Complementary Roles of Systems Representing Sensory Evidence and Systems Detecting Task Difficulty During Perceptual Decision Making

Perceptual decision making is a multi-stage process where incoming sensory information is used to select one option from several alternatives. Researchers typically have adopted one of two conceptual frameworks to define the criteria for determining whether a brain region is involved in decision computations. One framework, building on single-unit recordings in monkeys, posits that activity in a region involved in decision making reflects the accumulation of evidence toward a decision threshold, thus showing the lowest level of BOLD signal during the hardest decisions. The other framework instead posits that activity in a decision-making region reflects the difficulty of a decision, thus showing the highest level of BOLD signal during the hardest decisions. We had subjects perform a face detection task on degraded face images while we simultaneously recorded BOLD activity. We searched for brain regions where changes in BOLD activity during this task supported either of these frameworks by calculating the correlation of BOLD activity with reaction time – a measure of task difficulty. We found that the right supplementary eye field, right frontal eye field, and right inferior frontal gyrus had increased activity relative to baseline that positively correlated with reaction time, while the left superior frontal sulcus and left middle temporal gyrus had decreased activity relative to baseline that negatively correlated with reaction time. We propose that a simple mechanism that scales a region's activity based on task demands can explain our results

Introducing alternative-based thresholding for defining functional regions of interest in fMRI

In fMRI research, one often aims to examine activation in specific functional regions of interest (fROIs). Current statistical methods tend to localize fROIs inconsistently, focusing on avoiding detection of false activation. Not missing true activation is however equally important in this context. In this study, we explored the potential of an alternative-based thresholding (ABT) procedure, where evidence against the null hypothesis of no effect and evidence against a prespecified alternative hypothesis is measured to control both false positives and false negatives directly. The procedure was validated in the context of localizer tasks on simulated brain images and using a real data set of 100 runs per subject. Voxels categorized as active with ABT can be confidently included in the definition of the fROI, while inactive voxels can be confidently excluded. Additionally, the ABT method complements classic null hypothesis significance testing with valuable information by making a distinction between voxels that show evidence against both the null and alternative and voxels for which the alternative hypothesis cannot be rejected despite lack of evidence against the null

Advances in clinical applications of cardiovascular magnetic resonance imaging

Cardiovascular magnetic resonance (CMR) is an evolving technology with growing indications within the clinical cardiology setting. This review article summarises the current clinical applications of CMR. The focus is on the use of CMR in the diagnosis of coronary artery disease with summaries of validation literature in CMR viability, myocardial perfusion, and dobutamine CMR. Practical uses of CMR in non-coronary diseases are also discussed

Detailed transonic flow field measurements about a supercritical airfoil section

The transonic flow field about a Whitcomb-type supercritical airfoil profile was measured in detail. In addition to the usual surface pressure distributions and wake surveys, schlieren photographs were taken and velocity vector profiles were determined in the upper surface boundary layer and in the near wake. Spanwise variations in the measured pressures were also determined. The data are analyzed with the aid of an inviscid transonic finite-difference computer program as well as with boundary layer modeling and calculation schemes

Effects of forward contour modification on the aerodynamic characteristics of the NACA 641-212 airfoil section

Two different forward contour modifications designed to increase the maximum lift coefficient of the NACA 64 sub 1-212 airfoil section were evaluated experimentally at low speeds. One modification consisted of a slight droop of the leading edge with an increased leading-edge radius; the other modification incorporated increased thickness over the forward 35 percent of the upper surface of the profile. Both modified airfoil sections were found to provide substantially higher maximum lift coefficients than the 64 sub 1-212 section. The drooped leading-edge modification incurred a drag penalty of approximately 10 percent at low and moderate lift coefficients and exhibited a greater nosedown pitching moment than the 64 sub 1-212 profile. The upper surface modification produced about the same drag level as the 64 sub 1-212 section at low and moderate lift coefficients and less nosedown pitching moment than the 64 sub 1-212 profile. Both modified airfoil sections had lower drag coefficients than the 64 sub 1-212 section at high lift coefficients

Knowing what you know in brain segmentation using Bayesian deep neural networks

In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out dataset (n = 418). The network was trained using a novel spike-and-slab dropout-based variational inference approach. We show that, on these datasets, the proposed Bayesian DNN outperforms previously proposed methods, in terms of the similarity between the segmentation predictions and the FreeSurfer labels, and the usefulness of the estimate uncertainty of these predictions. In particular, we demonstrated that the prediction uncertainty of this network at each voxel is a good indicator of whether the network has made an error and that the uncertainty across the whole brain can predict the manual quality control ratings of a scan. The proposed Bayesian DNN method should be applicable to any new network architecture for addressing the segmentation problem.Comment: Submitted to Frontiers in Neuroinformatic

