Cognitive-behavioral therapy for patients with somatoform disorders: A pilot preliminary randomized controlled trial.

Background and objective: Cognitive-behavioral therapy (CBT) for somatoform disorders (SFDs) is understudied in China. Western findings may not be applicable to Chinese culture. This preliminary study evaluated the efficacy of CBT for patients in China, relative to treatment-as-usual (TAU). Methods: Seventy patients with SFDs randomly received either combined CBT and TAU (CBT + TAU), or TAU alone between January 2018 to May 2019. The CBT + TAU group received 12 weekly individual 50-minute CBT sessions. Participants were blindly assessed at 4 timepoints (baseline, week 6, end of treatment: week 12; 12 weeks post-treatment: week 24) using the following outcome measures: SQSS (Self-screening Questionnaire for Somatic Symptoms); PHQ-15 (Patient-Health-Questionnaire-15) and the WI (Whiteley Index); GAD-7 (General Anxiety Disorder-7); HAMD-17 (Hamilton Depression Rating Scale-17); Family Burden Interview Schedule (FBIS); Sheehan Disability Scale (SDS); and the Short Form of Quality-of-Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q-SF). The primary endpoint was the difference between the SQSS total score at week 24 and the baseline. A mixed model for repeated measures was used to analyze inter- and intra-group changes from the baseline. Results: At week 24, The least-squares mean (LSM) change of the total score on the SQSS was -18.87 points and -9.69 points, respectively in the CBT + TAU group and in the TAU group (LSM difference, -9.18 points; 95% confidence interval, -15.72 to -2.64; P = 0.0068). At week 24, the LSM changes from baseline in the WI, HAMD, PHQ15, FBIS and SDS total scores were significantly different between the two groups, however, there was no significant difference in the Q-LES-Q-SF. The SQSS of group effect sizes were 0.63 at 24 weeks. The dropout rates of the CBT + TAU and TAU groups were comparable (22.9% and 19.3%). Conclusions: These preliminary findings suggest that CBT may be helpful for improving the symptoms of patients with SFDs in China.

Functional and structural MRI based obsessive-compulsive disorder diagnosis using machine learning methods.

BACKGROUND: The success of neuroimaging in revealing neural correlates of obsessive-compulsive disorder (OCD) has raised hopes of using magnetic resonance imaging (MRI) indices to discriminate patients with OCD and the healthy. The aim of this study was to explore MRI based OCD diagnosis using machine learning methods. METHODS: Fifty patients with OCD and fifty healthy subjects were allocated into training and testing set by eight to two. Functional MRI (fMRI) indices, including amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and structural MRI (sMRI) indices, including volume of gray matter, cortical thickness and sulcal depth, were extracted in each brain region as features. The features were reduced using least absolute shrinkage and selection operator regression on training set. Diagnosis models based on single MRI index / combined MRI indices were established on training set using support vector machine (SVM), logistic regression and random forest, and validated on testing set. RESULTS: SVM model based on combined fMRI indices, including ALFF, fALFF, ReHo and DC, achieved the optimal performance, with a cross-validation accuracy of 94%; on testing set, the area under the receiver operating characteristic curve was 0.90 and the validation accuracy was 85%. The selected features were located both within and outside the cortico-striato-thalamo-cortical (CSTC) circuit of OCD. Models based on single MRI index / combined fMRI and sMRI indices underperformed on the classification, with a largest validation accuracy of 75% from SVM model of ALFF on testing set. CONCLUSION: SVM model of combined fMRI indices has the greatest potential to discriminate patients with OCD and the healthy, suggesting a complementary effect of fMRI indices on the classification; the features were located within and outside the CSTC circuit, indicating an importance of including various brain regions in the model.

Analytical method for calculating internal stray radiation from an IR spectrometer based on the view factor.

Optomechanical components such as the lens barrels and frames of IR spectrometers produce strong internal stray radiation, which reduces the instrument's SNR and dynamic range. An IR internal stray radiation calculation method based on an analytical model of the view factor is proposed. The mathematical model of the view factor calculation method of typical optomechanical components is established. For any IR optical systems, the internal stray radiation can be quickly and accurately calculated by adjusting the coordinate systems in the calculation method. Based on the proposed method, the internal stray radiation of a double-pass long-wave IR spectrometer was calculated. The calculation results are consistent with the simulation results. The RMS value of the relative error between the calculated value and the simulated value is around 11%. To verify the proposed method, an experiment was conducted to test the internal stray radiation of the long-wave IR spectrometer. The internal stray radiation test results agree with the calculated and simulated results, and the relative error between the test results and the calculation results is within 9%.

Predicting responses to cognitive behavioral therapy in obsessive-compulsive disorder based on multilevel indices of rs-fMRI.

OBJECTIVE: This study aimed to identify neuroimaging predictors to predict the response of cognitive behavioral therapy (CBT) in patients with obsessive-compulsive disorder (OCD) based on indices of resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: Fifty patients with OCD were enrolled and allocated to either high or low responder groups after CBT using a 50 % response rate as the delineator. The pre-treatment amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC) in each cerebrum region, defined by automated anatomical labeling atlas, were extracted. Least absolute shrinkage and selection operator and logistic regression were used to select features and establish models. RESULTS: The combination of multilevel rs-fMRI indices achieved the best performance, with a cross-validation area under the receiver operating characteristic curve (AUC) of 0.900. In this combined model, an increase of interquartile range (IQR) in fALFF of right inferior orbital frontal gyrus (IOFG), and ReHo of left hippocampus and superior occipital gyrus (SOG) corresponded to a 26.52 %, 38.67 % and 24.38 % increase in the possibility to be high responders of CBT, respectively. ALFF of left thalamus and ReHo of left putamen were negatively associated with the response to CBT, with a 14.30 % and 19.91 % decrease per IQR increase of the index value. CONCLUSION: The combination of ALFF, fALFF and ReHo achieved a better predictive performance than separate index. Pre-treatment ALFF of the left thalamus, fALFF of the right IOFG, ReHo of the left hippocampus, SOG and putamen can be used as predictors of CBT response.

Comprehensive Cortical Structural Features Predict the Efficacy of Cognitive Behavioral Therapy in Obsessive-Compulsive Disorder.

Although cognitive behavioral therapy (CBT) is effective for patients with obsessive-compulsive disorder (OCD), 40% of OCD patients show a poor response to CBT. This study aimed to identify the cortical structural factors that predict CBT outcomes in OCD patients. A total of 56 patients with OCD received baseline structural MRI (sMRI) scanning and 14 individual CBT sessions. The linear support vector regression (SVR) models were used to identify the predictive performance of sMRI indices, including gray matter volume, cortical thickness, sulcal depth, and gyrification value. The patients' OC symptoms decreased significantly after CBT intervention (p < 0.001). We found the model with the comprehensive variables exhibited better performance than the models with single structural indices (MAE = 0.14, MSE = 0.03, R2 = 0.36), showing a significant correlation between the true value and the predicted value (r = 0.63, p < 0.001). The results indicated that a model integrating four cortical structural features can accurately predict the effectiveness of CBT for OCD. Future models incorporating other brain indicators, including brain functional indicators, EEG indicators, neurotransmitters, etc., which might be more accurate for predicting the effectiveness of CBT for OCD, are needed.

Using eye movements in the dot-probe paradigm to investigate attention bias in illness anxiety disorder.

BACKGROUND: Illness anxiety disorder (IAD) is a common, distressing, and debilitating condition with the key feature being a persistent conviction of the possibility of having one or more serious or progressive physical disorders. Because eye movements are guided by visual-spatial attention, eye-tracking technology is a comparatively direct, continuous measure of attention direction and speed when stimuli are oriented. Researchers have tried to identify selective visual attention biases by tracking eye movements within dot-probe paradigms because dot-probe paradigm can distinguish these attentional biases more clearly. AIM: To examine the association between IAD and biased processing of illness-related information. METHODS: A case-control study design was used to record eye movements of individuals with IAD and healthy controls while participants viewed a set of pictures from four categories (illness-related, socially threatening, positive, and neutral images). Biases in initial orienting were assessed from the location of the initial shift in gaze, and biases in the maintenance of attention were assessed from the duration of gaze that was initially fixated on the picture per image category. RESULTS: The eye movement of the participants in the IAD group was characterized by an avoidance bias in initial orienting to illness-related pictures. There was no evidence of individuals with IAD spending significantly more time viewing illness-related images compared with other images. Patients with IAD had an attention bias at the early stage and overall attentional avoidance. In addition, this study found that patients with significant anxiety symptoms showed attention bias in the late stages of attention processing. CONCLUSION: Illness-related information processing biases appear to be a robust feature of IAD and may have an important role in explaining the etiology and maintenance of the disorder.

A simple and effective coumarin-based fluorescent probe for cysteine.

Acrylic acid 3-acetyl-2-oxo-2 H-chromen-7-yl ester (ACA) was rationally designed and synthesized as a simple and effective fluorescent probe for sensing cysteine with high selectivity and naked-eye detection. The probe can detect cysteine by fluorescence spectrometry with a detection limit of 0.657 µM and can be used with calf serum and in live cell imaging. The conjugate addition/cyclization sequence mechanism of the reaction between ACA and cysteine was confirmed by ESI-MS and fluorescence spectra.

[Polarization-sensitive characteristics of the transmission spectra in photonic crystal with nematic liquid crystal defects].

The polarization-sensitive characteristics in the transmission spectra of TiO2/SiO2 optical multilayer films of one-dimensional photonic crystal (1D PC) with nematic liquid crystal defects were investigated in the present paper. The transmission spectra measurements and simulated results show that the polarization-sensitive feature was obvious when natural light was normal incident onto the parallelly aligned nematic liquid crystal. There were peaks of the extraordinary light (TE mode) with center wavelengths 1831 and 1800 nm and the ordinary light (TM mode) with center wavelengths 1452 and 1418 nm in the photonic forbidden band, respectively. With applied voltage increasing, the peaks of the extraordinary light was blue-shifted, and coincided with the peaks of O light gradually. Their tunable ranges were about 31 and 34 nm, respectively. For the random nematic liquid crystal, polarization sensitivity was not observed. Meanwhile, an individual extraordinary light peak with center wavelength 1801 nm and an individual ordinary light peak with center wavelength 1391 nm were obtained in the photonic forbidden band, respectively. The peaks were also found blue-shifted with applied voltage increasing, and their tunable ranges were about 64 and 15 nm, respectively. The polarization insensitive photonic crystal with nematic liquid crystal defects can be achieved by random liquid crystal molecules, which make the effective refractive index of the extraordinary light equal to that of the ordinary light.