Translucency and the perception of shape

Previous studies have shown that the perceived threedimensional (3D) shape of objects depends on their material composition. The majority of this work has focused on glossy, flat-matte, or velvety materials. Here, we studied perceived 3D shape of translucent materials. We manipulated the spatial frequency of surface relief perturbations of translucent and opaque objects. Observers indicated which of two surfaces appeared to have more bumps. They also judged local surface orientation using gauge probe figures. We found that translucent surfaces appeared to have fewer bumps than opaque surfaces with the same 3D shape (Experiment 1), particularly when self-occluding contours were hidden from view (Experiment 2). We also found that perceived local curvature was underestimated for translucent objects relative to opaque objects, and that estimates of perceived local surface orientation were similarly correlated with luminance for images of both opaque and translucent objects (Experiment 3). These findings suggest that the perceived mesoscopic shape of completely matte translucent objects can be underestimated due to a decline in the steepness of luminance gradients relative to those of opaque objects

A novel cortical biomarker signature for predicting pain sensitivity : protocol for the PREDICT longitudinal analytical validation study

Introduction: Temporomandibular disorder is a common musculoskeletal pain condition with development of chronic symptoms in 49% of patients. Although a number of biological factors have shown an association with chronic temporomandibular disorder in cross-sectional and case control studies, there are currently no biomarkers that can predict the development of chronic symptoms. The PREDICT study aims to undertake analytical validation of a novel peak alpha frequency (PAF) and corticomotor excitability (CME) biomarker signature using a human model of the transition to sustained myofascial temporomandibular pain (masseter intramuscular injection of nerve growth factor [NGF]). This article describes, a priori, the methods and analysis plan. Methods: This study uses a multisite longitudinal, experimental study to follow individuals for a period of 30 days as they progressively develop and experience complete resolution of NGF-induced muscle pain. One hundred fifty healthy participants will be recruited. Participants will complete twice daily electronic pain diaries from day 0 to day 30 and undergo assessment of pressure pain thresholds, and recording of PAF and CME on days 0, 2, and 5. Intramuscular injection of NGF will be given into the right masseter muscle on days 0 and 2. The primary outcome is pain sensitivity. Perspective: PREDICT is the first study to undertake analytical validation of a PAF and CME biomarker signature. The study will determine the sensitivity, specificity, and accuracy of the biomarker signature to predict an individual's sensitivity to pain

Brain Segmentation From Computed Tomography of Healthy Aging and Geriatric Concussion at Variable Spatial Resolutions

When properly implemented and processed, anatomic T1-weighted magnetic resonance imaging (MRI) can be ideal for the noninvasive quantification of white matter (WM) and gray matter (GM) in the living human brain. Although MRI is more suitable for distinguishing GM from WM than computed tomography (CT), the growing clinical use of the latter technique has renewed interest in head CT segmentation. Such interest is particularly strong in settings where MRI is unavailable, logistically unfeasible or prohibitively expensive. Nevertheless, whereas MRI segmentation is a sophisticated and technically-mature research field, the task of automatically classifying soft brain tissues from CT remains largely unexplored. Furthermore, brain segmentation methods for MRI hold considerable potential for adaptation and application to CT image processing. Here we demonstrate this by combining probabilistic, atlas-based classification with topologically-constrained tissue boundary refinement to delineate WM, GM and cerebrospinal fluid (CSF) from head CT images. The feasibility and utility of this approach are revealed by comparison of MRI-only vs. CT-only segmentations in geriatric concussion victims with both MRI and CT scans. Comparison of the two segmentations yields mean Sørensen-Dice coefficients of 85.5 ± 4.6% (WM), 86.7 ± 5.6% (GM) and 91.3 ± 2.8% (CSF), as well as average Hausdorff distances of 3.76 ± 1.85 mm (WM), 3.43 ± 1.53 mm (GM) and 2.46 ± 1.27 mm (CSF). Bootstrapping results suggest that the segmentation approach is sensitive enough to yield WM, GM and CSF volume estimates within ~5%, ~4%, and ~3% of their MRI-based estimates, respectively. To our knowledge, this is the first 3D segmentation approach for CT to undergo rigorous within-subject comparison with high-resolution MRI. Results suggest that (1) standard-quality CT allows WM/GM/CSF segmentation with reasonable accuracy, and that (2) the task of soft brain tissue classification from CT merits further attention from neuroimaging researchers

Spatial presence depends on ‘coupling’ between body sway and visual motion presented on head-mounted displays (HMDs)

This study investigated the effects of simulating self-motion via a head-mounted display (HMD) on standing postural sway and spatial presence. Standing HMD users viewed simulated oscillatory self-motion in depth. On a particular trial, this naso-occipital visual oscillation had one of four different amplitudes (either 4, 8, 12 or 16 m peak-to-peak) and one of four different frequencies (either 0.125, 0.25, 0.5 or 1 Hz). We found that simulated high amplitude self-oscillation (approximately 16 m peak-to-peak) at either 0.25 Hz or 0.5 Hz: 1) generated the strongest effects on postural sway; and 2) made participants feel more spatially present in the virtual environment. Our findings provide insight into the parameters of simulated self-motion that generate the strongest postural responses within virtual environments. These postural constraints have valuable implications for improving our understanding of sensory processes underlying the ergonomic experience of virtual environments simulated using HMDs

Regional Neuroanatomic Effects on Brain Age Inferred Using Magnetic Resonance Imaging and Ridge Regression.

The biological age of the brain differs from its chronological age (CA) and can be used as biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age (BA) is often estimated from magnetic resonance images (MRIs) using machine learning (ML) that rarely indicates how regional brain features contribute to BA. Leveraging an aggregate training sample of 3 418 healthy controls (HCs), we describe a ridge regression model that quantifies each region's contribution to BA. After model testing on an independent sample of 651 HCs, we compute the coefficient of partial determination R¯p2 for each regional brain volume to quantify its contribution to BA. Model performance is also evaluated using the correlation r between chronological and biological ages, the mean absolute error (MAE ) and mean squared error (MSE) of BA estimates. On training data, r=0.92, MSE=70.94 years, MAE=6.57 years, and R¯2=0.81; on test data, r=0.90, MSE=81.96 years, MAE=7.00 years, and R¯2=0.79. The regions whose volumes contribute most to BA are the nucleus accumbens (R¯p2=7.27%), inferior temporal gyrus (R¯p2=4.03%), thalamus (R¯p2=3.61%), brainstem (R¯p2=3.29%), posterior lateral sulcus (R¯p2=3.22%), caudate nucleus (R¯p2=3.05%), orbital gyrus (R¯p2=2.96%), and precentral gyrus (R¯p2=2.80%). Our ridge regression, although outperformed by the most sophisticated ML approaches, identifies the importance and relative contribution of each brain structure to overall BA. Aside from its interpretability and quasi-mechanistic insights, our model can be used to validate future ML approaches for BA estimation

The reliability of two prospective cortical biomarkers for pain : EEG peak alpha frequency and TMS corticomotor excitability

Background: Many pain biomarkers fail to move from discovery to clinical application, attributed to poor reliability and an inability to accurately classify at-risk individuals. Preliminary evidence has shown that high pain sensitivity is associated with slow peak alpha frequency (PAF), and depression of corticomotor excitability (CME), potentially due to impairments in ascending sensory and descending motor pathway signalling respectively New method: The present study evaluated the reliability of PAF and CME responses during sustained pain. Specifically, we determined whether, over several days of pain, a) PAF remains stable and b) individuals show two stable and distinct CME responses: facilitation and depression. Participants were given an injection of nerve growth factor (NGF) into the right masseter muscle on Day 0 and Day 2, inducing sustained pain. Electroencephalography (EEG) to assess PAF and transcranial magnetic stimulation (TMS) to assess CME were recorded on Day 0, Day 2 and Day 5. Results: Using a weighted peak estimate, PAF reliability (n = 75) was in the excellent range even without standard pre-processing and ∼2 min recording length. Using a single peak estimate, PAF reliability was in the moderate-good range. For CME (n = 74), 80% of participants showed facilitation or depression of CME beyond an optimal cut-off point, with the stability of these changes in the good range. Comparison with existing methods: No study has assessed the reliability of PAF or feasibility of classifying individuals as facilitators/depressors, in response to sustained pain. PAF was reliable even in the presence of pain. The use of a weighted peak estimate for PAF is recommended, as excellent test-retest reliability can be obtained even when using minimal pre-processing and ∼2 min recording. We also showed that 80% of individuals exhibit either facilitation or depression of CME, with these changes being stable across sessions. Conclusions: Our study provides support for the reliability of PAF and CME as prospective cortical biomarkers. As such, our paper adds important methodological advances to the rapidly growing field of pain biomarkers

OSARI, an Open-Source Anticipated Response Inhibition Task

The stop-signal paradigm has become ubiquitous in investigations of inhibitory control. Tasks inspired by the paradigm, referredto as stop-signal tasks, require participants to make responses on go trials and to inhibit those responses when presented with astop-signal on stop trials. Currently, the most popular version of the stop-signal task is the ‘choice-reaction’ variant, whereparticipants make choice responses, but must inhibit those responses when presented with a stop-signal. An alternative to thechoice-reaction variant of the stop-signal task is the ‘anticipated response inhibition’ task. In anticipated response inhibition tasks,participants are required to make a planned response that coincides with a predictably timed event (such as lifting a finger from acomputer key to stop a filling bar at a predefined target). Anticipated response inhibition tasks have some advantages over themore traditional choice-reaction stop-signal tasks and are becoming increasingly popular. However, currently, there are noopenly available versions of the anticipated response inhibition task, limiting potential uptake. Here, we present an open-source,free, and ready-to-use version of the anticipated response inhibition task, which we refer to as the OSARI (the Open-SourceAnticipated Response Inhibition) task