Variance decomposition for single-subject task-based fMRI activity estimates across many sessions

AbstractHere we report an exploratory within-subject variance decomposition analysis conducted on a task-based fMRI dataset with an unusually large number of repeated measures (i.e., 500 trials in each of three different subjects) distributed across 100 functional scans and 9 to 10 different sessions. Within-subject variance was segregated into four primary components: variance across-sessions, variance across-runs within a session, variance across-blocks within a run, and residual measurement/modeling error. Our results reveal inhomogeneous and distinct spatial distributions of these variance components across significantly active voxels in grey matter. Measurement error is dominant across the whole brain. Detailed evaluation of the remaining three components shows that across-session variance is the second largest contributor to total variance in occipital cortex, while across-runs variance is the second dominant source for the rest of the brain. Network-specific analysis revealed that across-block variance contributes more to total variance in higher-order cognitive networks than in somatosensory cortex. Moreover, in some higher-order cognitive networks across-block variance can exceed across-session variance. These results help us better understand the temporal (i.e., across blocks, runs and sessions) and spatial distributions (i.e., across different networks) of within-subject natural variability in estimates of task responses in fMRI. They also suggest that different brain regions will show different natural levels of test-retest reliability even in the absence of residual artifacts and sufficiently high contrast-to-noise measurements. Further confirmation with a larger sample of subjects and other tasks is necessary to ensure generality of these results

Aortic pulse wave velocity in children with Cushing syndrome: A window into a marker of early cardiovascular disease.

ObjectiveTo investigate early signs of cardiovascular arterial remodelling in paediatric patients with Cushing syndrome (CS) in comparison with normative values from healthy children.Study designThe metrics used to assess cardiac health were from thoracic aorta and carotid MRI. Scans were performed on 18 children with CS (mean: 12.5 ± 3.1 years, range: 6.0-16.8 years, 10 female). Pulse wave velocity (PWV), aortic distensibility (AD) and carotid intima-media thickness (cIMT), well-validated measurements of cardiac compromise, were measured from the images and compared to normative age-matched values where available.ResultsPatients with CS had significantly higher PWV compared to age-adjusted normal median control values (4.0 ± 0.7 m/s vs. 3.4 ± 0.2 m/s, respectively, P = 0.0115). PWV was positively correlated with midnight plasma cortisol (r = 0.56, P = 0.02). Internal and common cIMT were negatively correlated with ascending AD (r = -0.75, P = 0.0022, r = -0.69, P = 0.0068, respectively).ConclusionPulse wave velocity data indicate that paediatric patients with CS have early evidence of cardiovascular remodelling. The results suggest the opportunity for monitoring as these changes begin in childhood

The art and science of using quality control to understand and improve fMRI data

Designing and executing a good quality control (QC) process is vital to robust and reproducible science and is often taught through hands on training. As FMRI research trends toward studies with larger sample sizes and highly automated processing pipelines, the people who analyze data are often distinct from those who collect and preprocess the data. While there are good reasons for this trend, it also means that important information about how data were acquired, and their quality, may be missed by those working at later stages of these workflows. Similarly, an abundance of publicly available datasets, where people (not always correctly) assume others already validated data quality, makes it easier for trainees to advance in the field without learning how to identify problematic data. This manuscript is designed as an introduction for researchers who are already familiar with fMRI, but who did not get hands on QC training or who want to think more deeply about QC. This could be someone who has analyzed fMRI data but is planning to personally acquire data for the first time, or someone who regularly uses openly shared data and wants to learn how to better assess data quality. We describe why good QC processes are important, explain key priorities and steps for fMRI QC, and as part of the FMRI Open QC Project, we demonstrate some of these steps by using AFNI software and AFNI’s QC reports on an openly shared dataset. A good QC process is context dependent and should address whether data have the potential to answer a scientific question, whether any variation in the data has the potential to skew or hide key results, and whether any problems can potentially be addressed through changes in acquisition or data processing. Automated metrics are essential and can often highlight a possible problem, but human interpretation at every stage of a study is vital for understanding causes and potential solutions