Muscle Synergies and Clinical Outcome Measures Describe Different Factors of Upper Limb Motor Function in Stroke Survivors Undergoing Rehabilitation in a Virtual Reality Environment

Recent studies have investigated muscle synergies as biomarkers for stroke, but it remains controversial if muscle synergies and clinical observation convey the same information on motor impairment. We aim to identify whether muscle synergies and clinical scales convey the same information or not. Post-stroke patients were administered an upper limb treatment. Before (T0) and after (T1) treatment, we assessed motor performance with clinical scales and motor output with EMG-derived muscle synergies. We implemented an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) to identify the underlying relationships among all variables, at T0 and T1, and a general linear regression model to infer any relationships between the similarity between the affected and unaffected synergies (Median-sp) and clinical outcomes at T0. Clinical variables improved with rehabilitation whereas muscle-synergy parameters did not show any significant change. EFA and CFA showed that clinical variables and muscle-synergy parameters (except Median-sp) were grouped into different factors. Regression model showed that Median-sp could be well predicted by clinical scales. The information underlying clinical scales and muscle synergies are therefore different. However, clinical scales well predicted the similarity between the affected and unaffected synergies. Our results may have implications on personalizing rehabilitation protocols.

Risk Assessment for Heroin Use and Craving Score Using Polygenic Risk Score

Addiction is characterized by drug-craving, compulsive drug-taking, and relapse, and results from the interaction between multiple genetic and environmental factors. Reward pathways play an important role in mediating drug-seeking and drug-taking behaviors, and relapse. The objective of this study was to identify heroin addicts who carry specific genetic variants in their dopaminergic reward systems. A total of 326 heroin-dependent patients undergoing methadone maintenance therapy (MMT) were recruited from the Addiction Center of the China Medical University Hospital. A heroin-use and craving questionnaire was used to evaluate the urge for heroin, the daily or weekly frequency of heroin usage, daily life disturbance, anxiety, and the ability to overcome heroin use. A general linear regression model was used to assess the associations of genetic polymorphisms in one’s dopaminergic reward system with heroin-use and craving scores. Results: The most significant results were obtained for rs2240158 in GRIN3B (p = 0.021), rs3983721 in GRIN3A (p = 0.00326), rs2129575 in TPH2 (p = 0.033), rs6583954 in CYP2C19 (p = 0.033), and rs174699 in COMT (p = 0.036). These were all associated with heroin-using and craving scores with and without adjustments for age, sex, and body mass index. We combined five variants, and the ensuing dose-response effect indicated that heroin-craving scores increased with the numbers of risk alleles (p for trend = 0.0008). These findings will likely help us to understand the genetic mechanism of craving, which will help in predicting the risk of relapse in clinical practice and the potential for therapies to target craving in heroin addiction.

Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model

The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter. The problems of the OLS estimator for linear regression analysis include that of multicollinearity and outliers, which lead to unfavourable results. This study proposed a two-parameter ridge-type modified M-estimator (RTMME) based on the M-estimator to deal with the combined problem resulting from multicollinearity and outliers. Through theoretical proofs, Monte Carlo simulation, and a numerical example, the proposed estimator outperforms the modified ridge-type estimator and some other considered existing estimators.

Impact of Occupation and Family Burden on Psychological Adjustment in Returning Migrants

Background: Recently, increasing returning migrants in China accompany the massive rural-urban migration, but little information on mental health is available. Methods: A cross-sectional survey was conducted in 2,100 households from seven provinces to examine the effect of return migration on mental health and its association with entrepreneurial experience, occupation, and family burden compared with local rural non-migrants. The 12-item General Health Questionnaire (GHQ-12) was used to measure mental health status, and factor scores were extracted through factor analysis to gauge three sub-domains of loss of confidence, social dysfunction, and anxiety. A general linear regression model was used to analyze the data for the association. Results: Returning migrants were more likely to have elevated levels of anxiety compared with rural non-migrants when adjusting for social and demographic variables. Entrepreneurial experiences reduced loss of confidence and social dysfunction but increased anxiety; started but not currently running a business, and having older adults at home to care seemed growing concern in returning migrants but not in the rural non-migrants. Conclusion: Our study supports the salmon bias effect, but that occupation, entrepreneurship, and family burden may have non-negligible impacts on the anxiety in returning migrants. The findings may have implications for promoting social integration for returning migrants.

Cognitive Correlates of Hippocampal Atrophy and Ventricular Enlargement in Adults with or without Mild Cognitive Impairment

We analyzed structural magnetic resonance imaging data from 58 cognitively normal and 101 mild cognitive impairment subjects. We used a general linear regression model to study the association between cognitive performance with hippocampal atrophy and ventricular enlargement using the radial distance method.Bilateral hippocampal atrophy was associated with baseline and longitudinal memory performance. Left hippocampal atrophy predicted longitudinal decline in visuospatial function. The multidomain ventricular analysis did not reveal any significant predictors.

Effects of Imagery as Visual Stimuli on the Physiological and Emotional Responses

Study of emotions has gained interest in the field of sensory and consumer research. Accurate information can be obtained by studying physiological behavior along with self-reported-responses. The aim was to identify physiological and self-reported-responses towards visual stimuli and predict self-reported-responses using biometrics. Panelists (N = 63) were exposed to 12 images (ten from Geneva Affective PicturE Database (GAPED), two based on common fears) and a questionnaire (Face scale and EsSense). Emotions from facial expressions (FaceReaderTM), heart rate (HR), systolic pressure (SP), diastolic pressure (DP), and skin temperature (ST) were analyzed. Multiple regression analysis was used to predict self-reported-responses based on biometrics. Results showed that physiological along with self-reported responses were able to separate images based on cluster analysis as positive, neutral, or negative according to GAPED classification. Emotional terms with high or low valence were predicted by a general linear regression model using biometrics, while calm, which is in the center of emotion dimensional model, was not predicted. After separating images, positive and neutral categories could predict all emotional terms, while negative predicted Happy, Sad, and Scared. Heart Rate predicted emotions in positive (R2 = 0.52 for Scared) and neutral (R2 = 0.55 for Sad) categories while ST in positive images (R2 = 0.55 for Sad, R2 = 0.45 for Calm).

Association of disease prognostic model (PM) with baseline quality of life (QOL) in metastatic castration resistant prostate cancer (mCRPC).

e16579 Background: PM’s in mCRPC accurately predict survival, but little is known about their association with QOL. We hypothesize that pre-treatment QOL is inversely correlated with PM derived scores. Our study explored associations between baseline Halabi PM derived risk score (including metastatic site, opioid use, ECOG performance status (ECOG PS), Alk phos, albumin, hemoglobin, LDH, and PSA) and multiple QOL domains at treatment initiation with docetaxel and prednisone (DP). Methods: The DP arm of MAINSAIL, a multicenter, randomized Phase 3 study of DP +/- lenalidomide in mCRPC, was analyzed via ProjectDataSphere. Halabi PM score was computed as continuous with higher score reflecting worse survival and classified as low- ( < 140 points), intermediate- (140-194.96) or high-risk groups ( > 194.96 points). QOL tests included FACT-P (higher score = better QOL), BPI-SF Severity score (BPI-SFSS; higher score = higher pain severity); and BPI-SF Interference score (BPI-SFIS, higher score = worse pain). General linear regression model was used to calculate beta estimates and 95% confidence intervals. Results: The sample included 526 mCPRC pts (median 68 years, White: 91.9%, Black: 5.3%, median PSA 75.95 ng/ml). Median [range] for FACT-P, BPI-SFSS and BPI-SFIS were 111 [0-152], 1.8 [0-9.3], and 1.4 [0-10], respectively. PM score was correlated with BPI-SFIS and BPI-SFSS and inversely correlated with FACT-P with correlation of 0.36, 0.31 and -0.33, respectively (all p < 0.0001). For each unit increase in the PM score, BPI-SFIS increased by 2.1 points), BPI-SFSS increased by 1.5 points, and FACT-P decreased by 16.4 points. Using the three-risk groups, pts in the intermediate and high-risk groups had worse FACT-P QOL and higher BPI-SFIS and BPI-SFSS than pts in the low-risk group. In multivariate analysis, factors negatively impacting FACT-P QOL, BPI-SFSS and BPI-SFIS were higher ECOG PS, visceral metastases and opioid use. Declining hemoglobin levels were associated with increased BPI-SFSS and BPS-SFIS. Conclusions: Higher PM scores are associated with a lower baseline QOL overall, higher pain and higher pain interference. These results should be validated prospectively.

Feasible Generalized Stein-Rule Restricted Ridge Regression Estimators

Several versions of the Stein-rule estimators of the coefficient vector in a linear regression model are proposed in the literature. In the present paper, we propose new feasible generalized Stein-rule restricted ridge regression estimators to examine multicollinearity and autocorrelation problems simultaneously for the general linear regression model, when certain additional exact restrictions are placed on these coefficients. Moreover, a Monte Carlo simulation experiment is performed to investigate the performance of the proposed estimator over the others.