Biology subjects

Gallant, J. L.

Publications and source records attributed to Gallant, J. L..

10 recordsLinked to original sources

bioRxiv2026

Attention warps visual-semantic representations across the human cerebellum

Humans effortlessly shift their attention between objects and events in the environment. These attentional shifts modulate the representation of information in many regions of the cerebral cortex, including those that represent visual-semantic information. Based on recent evidence that the cerebellum also represents semantic information, we hypothesized that attention would also shift semantic representations in the cerebellum. To test this hypothesis, we used fMRI to measure brain activity while participants watched naturalistic movie clips and were instructed to attend to either people or vehicles. We used a voxelwise encoding model framework to recover visual-semantic representations in the cerebral cortex and cerebellum under the two conditions, and quantified the direction and magnitude of visual-semantic tuning shifts between conditions. Attention to different semantic categories had a large effect on visual-semantic tuning in the cerebellum. Representations in some regions shifted toward the attended category, and representations in other regions shifted away from the attended category. Within cerebellar regions that best represent visual-semantic information, the tuning shifts were most pronounced in Crus I and II and in lobule VI. Further, the magnitude of attentional tuning shifts was greater in the cerebellum than in the cerebral cortex, particularly for tuning shifts towards the attended category. These differences suggest that attentional modulation in the cerebellum is different than in the cerebral cortex and may not solely be driven by cerebral inputs.

neuroscience↗

bioRxiv2026

Preferred visual experiences provide intuitive descriptions of the functional properties of the cortical navigation network

Complex, high-dimensional brain representations are often difficult to understand intuitively. Recent advances in generative neural networks have made it possible to predict the optimal stimulus that maximizes the activity in a specific brain region. Because these stimuli are in image space, they provide an intuitive understanding of the functional properties of that region. Previous work on optimized stimuli has focused on passive perceptual tasks. Yet real-world perception operates in a closed loop with cognition and action, and so passive stimuli are unlikely to be optimal for many brain regions. In a previous study, we used fMRI to record BOLD activity from participants actively navigating through a virtual world. We identified a network of 11 cortical regions that support active navigation and are tuned for complex feature combinations. Here, we used a variational autoencoder to generate video clips predicted to correspond to maximal activity in each region. These preferred visual experiences capture dynamic scenes that incorporate both world events and participant actions. These preferred visual experiences provide intuitive descriptions of the functional properties of each region, and suggest that these regions are more engaged during dynamic than static scenes. These patterns also generate novel, data-driven hypotheses for future study.

neuroscience↗

bioRxiv2025

Controlled Induction of ALK1 in ACVRL1-null hiPSC-derived Endothelial Cells Provides Insight into Organ-Restricted AVMs in HHT

Genetic alterations in activin receptor-like kinase 1 (ACVRL1, ALK1) are linked to hereditary hemorrhagic telangiectasia (HHT), a condition characterized by hemorrhages, arteriovenous malformations (AVMs) and endothelial cell (EC) dysfunction. Haploinsufficiency is considered the central disease-driving mechanism, but it remains unclear why vascular lesions are tissue-restricted. To investigate whether ACVRL1 gene dosage in human ECs plays a role in organ-specific susceptibility, we developed a tunable human in vitro model by deleting ACVRL1 in human induced pluripotent stem cells (hiPSCs), and then reintroduced wild-type ACVRL1 under control of a doxycycline-inducible promoter. This enabled temporal, dose-dependent induction of ALK1 expression in ECs, allowing definition of threshold levels required to restore endothelial function and support vascular homeostasis. ALK1-deficient hiPSC-ECs displayed disrupted SMAD1/5 signaling, hyperproliferation, altered tip/stalk cell specification, and dysregulated transcriptional programs. Even low-level reinduction of ALK1 was sufficient to restore endothelial function, indicating threshold-dependence for ALK1 in vascular homeostasis. These data indicated that one mechanism underlying tissue-restricted prevalence of AVMs in vascular beds with low basal ACVRL1 expression, such as the liver, is a minimal requirement for ALK1 in sustaining EC functionality. This tunable human platform offers a powerful tool for dissecting HHT pathobiology and a platform for identifying strategies to restore ALK1 signaling therapeutically in affected tissues.

molecular biology↗

bioRxiv2024

A biologically-inspired hierarchical convolutional energy model predicts V4 responses to natural videos

V4 is a key area within the visual processing hierarchy, and it represents features of intermediate complexity. However, no current computational model explains V4 responses under natural conditions. To address this, we developed a new hierarchical convolutional energy (HCE) model reflecting computations thought to occur in areas V1, V2, and V4, but which consists entirely of simple- and complex-like units like those found in V1. In contrast to prior models, the HCE model is trained end-to-end on neurophysiology data, without relying on pre-trained network features. We recorded 313 V4 neurons during full-color nature video stimulation and fit the HCE model to each neuron. The models predicted optimal patterns (POPs) revealed complex spatiotemporal pattern selectivity in V4, supporting its role in representing space, time, and color. These findings indicate that area V4 is crucial for image segmentation and grouping operations that are essential for complex vision. Thus, responses of V4 neurons under naturalistic conditions can be explained by a hierarchical three-stage model where each stage consists entirely of units like those found in area V1.

neuroscience↗

bioRxiv2023

Model connectivity: leveraging the power of encoding models to overcome the limitations of functional connectivity

Functional connectivity (FC) is the most popular method for recovering functional networks of brain areas with fMRI. However, because FC is defined as temporal correlations in brain activity, FC networks are confounded by noise and lack a precise functional role. To overcome these limitations, we developed model connectivity (MC). MC is defined as similarities in encoding model weights, which quantify reliable functional activity in terms of interpretable stimulus- or task-related features. To compare FC and MC, both methods were applied to a naturalistic story listening dataset. FC recovered spatially broad networks that are confounded by noise, and that lack a clear role during natural language comprehension. By contrast, MC recovered spatially localized networks that are robust to noise, and that represent distinct categories of semantic concepts. Thus, MC is a powerful data-driven approach for recovering and interpreting the functional networks that support complex cognitive processes.

neuroscience↗

bioRxiv2022

Feature-space selection with banded ridge regression

Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential complementarity of different feature spaces, a joint model is fit on multiple feature spaces simultaneously. To adapt regularization strength to each feature space, ridge regression is extended to banded ridge regression, which optimizes a different regularization hyperparameter per feature space. The present paper proposes a method to decompose over feature spaces the variance explained by a banded ridge regression model. It also describes how banded ridge regression performs a feature-space selection, effectively ignoring non-predictive and redundant feature spaces. This feature-space selection leads to better prediction accuracy and to better interpretability. Banded ridge regression is then mathematically linked to a number of other regression methods with similar feature-space selection mechanisms. Finally, several methods are proposed to address the computational challenge of fitting banded ridge regressions on large numbers of voxels and feature spaces. All implementations are released in an open-source Python package called Himalaya.

neuroscience↗

bioRxiv2021

Semantic representations during language comprehension are affected by context

The meaning of words in natural language depends crucially on context. However, most neuroimaging studies of word meaning use isolated words and isolated sentences with little context. Because the brain may process natural language differently from how it processes simplified stimuli, there is a pressing need to determine whether prior results on word meaning generalize to natural language. fMRI was used to record human brain activity while four subjects (two female) read words in four conditions that vary in context: narratives, isolated sentences, blocks of semantically similar words, and isolated words. We then compared the signal-to-noise ratio (SNR) of evoked brain responses, and we used a voxelwise encoding modeling approach to compare the representation of semantic information across the four conditions. We find four consistent effects of varying context. First, stimuli with more context evoke brain responses with higher SNR across bilateral visual, temporal, parietal, and prefrontal cortices compared to stimuli with little context. Second, increasing context increases the representation of semantic information across bilateral temporal, parietal, and prefrontal cortices at the group level. In individual subjects, only natural language stimuli consistently evoke widespread representation of semantic information. Third, context affects voxel semantic tuning. Finally, models estimated using stimuli with little context do not generalize well to natural language. These results show that context has large effects on the quality of neuroimaging data and on the representation of meaning in the brain. Thus, neuroimaging studies that use stimuli with little context may not generalize well to the natural regime. Significance StatementContext is an important part of understanding the meaning of natural language, but most neuroimaging studies of meaning use isolated words and isolated sentences with little context. Here we examined whether the results of neuroimaging studies that use out-of-context stimuli generalize to natural language. We find that increasing context improves the quality of neuroimaging data and changes where and how semantic information is represented in the brain. These results suggest that findings from studies using out-of-context stimuli may not generalize to natural language used in daily life.

neuroscience↗

bioRxiv2021

Task-Dependent Warping of Semantic Representations During Search for Visual Action Categories

Object and action perception in cluttered dynamic natural scenes relies on efficient allocation of limited brain resources to prioritize the attended targets over distractors. It has been suggested that during visual search for objects, distributed semantic representation of hundreds of object categories is warped to expand the representation of targets. Yet, little is known about whether and where in the brain visual search for action categories modulates semantic representations. To address this fundamental question, we studied human brain activity recorded via functional magnetic resonance imaging while subjects viewed natural movies and searched for either communication or locomotion actions. We find that attention directed to action categories elicits tuning shifts that warp semantic representations broadly across neocortex, and that these shifts interact with intrinsic selectivity of cortical voxels for target actions. These results suggest that attention serves to facilitate task performance during social interactions by dynamically shifting semantic selectivity towards target actions, and that tuning shifts are a general feature of conceptual representations in the brain.

neuroscience↗

bioRxiv2018

The DeepTune framework for modeling and characterizing neurons in visual cortex area V4

Deep neural network models have recently been shown to be effective in predicting single neuron responses in primate visual cortex areas V4. Despite their high predictive accuracy, these models are generally difficult to interpret. This limits their applicability in characterizing V4 neuron function. Here, we propose the DeepTune framework as a way to elicit interpretations of deep neural network-based models of single neurons in area V4. V4 is a midtier visual cortical area in the ventral visual pathway. Its functional role is not yet well understood. Using a dataset of recordings of 71 V4 neurons stimulated with thousands of static natural images, we build an ensemble of 18 neural network-based models per neuron that accurately predict its response given a stimulus image. To interpret and visualize these models, we use a stability criterion to form optimal stimuli (DeepTune images) by pooling the 18 models together. These DeepTune images not only confirm previous findings on the presence of diverse shape and texture tuning in area V4, but also provide rich, concrete and naturalistic characterization of receptive fields of individual V4 neurons. The population analysis of DeepTune images for 71 neurons reveals how different types of curvature tuning are distributed in V4. In addition, it also suggests strong suppressive tuning for nearly half of the V4 neurons. Though we focus exclusively on the area V4, the DeepTune framework could be applied more generally to enhance the understanding of other visual cortex areas.

neuroscience↗

bioRxiv2018

Voxelwise encoding models with non-spherical multivariate normal priors

Predictive models for neural or fMRI data are often fit using regression methods that employ priors on the model parameters. One widely used method is ridge regression, which employs a spherical Gaussian prior that assumes equal and independent variance for all parameters. However, a spherical prior is not always optimal or appropriate. There are many cases where expert knowledge or hypotheses about the structure of the model parameters could be used to construct a better prior. In these cases, non-spherical Gaussian priors can be employed using a generalized form of ridge known as Tikhonov regression. Yet Tikhonov regression is only rarely used in neuroscience. In this paper we discuss the theoretical basis for Tikhonov regression, demonstrate a computationally efficient method for its application, and show several examples of how Tikhonov regression can improve predictive models for fMRI data. We also show that many earlier studies have implicitly used Tikhonov regression by linearly transforming the regressors before performing ridge regression.

neuroscience↗