Real-time motion analytics during brain MRI improve data quality and reduce costs

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more

Rapid Visual Statistical Learning in Distracting Environment

Mentor: Lori Markson From the Washington University Undergraduate Research Digest: WUURD, Volume 7, Issue 1, Fall 2011. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences; Kristin Sobotka, Editor

The Study of Autism and Social Cognition: A Neurological and Psychological Lens

From the Washington University Senior Honors Thesis Abstracts (WUSHTA), Volume 5, Spring 2013. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research / Assistant Dean in the College of Arts & Sciences; E. Holly Tasker, Editor; Kristin Sobotka, Undergraduate Research Coordinator. Mentor: Lori Markso

Callous-unemotional traits affect adolescents' perception of collaboration

BACKGROUND: How is the perception of collaboration influenced by individual characteristics, in particular high levels of callous-unemotional (CU) traits? CU traits are associated with low empathy and endorsement of negative social goals such as dominance and forced respect. Thus, it is possible that they could relate to difficulties in interpreting that others are collaborating based on a shared goal. METHODS: In the current study, a community sample of 15- to 16-year olds participated in an eye tracking task measuring whether they expect that others engaged in an action sequence are collaborating, depending on the emotion they display toward each other. Positive emotion would indicate that they share a goal, while negative emotion would indicate that they hold individual goals. RESULTS: When the actors showed positive emotion toward each other, expectations of collaboration varied with CU traits. The higher adolescents were on CU traits, the less likely they were to expect collaboration. When the actors showed negative emotion toward each other, CU traits did not influence expectations of collaboration. CONCLUSIONS: The findings suggest that CU traits are associated with difficulty in perceiving positive social interactions, which could further contribute to the behavioral and emotional problems common to those with high CU traits

The Role of Callous-Unemotional Traits on Adolescent Positive and Negative Emotional Reactivity : A Longitudinal Community-Based Study

Callous-unemotional (CU) traits are associated with lower emotional reactivity in adolescents. However, since previous studies have focused mainly on reactivity to negative stimuli, it is unclear whether reactivity to positive stimuli is also affected. Further, few studies have addressed the link between CU traits and emotional reactivity in longitudinal community samples, which is important for determining its generalizability and developmental course. In the current study, pupil dilation and self-ratings of arousal and valence were assessed in 100 adolescents (15-17 years) from a community sample, while viewing images with negative and positive valence from the International Affective Pictures System (ZAPS). Behavioral traits (CU) were assessed concurrently, as well as at ages 12-15, and 8-9 (subsample, n = 68, low levels of prosocial behavior were used as a proxy for CU traits). The results demonstrate that CU traits assessed at ages 12-15 and 8-9 predicted less pupil dilation to both positive and negative images at ages 15-17. Further, CU traits at ages 12-15 and concurrently were associated with less negative valence ratings for negative images and concurrently to less positive valence ratings for positive images. The current findings demonstrate that CU traits are related to lower emotional reactivity to both negative and positive stimuli in adolescents from a community sample

hMG addition affects the change in progesterone level during IVF stimulation and LBR: a retrospective cohort study

Abstract Background Premature progesterone (P) rise during IVF stimulation reduces endometrial receptivity and is associated with lower pregnancy rates following embryo transfer (ET), which can influence provider recommendation for fresh or frozen ET. This study aimed to determine whether change in P level between in IVF baseline and trigger (P) is predictive of pregnancy outcome following fresh ET, and whether the ratio of gonadotropins influences P rise and, as a result, clinical pregnancy outcomes: clinical pregnancy rate (CPR) and live birth rates (LBR). Methods Retrospective cohort study at a single fertility center at an academic institution. The peak P level and P were modeled in relation to prediction of CPR and LBR, and the ratios of hMG:rFSH were also modeled in relation to prediction of peak P level on day of trigger, P, and CPR/LBR in a total of 291 patients undergoing fresh embryo transfer after controlled ovarian hyperstimulation-IVF (COH-IVF). Results P correlates with CPR, with the most predictive range for success as P 0.7–0.85 ng/mL (p = 0.005, 95% CI 0.635, 3.636; predicting CPR of 88.9%). The optimal range for peak P in regard to pregnancy outcome was 0.15–1.349 ng/mL (p = 0.01; 95% CI for coefficient in model 0.48–3.570). A multivariable logistic model for prediction of CPR and LBR using either peak or P supported a stronger association between P and CPR/LBR as compared to peak P. Furthermore, an hMG:rFSH ratio of > 0.6 was predictive of lowest peak P (p = 0.010, 95% CI 0.035, 0.256) and smallest P (p = 0.012, 95% CI 0.030, 0.243) during COH-IVF cycles. Highest CPRs were observed within hMG:rFSH ratios of 0.3–0.4 [75.6% vs. 62.5% within and outside of the range, respectively, (p = 0.023, 95% CI 0.119, 1.618)]. Highest LBRs were seen within the range of 0.3–0.6 hMG:rFSH, [LBR of 55.4% vs. 41.4% (p = 0.010, 95% CI 0.176, 1.311)]. Conclusions Our data supports use of P to best predict pregnancy rates and therefore can improve clinical decision making as to when fresh ET is most appropriate. Furthermore, we found optimal gonadotropin ratios can be considered to minimize P rise and to optimize CPR/LBR, emphasizing the importance of luteinizing hormone (LH) activity in COH-IVF cycles

Real-time motion analytics during brain MRI improve data quality and reduce costs.

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more