Where we perceive before we look: The distribution of presaccadic attention assessed with dynamic 1/f noise

Already before the onset of a saccadic eye movement, we preferentially process visual information at the upcoming eye fixation. This 'presaccadic shift of attention' is typically assessed via localized test items, which potentially bias the attention measurement. Here we show how presaccadic attention shapes perception from saccade origin to target when no scene-structuring items are presented. Participants made saccades into a 1/f ('pink') noise field, in which we embedded a brief orientation signal at various locations shortly before saccade onset. Local orientation discrimination performance served as a proxy for the allocation of attention. Results demonstrate that (1) saccades are preceded by shifts of attention to their goal location even if they are directed into an unstructured visual field, but the spread of attention, compared to target-directed saccades, is broad; (2) the presaccadic attention shift is accompanied by considerable attentional costs at the presaccadic eye fixation; (3) objects markedly shape the distribution of presaccadic attention, demonstrating the relevance of an item-free approach for measuring attentional dynamics across the visual field.

Noninformative Vision of Body Movements can Enhance Tactile Discrimination

Vision of the body without task cues enhances tactile discrimination performance. This effect has been investigated only with static visual information, although our body usually moves, and dynamic visual and bodily information provides ownership (SoO) and agency (SoA) sensations to body parts. We investigated whether vision of body movements could enhance tactile discrimination performance. Participants observed white dots without any textural information showing lateral hand movements (dynamic condition) or static hands (static condition). For participants experiencing the dynamic condition first, it induced a lower tactile discrimination threshold, as well as a stronger SoO and SoA, compared to the static condition. For participants observing the static condition first, the magnitudes of the enhancement effect in the dynamic condition were positively correlated between the tactile discrimination and SoO/SoA. The enhancement of the dynamic visual information was not observed when the hand shape was not maintained in the scrambled white dot images. Our results suggest that dynamic visual information without task cues can enhance tactile discrimination performance by feeling SoO and SoA only when it maintains bodily information.

Association between Patient-Reported Outcomes and Survival in Patients with Advanced Urothelial Carcinoma Treated with Atezolizumab

BACKGROUND: Atezolizumab is an immune checkpoint inhibitor (ICI) and a frontline treatment of patients with cisplatin-ineligible advanced urothelial carcinoma (UC). There is limited evidence on the prognostic value of patient reported outcomes (PROs) in advanced UC treatment, particularly in the context of ICI therapy. OBJECTIVE: To investigate the prognostic association of PROs with survival in patients with advanced UC treated with atezolizumab. METHODS: This study used data from 467 patients with advanced UC initiating atezolizumab in the IMvigor211 trial. Pre-treatment PROs association with overall survival (OS) and progression free survival (PFS) was assessed using Cox proportional hazard analysis. PROs were recorded via the European Organisation for Research and Treatment of Cancer QLQ-C30. Discrimination performance was assessed via the C-statistic (c). RESULTS: Patient reported physical function, pain, appetite loss, global health, fatigue, role function, constipation, nausea and vomiting, dyspnoea, and insomnia were significantly associated with OS and PFS on univariable and adjusted analysis (P < 0.05). Physical function (c = 0.63), pain (c = 0.63), appetite loss (c = 0.62), global health status (c = 0.62), and fatigue (c = 0.62), were the most prognostic factors of OS. The OS discrimination performance of physical function (c = 0.61) was superior to ECOG PS (c = 0.58). Of patients assessed by investigators as having no performance restrictions (ECOG PS of 0), 38 (18%) and 91 (42%) self-reported low and intermediate physical function scores, respectively. CONCLUSION: Pre-treatment PROs were identified as independent prognostic factors of OS and PFS. Patient-reported physical function was more prognostic of OS than ECOG PS. This highlights a potential for PROs to enable improved patient stratification in ICI trials.

The evolution of quantitative sensitivity

The ability to represent approximate quantities appears to be phylogenetically widespread, but the selective pressures and proximate mechanisms favouring this ability remain unknown. We analysed quantity discrimination data from 672 subjects across 33 bird and mammal species, using a novel Bayesian model that combined phylogenetic regression with a model of number psychophysics and random effect components. This allowed us to combine data from 49 studies and calculate the Weber fraction (a measure of quantity representation precision) for each species. We then examined which cognitive, socioecological and biological factors were related to variance in Weber fraction. We found contributions of phylogeny to quantity discrimination performance across taxa. Of the neural, socioecological and general cognitive factors we tested, cortical neuron density and domain-general cognition were the strongest predictors of Weber fraction, controlling for phylogeny. Our study is a new demonstration of evolutionary constraints on cognition, as well as of a relation between species-specific neuron density and a particular cognitive ability. This article is part of the theme issue ‘Systems neuroscience through the lens of evolutionary theory’.

RISK OF POST STROKE PNEUMONIA DURING HOSPITALIZATION

Introduction: Post-stroke pneumonia is the most a common complication during the first few weeks after a stroke. Thus, a score is needed for the early identification of stroke patients with an increased risk of pneumonia to assist the nursing team in preventing the onset of pneumonia in stroke patients during hospitalization. This study aimed to assess the application of the A2DS2 score to predict pneumonia in acute ischemic stroke patients. Method: This is a diagnostic study that used a cross-sectional method conducted among adult acute ischemic stroke patients. Data analysis was performed to assess the calibration and discrimination performance of the A2DS2 score. Results: A total of 16 respondents were followed up. The incidence of post-stroke pneumonia was observed in 6 patients (37.5%). Conclusion: This scoring proved clinically accurate to predict the incidence of pneumonia in acute ischemic stroke patients.

Value of Radiomics of Perinephric Fat for Prediction of Intraoperative Complexity in Renal Tumor Surgery

<b><i>Introduction:</i></b> The aim of this study was to assess the value of computed tomography (CT)-based radiomics of perinephric fat (PNF) for prediction of surgical complexity. <b><i>Methods:</i></b> Fifty-six patients who underwent renal tumor surgery were included. Radiomic features were extracted from contrast-enhanced CT. Machine learning models using radiomic features, the Mayo Adhesive Probability (MAP) score, and/or clinical variables (age, sex, and body mass index) were compared for the prediction of adherent PNF (APF), the occurrence of postoperative complications (Clavien-Dindo Classification ≥2), and surgery duration. Discrimination performance was assessed by the area under the receiver operating characteristic curve (AUC). In addition, the root mean square error (RMSE) and <i>R</i>² (fraction of explained variance) were used as additional evaluation metrics. <b><i>Results:</i></b> A single feature logit model containing “Wavelet-LHH-transformed GLCM Correlation” achieved the best discrimination (AUC 0.90, 95% confidence interval [CI]: 0.75–1.00) and lowest error (RMSE 0.32, 95% CI: 0.20–0.42) at prediction of APF. This model was superior to all other models containing all radiomic features, clinical variables, and/or the MAP score. The performance of uninformative benchmark models for prediction of postoperative complications and surgery duration were not improved by machine learning models. <b><i>Conclusion:</i></b> Radiomic features derived from PNF may provide valuable information for preoperative risk stratification of patients undergoing renal tumor surgery.

A Bounding Box-Based Radiomics Model for Detecting Occult Peritoneal Metastasis in Advanced Gastric Cancer: A Multicenter Study

PurposeTo develop a bounding box (BBOX)-based radiomics model for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients.Materials and Methods599 AGC patients from 3 centers were retrospectively enrolled and were divided into training, validation, and testing cohorts. The minimum circumscribed rectangle of the ROIs for the largest tumor area (R_BBOX), the nonoverlapping area between the tumor and R_BBOX (peritumoral area; PERI) and the smallest rectangle that could completely contain the tumor determined by a radiologist (M_BBOX) were used as inputs to extract radiomic features. Multivariate logistic regression was used to construct a radiomics model to estimate the preoperative probability of OPM in AGC patients.ResultsThe M_BBOX model was not significantly different from R_BBOX in the validation cohort [AUC: M_BBOX model 0.871 (95% CI, 0.814–0.940) vs. R_BBOX model 0.873 (95% CI, 0.820–0.940); p = 0.937]. M_BBOX was selected as the final radiomics model because of its extremely low annotation cost and superior OPM discrimination performance (sensitivity of 85.7% and specificity of 82.8%) over the clinical model, and this radiomics model showed comparable diagnostic efficacy in the testing cohort.ConclusionsThe BBOX-based radiomics could serve as a simpler reliable and powerful tool for the preoperative diagnosis of OPM in AGC patients. And M_BBOX-based radiomics is simpler and less time consuming.

Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder

Objectives: To implement a machine learning model using radiomic features extracted from quantitative susceptibility mapping (QSM) in discriminating multiple sclerosis (MS) from neuromyelitis optica spectrum disorder (NMOSD).Materials and Methods: Forty-seven patients with MS (mean age = 40.00 ± 13.72 years) and 36 patients with NMOSD (mean age = 42.14 ± 12.34 years) who underwent enhanced gradient-echo T2*-weighted angiography (ESWAN) sequence in 3.0-T MRI were included between April 2017 and October 2019. QSM images were reconstructed from ESWAN, and QSM-derived radiomic features were obtained from seven regions of interest (ROIs), including bilateral putamen, globus pallidus, head of the caudate nucleus, thalamus, substantia nigra, red nucleus, and dentate nucleus. A machine learning model (logistic regression) was applied to classify MS and NMOSD, which combined radiomic signatures and demographic information to assess the classification accuracy using the area under the receiver operating characteristic (ROC) curve (AUC).Results: The radiomics-only models showed better discrimination performance in almost all deep gray matter (DGM) regions than the demographic information-only model, with the highest AUC in DN of 0.902 (95% CI: 0.840–0.955). Moreover, the hybrid model combining radiomic signatures and demographic information showed the highest discrimination performance which achieved the AUC of 0.927 (95% CI: 0.871–0.984) with fivefold cross-validation.Conclusion: The hybrid model based on QSM and powered with machine learning has the potential to discriminate MS from NMOSD.

The role of eye movements in perceiving vehicle speed and time-to-arrival at the roadside

To avoid collisions, pedestrians depend on their ability to perceive and interpret the visual motion of other road users. Eye movements influence motion perception, yet pedestrians’ gaze behavior has been little investigated. In the present study, we ask whether observers sample visual information differently when making two types of judgements based on the same virtual road-crossing scenario and to which extent spontaneous gaze behavior affects those judgements. Participants performed in succession a speed and a time-to-arrival two-interval discrimination task on the same simple traffic scenario—a car approaching at a constant speed (varying from 10 to 90 km/h) on a single-lane road. On average, observers were able to discriminate vehicle speeds of around 18 km/h and times-to-arrival of 0.7 s. In both tasks, observers placed their gaze closely towards the center of the vehicle’s front plane while pursuing the vehicle. Other areas of the visual scene were sampled infrequently. No differences were found in the average gaze behavior between the two tasks and a pattern classifier (Support Vector Machine), trained on trial-level gaze patterns, failed to reliably classify the task from the spontaneous eye movements it elicited. Saccadic gaze behavior could predict time-to-arrival discrimination performance, demonstrating the relevance of gaze behavior for perceptual sensitivity in road-crossing.

Predicting cerebral infarction in patients with atrial fibrillation using machine learning: The Fushimi AF registry

The CHADS2 and CHA2DS2-VASc scores are widely used to assess ischemic risk in the patients with atrial fibrillation (AF). However, the discrimination performance of these scores is limited. Using the data from a community-based prospective cohort study, we sought to construct a machine learning-based prediction model for cerebral infarction in patients with AF, and to compare its performance with the existing scores. All consecutive patients with AF treated at 81 study institutions from March 2011 to May 2017 were enrolled (n = 4396). The whole dataset was divided into a derivation cohort (n = 1005) and validation cohort (n = 752) after excluding the patients with valvular AF and anticoagulation therapy. Using the derivation cohort dataset, a machine learning model based on gradient boosting tree algorithm (ML) was built to predict cerebral infarction. In the validation cohort, the receiver operating characteristic area under the curve of the ML model was higher than those of the existing models according to the Hanley and McNeil method: ML, 0.72 (95%CI, 0.66–0.79); CHADS2, 0.61 (95%CI, 0.53–0.69); CHA2DS2-VASc, 0.62 (95%CI, 0.54–0.70). As a conclusion, machine learning algorithm have the potential to perform better than the CHADS2 and CHA2DS2-VASc scores for predicting cerebral infarction in patients with non-valvular AF.