The Relationship Between Facial Expression and Cognitive Function in Patients With Depression

Objective: Considerable evidence has shown that facial expression recognition ability and cognitive function are impaired in patients with depression. We aimed to investigate the relationship between facial expression recognition and cognitive function in patients with depression.Methods: A total of 51 participants (i.e., 31 patients with depression and 20 healthy control subjects) underwent facial expression recognition tests, measuring anger, fear, disgust, sadness, happiness, and surprise. The Chinese version of the MATRICS Consensus Cognitive Battery (MCCB), which assesses seven cognitive domains, was used.Results: When compared with a control group, there were differences in the recognition of the expressions of sadness (p = 0.036), happiness (p = 0.041), and disgust (p = 0.030) in a depression group. In terms of cognitive function, the scores of patients with depression in the Trail Making Test (TMT; p < 0.001), symbol coding (p < 0.001), spatial span (p < 0.001), mazes (p = 0.007), the Brief Visuospatial Memory Test (BVMT; p = 0.001), category fluency (p = 0.029), and continuous performance test (p = 0.001) were lower than those of the control group, and the difference was statistically significant. The accuracy of sadness and disgust expression recognition in patients with depression was significantly positively correlated with cognitive function scores. The deficits in sadness expression recognition were significantly correlated with the TMT (p = 0.001, r = 0.561), symbol coding (p = 0.001, r = 0.596), maze (p = 0.015, r = 0.439), and the BVMT (p = 0.044, r = 0.370). The deficits in disgust expression recognition were significantly correlated with impairments in the TMT (p = 0.005, r = 0.501) and symbol coding (p = 0.001, r = 0.560).Conclusion: Since cognitive function is impaired in patients with depression, the ability to recognize negative facial expressions declines, which is mainly reflected in processing speed, reasoning, problem-solving, and memory

Simulating Study on Mechanical Properties of Rock Wool Board for Thermal Insulation on External Walls

Rock wool board (RWB) is widely used in construction of exterior insulation worldwide. Fiber diameter, solid volume fraction (SVF), and contact degree among the fibers significantly influence the RWB physical properties. Herein, the effects of these factors on the mechanical properties of RWB were investigated using the GeoDict software. First, the fiberization process resulted in a finer fiber diameter, and the SVF of RWB increased with decreasing pore sizes. In addition, both the fiber diameter and SVF significantly influenced the RWB shear strength. Furthermore, in compliance with the Chinese standards of compression, tensile, and shear strength, the SVF of RWB with a 10.5 μm fiber diameter did not exceed 4.72%, 4.04%, and 5.4%, respectively. The novel method proposed herein can be used for optimizing the RWB manufacturing process

Identifying the difference in time perception between major depressive disorder and bipolar depression through a temporal bisection task

Background It is difficult to make a precise diagnosis to distinguish patients with Major Depressive Disorder (MDD) from patients with Bipolar Depressive Disorder (current depressive episode, BD). This study will explore the difference in time perception between MDD and BD using a temporal bisection task. Methods In this temporal bisection task, 30 MDD patients, 30 BD patients, and 30 healthy controls (HC) had to categorize a signal duration, between 400 and 1600 milliseconds (ms), as either short or long. A repeated measurement analysis of variance with 3 (subject type) × 7 (time interval) was performed on the long response ratio with Bonferroni correction for multiple comparisons. Origin software was used to calculate the subjective bisection point (BP), difference limen (DL), and Weber ratio (WR). The Hamilton Depression Rating Scale for depression-17 was used to assess depressive symptoms in the patients. Results The data showed that the interaction effect between subject type and duration was significant (F(6,498) = 4.656, p Limitations Severity of depression not divided and analyzed according to the Hamilton Depression Rating Scale score. Conclusion The time perception of the MDD and BD groups was different from that of the HC group, they overestimated short time periods. Compared with the BD group, the MDD group had a smaller time bisector, and these patients felt that time passed more slowly. The time sensitivity of MDD group and BD group were less than the HC group. However, there was no statistical difference in time sensitivity between the MDD and BD groups

Facial Expression Recognition and ReHo Analysis in Major Depressive Disorder

Objective: To explore the characteristics of expression recognition and spontaneous activity of the resting state brain in major depressive disorder (MDD) patients to find the neural basis of expression recognition and emotional processing. &nbsp; Methods: In this study, two of the six facial expressions (happiness, sadness, anger, fear, aversion, and surprise) were presented in quick succession using a short expression recognition test. The differences in facial expression recognition between MDD patients and healthy people were compared. Further, the differences in ReHo values between the two groups were compared using a resting-state functional magnetic resonance imaging (fMRI) scan to investigate the characteristics of spontaneous brain activity in the resting state and its relationship with clinical symptoms and the accuracy of facial expression recognition in patients with MDD. &nbsp; Results: (1) The accuracy of facial expression recognition in patients with MDD was lower than that of the HC group. There were differences in facial expression recognition between the two groups in sadness-anger (p = 0.026), surprise-aversion (p = 0.038), surprise-happiness (p = 0.014), surprise-sadness (p = 0.019), fear-happiness (p = 0.027), and fear-anger (p = 0.009). The reaction time for facial expression recognition in the patient group was significantly longer than that of the HC group. (2) Compared with the HC group, the ReHo values decreased in the left parahippocampal gyrus, left thalamus, right putamen, left putamen, and right angular gyrus, and increased in the left superior frontal gyrus, left middle temporal gyrus, left medial superior frontal gyrus, and right medial superior frontal gyrus in the patient group. (3) Spearman correlation analysis showed no statistical correlation between ReHo and HAMD-17 scores in MDD patients (p &gt; 0.05). The ReHo value of the left putamen was negatively correlated with the recognition of fear-surprise (r = -0.429, p = 0.016), the ReHo value of the right angular gyrus was positively correlated with the recognition of sadness-anger (r = 0.367, p = 0.042), and the ReHo value of the right medial superior frontal gyrus was negatively correlated with the recognition of fear-anger (r = -0.377, p = 0.037). &nbsp; Conclusion: In view of the different performance of patients with MDD in facial expression tasks, facial expression recognition may have some suggestive effect on the diagnosis of depression and has clinical guiding significance. Many brain regions, including the frontal lobe, temporal lobe, striatum, hippocampus, and thalamus, in patients with MDD show extensive ReHo abnormalities in the resting state. These brain regions with abnormal spontaneous neural activity are important components of LCSPT and LTC circuits, and their dysfunctional functions cause disorder of emotion regulation. The changes in spontaneous activity in the left putamen, right angular gyrus, and right medial superior frontal gyrus may represent the abnormal pattern of spontaneous brain activity in the neural circuits related to emotion perception and may be the neural basis of facial expression recognition. &nbsp;</p

Proportion of long response in MDD group, BD group and HC group under seven durations.

MDD, major depressive disorder; BD, bipolar disorder with depressive period; HC, healthy control group; P(LONG), proportion of long responses.</p

General demographic profile of enrolled participants.

General demographic profile of enrolled participants.</p

Paradigm of a trial in the test phase.

In the test phases, participants were instructed to judge a series of time intervals as “short” or “long” based on the previously learned time interval.</p

The BP, the DL and WR for three groups.

MDD: the group of major depressive disorder; BD: the group of bipolar disorder with depressive period; HC: the group of health controls; BP: the subjective bisection point; DL: difference limen; WR: Weber ratio; * p< 0.05.</p

Proportion of long responses across the three groups plotted in the bisection task (ms, ± S).

Proportion of long responses across the three groups plotted in the bisection task (ms, ± S).</p