Biology subjects

Lu, Z.-L.

Publications and source records attributed to Lu, Z.-L..

4 recordsLinked to original sources

bioRxiv2024

qPRF: A system to accelerate population receptive field decoding

Patterns of BOLD response can be decoded using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). The time cost of evaluating the PRF model is high, often requiring days to decode BOLD signals for a small cohort of subjects. We introduce the qPRF, an efficient method for decoding that reduced the computation time by a factor of 1436 when compared to another widely available PRF decoder (Kay, Winawer, Mezer and Wandell, 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen, Smith, Barch, Behrens, Yacoub and Ugurbil, 2013). With a specially designed data structure and an efficient search algorithm, the qPRF optimizes the five PRF model parameters according to a least-squares criterion. To verify the accuracy of the qPRF solutions, we compared them to those provided by Benson, Jamison, Arcaro, Vu, Glasser, Coalson, Van Essen, Yacoub, Ugurbil, Winawer and Kay (2018). Both hemispheres of the 181 subjects in the HCP data set (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were decoded by qPRF in 15.2 hours on an ordinary CPU. The absolute difference in R2 reported by Benson et al. and achieved by the qPRF was negligible, with a median of 0.39% (R2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R2 on 99.7% of vertices. The qPRF may facilitate the development and computation of more elaborate models based on the PRF framework, as well as the exploration of novel clinical applications. HighlightsO_LIWe describe a novel software system, qPRF, which can perform population receptive field (PRF) decoding of BOLD fMRI at speeds about 1400 times faster than the conventional systems designed for PRF decoding. C_LIO_LIWe show that qPRF yields estimates of PRF model parameters that, in terms of goodness-of-fit, are equivalent to estimates derived using the conventional systems. C_LIO_LIAn efficient similarity-based search strategy, underlies the accelerated computations of qPRF, supported by a specially designed data structure wherein tens of millions of pre-computed prediction curves are stored. C_LI

neuroscience↗

bioRxiv2024

Hierarchical Bayesian Augmented Hebbian Reweighting Model of Perceptual Learning

The Augmented Hebbian Reweighting Model (AHRM) has been effectively utilized to model the collective performance of observers in various perceptual learning studies. In this work, we have introduced a novel hierarchical Bayesian Augmented Hebbian Reweighting Model (HB-AHRM) to simultaneously model the learning curves of individual participants and the entire population within a single framework. We have compared its performance to that of a Bayesian Inference Procedure (BIP), which independently estimates the posterior distributions of model parameters for each individual subject without employing a hierarchical structure. To cope with the substantial computational demands, we developed an approach to approximate the likelihood function in the AHRM with feature engineering and linear regression, increasing the speed of the estimation procedure by 20,000 times. The HB-AHRM has enabled us to compute the joint posterior distribution of hyperparameters and parameters at the population, observer, and test levels, facilitating statistical inferences across these levels. While we have developed this methodology within the context of a single experiment, the HB-AHRM and the associated modeling techniques can be readily applied to analyze data from various perceptual learning experiments and provide predictions of human performance at both the population and individual levels. The likelihood approximation concept introduced in this study may have broader utility in fitting other stochastic models lacking analytic forms.

neuroscience↗

bioRxiv2024

How the window of visibility varies around polar angle

Contrast sensitivity, the amount of contrast required to detect or discriminate an object, depends on spatial frequency (SF): The Contrast Sensitivity Function (CSF) peaks at intermediate SFs and drops at lower and higher SFs and is the basis of computational models of visual object recognition. The CSF varies from foveal to peripheral vision, but only a couple studies have assessed changes around polar angle of the visual field. Sensitivity is generally better along the horizontal than the vertical meridian, and better at the lower vertical than the upper vertical meridian, yielding polar angle asymmetries. Here, we investigate CSF attributes at polar angle locations at both group and individual levels, using Hierarchical Bayesian Modeling. This method enables precise estimation of CSF parameters by decomposing the variability of the dataset into multiple levels and analyzing covariance across observers. At the group level, peak contrast sensitivity and corresponding spatial frequency with the highest sensitivity are higher at the horizontal than vertical meridian, and at the lower than upper vertical meridian. At an individual level, CSF attributes (e.g., maximum sensitivity, the most preferred SF) across locations are highly correlated, indicating that although the CSFs differ across locations, the CSF at one location is predictive of the CSF at another location. Within each location, the CSF attributes co-vary, indicating that CSFs across individuals vary in a consistent manner (e.g., as maximum sensitivity increases, wso does the SF at which sensitivity peaks), but more so at the horizontal than the vertical meridian locations. These results show similarities and uncover some critical polar angle differences across locations and individuals, suggesting that the CSF should not be generalized across iso-eccentric locations around the visual field. Our window of visibility varies with polar angle: It is enhanced and more consistent at the horizontal meridian. Author summaryThe contrast sensitivity function (CSF), depicting how our ability to perceive contrast depends on spatial frequency, characterizes our "window of visibility": We can only see objects with contrast and spatial frequency properties encompassed by this function. The CSF is mostly assessed only along the horizontal meridian of the visual field and sometimes averaged across locations, but visual performance varies with polar angle (e.g., we are more sensitive to objects along the horizontal than the vertical meridian). Here, we systematically assess the key attributes of the CSF and show critical differences in the window of visibility across polar angles and individuals. We found that at the horizontal meridian, our overall contrast sensitivity and preferred SF are higher, and CSFs of individual observers co-vary more than at the vertical meridian. This research highlights that this fundamental perceptual measure is not the same and should be assessed around the visual field. Polar angle thus should be a key consideration for applications of the CSF in computational models of vision.

neuroscience↗

bioRxiv2023

Presaccadic attention enhances and reshapes the Contrast Sensitivity Function around the visual field

Contrast sensitivity, which constrains our vision, decreases from fovea to periphery, from the horizontal to the vertical meridian, and from the lower vertical to the upper vertical meridian. The Contrast Sensitivity Function (CSF) depicts how contrast sensitivity varies with spatial frequency (SF). To overcome these visual constraints, we constantly make saccadic eye movements to foveate on relevant objects in the scene. Already before saccade onset, presaccadic attention shifts to the saccade target and enhances perception. However, it is unknown whether and how it modulates the interplay between contrast sensitivity and SF, and if this effect varies around polar angle locations. Contrast sensitivity enhancement may result from a horizontal or vertical shift of the CSF, increase in bandwidth, or any combination. Here, we investigated these possibilities by extracting key attributes of the CSF using Hierarchical Bayesian Modeling, which enables precise estimation of the CSF parameters by decomposing the variability of the dataset into multiple levels. The results reveal that presaccadic attention (1) enhances contrast sensitivity across SF, (2) increases the most preferred and the highest discernable SF, and (3) narrows the bandwidth. Therefore, presaccadic attention bridges the gap between presaccadic and post-saccadic input by increasing visibility at the saccade target. Counterintuitively, the presaccadic enhancement in contrast sensitivity was more pronounced where perception is better -along the horizontal than the vertical meridian- exacerbating polar angle asymmetries. Our results call for an investigation of the differential neural modulations underlying presaccadic perceptual changes for different saccade directions. Significance statementThe contrast sensitivity function (CSF) describes how our ability to perceive contrast depends on spatial frequency. Contrast sensitivity is highest at the fovea and decreases in the periphery, especially at vertical locations. We thus make saccadic eye movements to view objects in detail. Already before moving our eyes, presaccadic attention enhances perception at the target location. But how does it influence the interplay between spatial frequency and contrast sensitivity, and does its effect vary around the visual field? Using Hierarchical Bayesian Modeling, we show that presaccadic attention enhances and reshapes the CSF to prepare the periphery for upcoming fixation. Counterintuitively, it does so more at horizontal locations where vision is stronger, suggesting smoother perception across horizontal than vertical eye movements.

neuroscience↗