12 research outputs found
A robust multivariate, non-parametric outlier identification method for scrubbing in fMRI
Functional magnetic resonance imaging (fMRI) data contain high levels of
noise and artifacts. To avoid contamination of downstream analyses, fMRI-based
studies must identify and remove these noise sources prior to statistical
analysis. One common approach is the "scrubbing" of fMRI volumes that are
thought to contain high levels of noise. However, existing scrubbing techniques
are based on ad hoc measures of signal change. We consider scrubbing via
outlier detection, where volumes containing artifacts are considered
multidimensional outliers. Robust multivariate outlier detection methods are
proposed using robust distances (RDs), which are related to the Mahalanobis
distance. These RDs have a known distribution when the data are i.i.d. normal,
and that distribution can be used to determine a threshold for outliers where
fMRI data violate these assumptions. Here, we develop a robust multivariate
outlier detection method that is applicable to non-normal data. The objective
is to obtain threshold values to flag outlying volumes based on their RDs. We
propose two threshold candidates that embark on the same two steps, but the
choice of which depends on a researcher's purpose. Our main steps are dimension
reduction and selection, robust univariate outlier imputation to get rid of the
effect of outliers on the distribution, and estimating an outlier threshold
based on the upper quantile of the RD distribution without outliers. The first
threshold candidate is an upper quantile of the empirical distribution of RDs
obtained from the imputed data. The second threshold candidate calculates the
upper quantile of the RD distribution that a nonparametric bootstrap uses to
account for uncertainty in the empirical quantile. We compare our proposed fMRI
scrubbing method to motion scrubbing, data-driven scrubbing, and restrictive
parametric multivariate outlier detection methods
Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation
In task fMRI analysis, OLS is typically used to estimate task-induced
activation in the brain. Since task fMRI residuals often exhibit temporal
autocorrelation, it is common practice to perform prewhitening prior to OLS to
satisfy the assumption of residual independence, equivalent to GLS. While
theoretically straightforward, a major challenge in prewhitening in fMRI is
accurately estimating the residual autocorrelation at each location of the
brain. Assuming a global autocorrelation model, as in several fMRI software
programs, may under- or over-whiten particular regions and fail to achieve
nominal false positive control across the brain. Faster multiband acquisitions
require more sophisticated models to capture autocorrelation, making
prewhitening more difficult. These issues are becoming more critical now
because of a trend towards subject-level analysis, where prewhitening has a
greater impact than in group-average analyses. In this article, we first
thoroughly examine the sources of residual autocorrelation in multiband task
fMRI. We find that residual autocorrelation varies spatially throughout the
cortex and is affected by the task, the acquisition method, modeling choices,
and individual differences. Second, we evaluate the ability of different
AR-based prewhitening strategies to effectively mitigate autocorrelation and
control false positives. We find that allowing the prewhitening filter to vary
spatially is the most important factor for successful prewhitening, even more
so than increasing AR model order. To overcome the computational challenge
associated with spatially variable prewhitening, we developed a computationally
efficient R implementation based on parallelization and fast C++ backend code.
This implementation is included in the open source R package BayesfMRI.Comment: 26 pages with 1 page of appendix, 11 figures with 1 figure of
supplementary figur
AI is a viable alternative to high throughput screening: a 318-target study
: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery
Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries
Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely