A BIASED COMPETITION COMPUTATIONAL MODEL OF SPATIAL AND OBJECT-BASED ATTENTION MEDIATING ACTIVE VISUAL SEARCH

A computational cognitive neuroscience approach was used to examine processes of visual attention in the human and monkey brain. The aim of the work was to produce a biologically plausible neurodynamical model of both spatial and object-based attention that accounted for observations in monkey visual areas V4, inferior temporal cortex (IT) and the lateral intraparietal area (LIP), and was able to produce search scan path behaviour similar to that observed in humans and monkeys. Of particular interest currently in the visual attention literature is the biased competition hypothesis (Desimone & Duncan. 1995). The model presented here is the first active vision implementation of biased competition, where attcntional shifts are overt. Therefore, retinal inputs change during the scan path and this approach raised issues, such as memory for searched locations across saccades, not addressed bv previous models with static retinas. This is the first model to examine the different time courses associated with spatial and object-based effects at the cellular level. Single cell recordings in areas V4 (Luck et al., 1997; Chelazzi et al., 2001) and IT (Chelazzi ct al., 1993, 1998) were replicated such that attentional effects occurred at the appropriate time after onset of the stimulus. Object-based effects at the cellular level of the model led to systems level behaviour that replicated that observed during active visual search for orientation and colour feature conjunction targets in psychophysical investigations. This provides a valuable insight into the link between cellular and system level behaviour in natural systems. At the systems level, the simulated search process showed selectivity in its scan path that was similar to that observed in humans (Scialfa & Joffe, 1998; Williams & Reingold, 2001) and monkeys (Motter & Belky. 1998b), being guided to target coloured locations in preference to locations containing the target orientation or blank areas. A connection between the ventral and dorsal visual processing streams (Ungerleider & Mishkin. 1982) is suggested to contribute to this selectivity and priority in the featural guidance of search. Such selectivity and avoidance of blank areas has potential application in computer vision applications. Simulation of lesions within the model and comparison with patient data provided further verification of the model. Simulation of visual neglect due to parietal cortical lesion suggests that the model has the capability to provide insights into the neural correlates of the conscious perception of stimuli The biased competition approach described here provides an extendable framework within which further "bottom-up" stimulus and "top-down" mnemonic and cognitive biases can be added, in order to further examine exogenous versus endogenous factors in the capture of attention

Teaching with Big Data: Report from the 2016 Society for Neuroscience Teaching Workshop

As part of a series of workshops on teaching neuroscience at the Society for Neuroscience annual meetings, William Grisham and Richard Olivo organized the 2016 workshop on Teaching Neuroscience with Big Data. This article presents a summary of that workshop. Speakers provided overviews of open datasets that could be used in teaching undergraduate courses. These included resources that already appear in educational settings, including the Allen Brain Atlas (presented by Joshua Brumberg and Terri Gilbert), and the Mouse Brain Library and GeneNetwork (presented by Robert Williams). Other resources, such as NeuroData (presented by William R. Gray Roncal), and OpenFMRI, NeuroVault, and Neurosynth (presented by Russell Poldrack) have not been broadly utilized by the neuroscience education community but offer obvious potential. Finally, William Grisham discussed the iNeuro Project, an NSF-sponsored effort to develop the necessary curriculum for preparing students to handle Big Data. Linda Lanyon further elaborated on the current state and challenges in educating students to deal with Big Data and described some training resources provided by the International Neuroinformatics Coordinating Facility. Neuroinformatics is a subfield of neuroscience that deals with data utilizing analytical tools and computational models. The feasibility of offering neuroinformatics programs at primarily undergraduate institutions was also discussed

Modelling Visual Neglect: Computational Insights into Conscious Perception

Background: Visual neglect is an attentional deficit typically resulting from parietal cortex lesion and sometimes frontal lesion. Patients fail to attend to objects and events in the visual hemifield contralateral to their lesion during visual search. Methodology/Principal Finding: The aim of this work was to examine the effects of parietal and frontal lesion in an existing computational model of visual attention and search and simulate visual search behaviour under lesion conditions. We find that unilateral parietal lesion in this model leads to symptoms of visual neglect in simulated search scan paths, including an inhibition of return (IOR) deficit, while frontal lesion leads to milder neglect and to more severe deficits in IOR and perseveration in the scan path. During simulations of search under unilateral parietal lesion, the model’s extrastriate ventral stream area exhibits lower activity for stimuli in the neglected hemifield compared to that for stimuli in the normally perceived hemifield. This could represent a computational correlate of differences observed in neuroimaging for unconscious versus conscious perception following parietal lesion. Conclusions/Significance: Our results lead to the prediction, supported by effective connectivity evidence, that connections between the dorsal and ventral visual streams may be an important factor in the explanation of perceptua

A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body

Effect of rituximab on a salivary gland ultrasound score in primary Sjögren’s syndrome: results of the TRACTISS randomised double-blind multicentre substudy

Objectives To compare the effects of rituximab versus placebo on salivary gland ultrasound (SGUS) in primary Sjögren’s syndrome (PSS) in a multicentre, multiobserver phase III trial substudy. Methods Subjects consenting to SGUS were randomised to rituximab or placebo given at weeks 0, 2, 24 and 26, and scanned at baseline and weeks 16 and 48. Sonographers completed a 0–11 total ultrasound score (TUS) comprising domains of echogenicity, homogeneity, glandular definition, glands involved and hypoechoic foci size. Baseline-adjusted TUS values were analysed over time, modelling change from baseline at each time point. For each TUS domain, we fitted a repeated-measures logistic regression model to model the odds of a response in the rituximab arm (≥1-point improvement) as a function of the baseline score, age category, disease duration and time point. Results 52 patients (n=26 rituximab and n=26 placebo) from nine centres completed baseline and one or more follow-up visits. Estimated between-group differences (rituximab-placebo) in baseline-adjusted TUS were −1.2 (95% CI −2.1 to −0.3; P=0.0099) and −1.2 (95% CI −2.0 to −0.5; P=0.0023) at weeks 16 and 48. Glandular definition improved in the rituximab arm with an OR of 6.8 (95% CI 1.1 to 43.0; P=0.043) at week 16 and 10.3 (95% CI 1.0 to 105.9; P=0.050) at week 48. Conclusions We demonstrated statistically significant improvement in TUS after rituximab compared with placebo. This encourages further research into both B cell depletion therapies in PSS and SGUS as an imaging biomarker

Dissecting the shared genetic basis of migraine and mental disorders using novel statistical tools

Migraine is three times more prevalent in people with bipolar disorder or depression. The relationship between schizophrenia and migraine is less certain although glutamatergic and serotonergic neurotransmission are implicated in both. A shared genetic basis to migraine and mental disorders has been suggested but previous studies have reported weak or non-significant genetic correlations and five shared risk loci. Using the largest samples to date and novel statistical tools, we aimed to determine the extent to which migraine’s polygenic architecture overlaps with bipolar disorder, depression and schizophrenia beyond genetic correlation, and to identify shared genetic loci. Summary statistics from genome-wide association studies were acquired from large-scale consortia for migraine (n cases = 59 674; n controls = 316 078), bipolar disorder (n cases = 20 352; n controls = 31 358), depression (n cases = 170 756; n controls = 328 443) and schizophrenia (n cases = 40 675, n controls = 64 643). We applied the bivariate causal mixture model to estimate the number of disorder-influencing variants shared between migraine and each mental disorder, and the conditional/conjunctional false discovery rate method to identify shared loci. Loci were functionally characterized to provide biological insights. Univariate MiXeR analysis revealed that migraine was substantially less polygenic (2.8 K disorder-influencing variants) compared to mental disorders (8100–12 300 disorder-influencing variants). Bivariate analysis estimated that 800 (SD = 300), 2100 (SD = 100) and 2300 (SD = 300) variants were shared between bipolar disorder, depression and schizophrenia, respectively. There was also extensive overlap with intelligence (1800, SD = 300) and educational attainment (2100, SD = 300) but not height (1000, SD = 100). We next identified 14 loci jointly associated with migraine and depression and 36 loci jointly associated with migraine and schizophrenia, with evidence of consistent genetic effects in independent samples. No loci were associated with migraine and bipolar disorder. Functional annotation mapped 37 and 298 genes to migraine and each of depression and schizophrenia, respectively, including several novel putative migraine genes such as L3MBTL2, CACNB2 and SLC9B1. Gene-set analysis identified several putative gene sets enriched with mapped genes including transmembrane transport in migraine and schizophrenia. Most migraine-influencing variants were predicted to influence depression and schizophrenia, although a minority of mental disorder-influencing variants were shared with migraine due to the difference in polygenicity. Similar overlap with other brain-related phenotypes suggests this represents a pool of ‘pleiotropic’ variants that influence vulnerability to diverse brain-related disorders and traits. We also identified specific loci shared between migraine and each of depression and schizophrenia, implicating shared molecular mechanisms and highlighting candidate migraine genes for experimental validation

Sensitivity and Bias in Decision-Making under Risk: Evaluating the Perception of Reward, Its Probability and Value

BACKGROUND: There are few clinical tools that assess decision-making under risk. Tests that characterize sensitivity and bias in decisions between prospects varying in magnitude and probability of gain may provide insights in conditions with anomalous reward-related behaviour. OBJECTIVE: We designed a simple test of how subjects integrate information about the magnitude and the probability of reward, which can determine discriminative thresholds and choice bias in decisions under risk. DESIGN/METHODS: Twenty subjects were required to choose between two explicitly described prospects, one with higher probability but lower magnitude of reward than the other, with the difference in expected value between the two prospects varying from 3 to 23%. RESULTS: Subjects showed a mean threshold sensitivity of 43% difference in expected value. Regarding choice bias, there was a 'risk premium' of 38%, indicating a tendency to choose higher probability over higher reward. An analysis using prospect theory showed that this risk premium is the predicted outcome of hypothesized non-linearities in the subjective perception of reward value and probability. CONCLUSIONS: This simple test provides a robust measure of discriminative value thresholds and biases in decisions under risk. Prospect theory can also make predictions about decisions when subjective perception of reward or probability is anomalous, as may occur in populations with dopaminergic or striatal dysfunction, such as Parkinson's disease and schizophrenia

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

No abstract available

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations