Biology subjects

Prince, J. S.

Publications and source records attributed to Prince, J. S..

4 recordsLinked to original sources

bioRxiv2022

Food for thought: selectivity for food in human ventral visual cortex

Visual cortex contains regions of selectivity for domains of ecological importance. Food is an evolutionarily critical category whose visual heterogeneity may make the identification of selectivity more challenging. We investigate neural responsiveness to food using natural images combined with large-scale human fMRI. Leveraging the improved sensitivity of modern designs and statistical analyses, we identify two food-selective regions in the ventral visual cortex. Our results are robust across 8 subjects from the Natural Scenes Dataset (NSD), multiple independent image sets and multiple analysis methods. We then test our findings of food selectivity in an fMRI "localizer" using grayscale food images. These independent results confirm the existence of food selectivity in ventral visual cortex and help illuminate why earlier studies may have failed to do so. Our identification of food-selective regions stands alongside prior findings of functional selectivity and adds to our understanding of the organization of knowledge within the human visual system.

neuroscience↗

bioRxiv2022

Large-Scale Benchmarking of Diverse Artificial Vision Models in Prediction of 7T Human Neuroimaging Data

The rapid development and open-source release of highly performant computer vision models offers new potential for examining how different inductive biases impact representation learning and emergent alignment with the high-level human ventral visual system. Here, we assess a diverse set of 224 models, curated to enable controlled comparison of different model properties, testing their brain predictivity using large-scale functional magnetic resonance imaging data. We find that models with qualitatively different architectures (e.g. CNNs versus Transformers) and markedly different task objectives (e.g. purely visual contrastive learning versus vision-language alignment) achieve near equivalent degrees of brain predictivity, when other factors are held constant. Instead, variation across model visual training diets yields the largest, most consistent effect on emergent brain predictivity. Overarching model properties commonly suspected to increase brain predictivity (e.g. greater effective dimensionality; learnable parameter count) were not robust indicators across this more extensive survey. We highlight that standard model-to-brain linear re-weighting methods may be too flexible, as most performant models have very similar brain-predictivity scores, despite significant variation in their underlying representations. Broadly, our findings point to the importance of visual diet, challenge common assumptions about the methods used to link models to brains, and more concretely outline future directions for leveraging the full diversity of existing open-source models as tools to probe the common computational principles underlying biological and artificial visual systems.

neuroscience↗

bioRxiv2022

GLMsingle: a toolbox for improving single-trial fMRI response estimates

Advances in modern artificial intelligence (AI) have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to tens of thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing GLMsingle, a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses (glmsingle.org). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Furthermore, these improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. Specifically, we demonstrate that GLMsingle: (i) improves the decorrelation of response estimates between trials that are nearby in time; (ii) enhances representational similarity between subjects both within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets that sample brain activity across many experimental conditions.

neuroscience↗

bioRxiv2021

Brain networks in human conscious visual perception

Consciousness is not explained by a single mechanism, rather it involves multiple specialized neural systems overlapping in space and time. We hypothesize that synergistic, large-scale subcortical and cortical attention and signal processing networks encode conscious experiences. To identify brain activity in conscious perception without overt report, we classified visual stimuli as perceived or not using eye measurements. Report-independent event-related potentials and functional magnetic resonance imaging (fMRI) signals both occurred at early times after stimuli. Direct recordings revealed a novel thalamic awareness potential linked to conscious visual perception based on report. fMRI showed thalamic and cortical detection, arousal, attentional salience, task-positive, and default mode networks were involved independent of overt report. These findings identify a specific sequence of neural mechanisms in human conscious visual perception. One-Sentence SummaryHuman conscious visual perception engages large-scale subcortical and cortical networks even without overt report.

neuroscience↗