Cortical depth dependent population receptive field attraction by spatial attention in human V1

Visual spatial attention concentrates neural resources at the attended location. Recently, we demonstrated that voluntary spatial attention attracts population receptive fields (pRFs) toward its location throughout the visual hierarchy. Theoretically, both a feed forward or feedback mechanism could underlie pRF attraction in a given cortical area. Here, we use sub-millimeter ultra-high field functional MRI to measure pRF attraction across cortical depth and assess the contribution of feed forward and feedback signals to pRF attraction. In line with previous findings, we find consistent attraction of pRFs with voluntary spatial attention in V1. When assessed as a function of cortical depth, we find pRF attraction in every cortical portion (deep, center and superficial), although the attraction is strongest in deep cortical portions (near the gray-white matter boundary). Following the organization of feed forward and feedback processing across V1, we speculate that a mixture of feed forward and feedback processing underlies pRF attraction in V1. Specifically, we propose that feedback processing contributes to the pRF attraction in deep cortical portions

Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Attraction of position preference by spatial attention throughout human visual cortex

Voluntary spatial attention concentrates neural resources at the attended location. Here, we examined the effects of spatial attention on spatial position selectivity in humans. We measured population receptive fields (pRFs) using high-field functional MRI (fMRI) (7T) while subjects performed an attention-demanding task at different locations. We show that spatial attention attracts pRF preferred positions across the entire visual field, not just at the attended location. This global change in pRF preferred positions systematically increases up the visual hierarchy. We model these pRF preferred position changes as an interaction between two components: an attention field and a pRF without the influence of attention. This computational model suggests that increasing effects of attention up the hierarchy result primarily from differences in pRF size and that the attention field is similar across the visual hierarchy. A similar attention field suggests that spatial attention transforms different neural response selectivities throughout the visual hierarchy in a similar manner

Predicting bias in perceived position using attention field models

Attention is the mechanism through which we select relevant information from our visual environment. We have recently demonstrated that attention attracts receptive fields across the visual hierarchy (Klein, Harvey, & Dumoulin, 2014). We captured this receptive field attraction using an attention field model. Here, we apply this model to human perception: We predict that receptive field attraction results in a bias in perceived position, which depends on the size of the underlying receptive fields. We instructed participants to compare the relative position of Gabor stimuli, while we manipulated the focus of attention using exogenous cueing. We varied the eccentric position and spatial frequency of the Gabor stimuli to vary underlying receptive field size. The positional biases as a function of eccentricity matched the predictions by an attention field model, whereas the bias as a function of spatial frequency did not. As spatial frequency and eccentricity are encoded differently across the visual hierarchy, we speculate that they might interact differently with the attention field that is spatially defined

Predicting bias in perceived position using attention field models

Attention is the mechanism through which we select relevant information from our visual environment. We have recently demonstrated that attention attracts receptive fields across the visual hierarchy (Klein, Harvey, & Dumoulin, 2014). We captured this receptive field attraction using an attention field model. Here, we apply this model to human perception: We predict that receptive field attraction results in a bias in perceived position, which depends on the size of the underlying receptive fields. We instructed participants to compare the relative position of Gabor stimuli, while we manipulated the focus of attention using exogenous cueing. We varied the eccentric position and spatial frequency of the Gabor stimuli to vary underlying receptive field size. The positional biases as a function of eccentricity matched the predictions by an attention field model, whereas the bias as a function of spatial frequency did not. As spatial frequency and eccentricity are encoded differently across the visual hierarchy, we speculate that they might interact differently with the attention field that is spatially defined

Cortical depth dependent population receptive field attraction by spatial attention in human V1

Visual spatial attention concentrates neural resources at the attended location. Recently, we demonstrated that voluntary spatial attention attracts population receptive fields (pRFs) toward its location throughout the visual hierarchy. Theoretically, both a feed forward or feedback mechanism could underlie pRF attraction in a given cortical area. Here, we use sub-millimeter ultra-high field functional MRI to measure pRF attraction across cortical depth and assess the contribution of feed forward and feedback signals to pRF attraction. In line with previous findings, we find consistent attraction of pRFs with voluntary spatial attention in V1. When assessed as a function of cortical depth, we find pRF attraction in every cortical portion (deep, center and superficial), although the attraction is strongest in deep cortical portions (near the gray-white matter boundary). Following the organization of feed forward and feedback processing across V1, we speculate that a mixture of feed forward and feedback processing underlies pRF attraction in V1. Specifically, we propose that feedback processing contributes to the pRF attraction in deep cortical portions

Cortical depth dependent population receptive field attraction by spatial attention in human V1

Visual spatial attention concentrates neural resources at the attended location. Recently, we demonstrated that voluntary spatial attention attracts population receptive fields (pRFs) toward its location throughout the visual hierarchy. Theoretically, both a feed forward or feedback mechanism could underlie pRF attraction in a given cortical area. Here, we use sub-millimeter ultra-high field functional MRI to measure pRF attraction across cortical depth and assess the contribution of feed forward and feedback signals to pRF attraction. In line with previous findings, we find consistent attraction of pRFs with voluntary spatial attention in V1. When assessed as a function of cortical depth, we find pRF attraction in every cortical portion (deep, center and superficial), although the attraction is strongest in deep cortical portions (near the gray-white matter boundary). Following the organization of feed forward and feedback processing across V1, we speculate that a mixture of feed forward and feedback processing underlies pRF attraction in V1. Specifically, we propose that feedback processing contributes to the pRF attraction in deep cortical portions