect: 0.0437, Boot 95% CI: 0.0024-0.1190). These common FCs mainly involved the insula, precentral gyri, postcentral gyri and cingulate gyri. Furthermore, FC between the triangular part of the left inferior frontal gyrus and the left postcentral gyrus was positively associated with subclinical anxiety, depressive and somatic symptoms in remission of MDD (FDR-corrected p < 0.01). CONCLUSIONS: Somatic symptoms partially mediate the interaction between subclinical anxiety and depressive symptoms. FCs involving the right medial superior frontal gyrus, left thalamus, triangular part of left inferior frontal gyrus, bilateral insula, precentral gyri, postcentral gyri and cingulate gyri maybe underlie the mediating effect of somatic symptoms.

Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle.

Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.

Pose Prediction of Autonomous Full Tracked Vehicle Based on 3D Sensor.

Autonomous vehicles can obtain real-time road information using 3D sensors. With road information, vehicles avoid obstacles through real-time path planning to improve their safety and stability. However, most of the research on driverless vehicles have been carried out on urban even driveways, with little consideration of uneven terrain. For an autonomous full tracked vehicle (FTV), the uneven terrain has a great impact on the stability and safety. In this paper, we proposed a method to predict the pose of the FTV based on accurate road elevation information obtained by 3D sensors. If we could predict the pose of the FTV traveling on uneven terrain, we would not only control the active suspension system but also change the driving trajectory to improve the safety and stability. In the first, 3D laser scanners were used to get real-time cloud data points of the terrain for extracting the elevation information of the terrain. Inertial measurement units (IMUs) and GPS are essential to get accurate attitude angle and position information. Then, the dynamics model of the FTV was established to calculate the vehicle's pose. Finally, the Kalman filter was used to improve the accuracy of the predicted pose. Compared to the traditional method of driverless vehicles, the proposed approach was more suitable for autonomous FTV. The real-world experimental result demonstrated the accuracy and effectiveness of our approach.

Understanding the cognitive and neuroimaging bases underlying the detrimental impact of sleep deprivation on reciprocity.

Although the impact of sleep loss on social behaviors has been widely observed in recent years, the mechanisms underpinning these impacts remain unclear. In this study, we explored the detrimental effects of sleep deprivation on reciprocity behavior as well as its underlying psychological and neuroimaging mechanisms by combining sleep manipulation, an interpersonal interactive game, computational modeling and neuroimaging. Our results suggested that after sleep deprivation, individuals showed reduced reciprocity behavior, mainly due to their reduced weights on communal concern when making social decisions. At neural level, we demonstrated that sleep deprivation's effects were observed in the precuneus (hyperactivity) and temporoparietal junction, dorsal lateral prefrontal cortex (DLPFC) (both hypoactivity), and reduced reciprocity was also accounted for by increased precuneus-thalamus connectivity and DLPFC-thalamus connectivity. Our findings contributed to the understanding of the psychological and neuroimaging bases underlying the deleterious impact of sleep deprivation on social behaviors.