Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
Remote Supergroup for Chemistry Undergraduates: An Inclusive Scientific Community for Primarily Undergraduate InstitutionsThe Remote Supergroup for Chemistry Undergraduates (RSCU) is a community of students and faculty from primarily undergraduate institutions that aims to (1) engage students in discussions of chemical research, (2) inform students of further educational and career pathways, (3) increase awareness and discourse of equity issues in science, and (4) foster scientific community across institutions. RSCU engaged participants in impactful virtual activities during the summer of 2020 when the COVID-19 pandemic precluded in-person undergraduate research experiences, and the program continued in 2021 as in-person research resumed. Results from self-reported surveys show that RSCU successfully achieved its aims both years, and both students and faculty research mentors benefited from participation. The diverse activities and scientific network cultivated by RSCU complement undergraduate research experiences and could be adapted to other disciplines.
The Remote Supergroup for Chemistry Undergraduates (RSCU) brought together student and faculty scientists from 18 public and private institutions that primarily serve undergraduates, spanning 14 US states and one other country. RSCU’s goals included networking across institutions, promoting student understanding of the chemical literature, informing students about further educational and career opportunities, and facilitating discussions of equity and inclusion in science.
This study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps.
Task-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked t-statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject.
fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant ( p = 4.88×10–4to p = 0.042; one p = 0.062) increases in consensus t-statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in t-statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoisingmore »
LLR denoising affords robust increases in t-statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality.