Trial types and regressors for isolating preparatory activity in the SST.

<p><b>Trials began with a fixation dot (warning stimulus) in the middle of the screen for 500 ms followed by a response stimulus for 1 s.</b> The response stimulus was either an “X”, indicating that the subject should press a button with their left thumb (a), or an “O”, indicating that the subject should press a button with their right thumb (b). On one third of trials (c), the response stimulus was followed by a stop signal (screen colour change from black to red), indicating that the subject should not respond on that trial. The box at the top right of the figure describes the stimuli that are used to estimate warning and response phase activities.</p

Dissociating Two Stages of Preparation in the Stop Signal Task Using fMRI

<div><p>Often we must balance being prepared to act quickly with being prepared to suddenly stop. The stop signal task (SST) is widely used to study inhibitory control, and provides a measure of the speed of the stop process that is robust to changes in subjects’ response strategy. Previous studies have shown that preparation affects inhibition. We used fMRI to separate activity that occurs after a brief (500 ms) warning stimulus (warning-phase) from activity that occurs during responses that follow (response-phase). Both of these phases could contribute to the preparedness to stop because they both precede stop signals. Warning stimuli activated posterior networks that signal the need for top-down control, whereas response phases engaged prefrontal and subcortical networks that implement top-down control. Regression analyses revealed that both of these phases affect inhibitory control in different ways. Warning-phase activity in the cerebellum and posterior cingulate predicted stop latency and accuracy, respectively. By contrast, response-phase activity in fronto-temporal areas and left striatum predicted go speed and stop accuracy, in pre-supplementary motor area affected stop accuracy, and in right striatum predicted stop latency and accuracy. The ability to separate hidden contributions to inhibitory control during warning-phases from those during response-phases can aid in the study of models of preparation and inhibitory control, and of disorders marked by poor top-down control.</p></div

Whole-brain corrected activities during warning-phases.

<p>Arrows indicate regions where the level of warning-phase activity significantly predicted individual differences in SSRT (cerebellum), and PC (posterior cingulate BA 23/31). Abbreviations: SSRT–stop signal reaction time; PC–percent correct inhibition. Numbers in bottom right corners refer to the z coordinate in Talairach space. Increased activity is coloured red-yellow and decreased activity blue. Slices are portrayed in radiological space (right = left).</p

List of whole-brain corrected BOLD activities during response-phases.

<p>BA–Brodmann area; SMA–supplementary motor area; ACC–anterior cingulate cortex; RT–regions that significantly correlated with go reaction time; SSRT–regions that significantly correlated with stop signal reaction time; PC–regions that significantly correlated with stop accuracy (i.e. percent correct stop trials). Locations indicate maximum activity cooridnates for all significant clusters, and are given in Talairach coordinates. Activity estimates in arbitrary units.</p><p>List of whole-brain corrected BOLD activities during response-phases.</p

Whole-brain corrected activities during response-phases.

<p>Arrows indicate regions that significantly predicted individual differences in behaviour. a) Right superior frontal BA 10 predicted RT and PC; right putamen predicted SSRT and PC; right ventral lateral thalamus predicted SSRT; left putamen, internal globus pallidus and left superior temporal BA 22 predicted RT and PC. b) Left superior frontal BA 10 predicted PC. c) Left pre-SMA predicted PC. Abbreviations: RT–go reaction time; PC–percent correct inhibition; SSRT–stop signal reaction time.</p

List of whole-brain corrected BOLD activities during warning-phases.

<p>BA–Brodmann area; SSRT–regions that significantly correlated with stop signal reaction time; PC–regions that significantly correlated with stop accuracy (i.e. percent correct stop trials). Locations indicate maximum activity cooridnates for all significant clusters, and are given in Talairach coordinates. Activity estimates in arbitrary units.</p><p>List of whole-brain corrected BOLD activities during warning-phases.</p

A multi-organ nucleus segmentation challenge

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics