Biology subjects

Hansen, B. C.

Publications and source records attributed to Hansen, B. C..

5 recordsLinked to original sources

bioRxiv2025

Differential retinoic acid responses across testicular development in vitro

Spermatogonial differentiation is controlled by distinct spatial and temporal activities of metabolizing enzymes and signaling molecule formation. In vitro models are reductionist ways to examine the interactions between Sertoli cells and spermatogonial stem cells, which may be therapeutic targets for infertility that cause non-obstructive azoospermia, due to either Sertoli-cell only syndrome, hypospermatogenesis or maturation arrest. Here, we find that nanomolar doses of isotretinoin are sufficient to drive Stra8 expression in vitro, an interaction that is both dose-dependent and inhibitable. We compare complex in vitro models (CIVMs) seeded from cells isolated post-natal day 5 (PND 5) and post-natal day 10 (PND 10) Sprague Dawley rat testis. The CIVMs maintain metabolic capacity to produce bioactive retinoids form retinol. We also investigate the impact of common media supplementations on spermatogonial phenotype and find that they can impact the expression of Stra8 and Plzf as markers of early differentiating and undifferentiated spermatogonia, respectively. These results highlight the power of in vitro models to investigate the dynamics of the spermatogonial niche.

pharmacology and toxicology↗

bioRxiv2025

Brain-Guided Convolutional Neural Networks Reveal Task-Specific Representations in Scene Processing

Scene categorization is the dominant proxy for visual understanding, yet humans can perform a large number of visual tasks within any scene. Consequently, we know little about how different tasks change how the scene is processed, represented, and its features ultimately used. Here, we developed a novel brain-guided convolutional neural network (CNN) where each convolutional layer was separately guided by neural responses taken at different time points while observers performed two different tasks on the same set of images. We then reconstructed each layers activation maps via deconvolution to spatially assess how different features were used as a function of task. The brain-guided CNN made use of image features that human observers identified as being crucial to complete each task starting around 244 ms and persisted to 402 ms. Critically, because the same images were used across the two tasks, the CNN could only succeed if the neural data captured task-relevant differences. Our analyses of the activation maps across layers revealed that the brains spatiotemporal representation of local image features evolves systematically over time. This underscores how distinct image features emerge at different stages of processing, shaped by the observers goals and behavioral context.

neuroscience↗

bioRxiv2021

Dynamic Electrode-to-Image (DETI) Mapping Reveals the Human Brain's Spatiotemporal Code of Visual Information

A chief goal of systems neuroscience is to understand how the brain encodes information in our visual environments. Understanding that neural code is crucial to explaining how visual content is transformed via subsequent semantic representations to enable intelligent behavior. Although the visual code is not static, this reality is often obscured in voxel-wise encoding models of BOLD signals due to fMRIs poor temporal resolution. We leveraged the high temporal resolution of EEG to develop an encoding technique based in state-space theory. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. This technique offers a spatiotemporal visualization of the evolution of the neural code of visual information thought impossible to obtain from EEG and promises to provide insight into how visual meaning is developed through dynamic feedforward and recurrent processes.

neuroscience↗

bioRxiv2020

Disentangling the Independent Contributions of Visual and Conceptual Features to the Spatiotemporal Dynamics of Scene Categorization

Human scene categorization is characterized by its remarkable speed. While many visual and conceptual features have been linked to this ability, significant correlations exist between feature spaces, impeding our ability to determine their relative contributions to scene categorization. Here, we employed a whitening transformation to decorrelate a variety of visual and conceptual features and assess the time course of their unique contributions to scene categorization. Participants (both sexes) viewed 2,250 full-color scene images drawn from 30 different scene categories while having their brain activity measured through 256-channel EEG. We examined the variance explained at each electrode and time point of visual event-related potential (vERP) data from nine different whitened encoding models. These ranged from low-level features obtained from filter outputs to high-level conceptual features requiring human annotation. The amount of category information in the vERPs was assessed through multivariate decoding methods. Behavioral similarity measures were obtained in separate crowdsourced experiments. We found that all nine models together contributed 78% of the variance of human scene similarity assessments and was within the noise ceiling of the vERP data. Low-level models explained earlier vERP variability (88 ms post-image onset), while high-level models explained later variance (169 ms). Critically, only high-level models shared vERP variability with behavior. Taken together, these results suggest that scene categorization is primarily a high-level process, but reliant on previously extracted low-level features. Significance StatementIn a single fixation, we glean enough information to describe a general scene category. Many types of features are associated with scene categories, ranging from low-level properties such as colors and contours, to high-level properties such as objects and attributes. Because these properties are correlated, it is difficult to understand each propertys unique contributions to scene categorization. This work uses a whitening transformation to remove the correlations between features and examines the extent to which each feature contributes to visual event-related potentials (vERPs) over time. We found that low-level visual features contributed first, but were not correlated with categorization behavior. High-level features followed 80 ms later, providing key insights into how the brain makes sense of a complex visual world.

neuroscience↗

bioRxiv2017

Shared Spatiotemporal Category Representations in Biological and Artificial Deep Neural Networks

Understanding the computational transformations that enable invariant visual categorization is a fundamental challenge in both systems and cognitive neuroscience. Recently developed deep convolutional neural networks (CNNs) perform visual categorization at accuracies that rival humans, providing neuroscientists with the opportunity to interrogate the series of representational transformations that enable categorization in silico. The goal of the current study is to assess the extent to which sequential visual representations built by a CNN map onto those built in the human brain as assessed by high-density, time-resolved event-related potentials (ERPs). We found correspondence both over time and across the scalp: earlier ERP activity was best explained by early CNN layers at all electrodes. Later neural activity was best explained by the later, conceptual layers of the CNN. This effect was especially true both in frontal and right occipital sites. Together, we conclude that deep artificial neural networks trained to perform scene categorization traverse similar representational stages as the human brain. Thus, examining these networks will allow neuroscientists to better understand the transformations that enable invariant visual categorization.

neuroscience↗