Biology subjects

Greene, M. R.

Publications and source records attributed to Greene, M. R..

8 recordsLinked to original sources

bioRxiv2026

An atfs-1 loss-of-function screen identifies novel regulators of a-synuclein toxicity in C. elegans dopaminergic neurons

Overexpression of -synuclein (-syn), an inherently disordered protein, triggers chronic activation of the mitochondrial unfolded protein response (UPRmt) pathway in Caenorhabditis elegans with enhanced dopaminergic (DAergic) neurodegeneration. Introduction of a loss-of-function(lf) mutation in atfs-1, the main transcriptional regulator of the UPRmt, into -syn nematodes results in significant neuroprotection from -syn-induced DA neuron loss. Using this sensitized neuroprotective background, we performed a F3 forward genetic screen in C. elegans atfs-1(lf) mutants to identify molecular components associated with the modulation of neurodegeneration in -syn-expressing DA neurons. Homozygous mutant animals were examined for enhanced neurodegeneration; multiple independent alleles were uncovered. Among these, we identified new nonsense alleles encoding the histone lysine demethylases (H3K27me3), jmjd-1.2 (orthologous to human KDM7A, PHF2, and PHF8) and jmjd-3.1 (homologous to yeast CYC8). Another line carried a nonsense allele of twk-14. This gene encodes a conserved protein termed KCNK12 in mammals that facilitates passive background K+ leak currents to set and stabilize resting membrane potential. To further examine the association of these gene products with DA neurodegeneration, we used neuron-targeted RNA interference, mutants, or both. DA neurodegeneration was observed in the -syn + atfs-1(lf) background when jmjd-1.2, jmjd-3.1, or twk-14 were individually depleted. These results provide evidence that jmjd-1.2 and jmjd-3.1, which encode previously characterized H3K27me3 demethylases, and the uncharacterized twk-14 gene product, orthologous to human KCNK12, naturally confer protection from -syn-induced neurotoxicity.

genetics↗

bioRxiv2026

A Forward Genetic Screen in Caenorhabditis elegans for Genes that Modulate α-synuclein-Induced Neurodegeneration through the Mitochondrial UPR

Overexpression of -synuclein (-syn), an inherently disordered protein, triggers chronic activation of the mitochondrial unfolded protein response (UPRmt) pathway in Caenorhabditis elegans with enhanced dopaminergic (DAergic) neurodegeneration. Introduction of a loss-of-function(lf) mutation in atfs-1, the main transcriptional regulator of the UPRmt, into -syn nematodes results in significant neuroprotection from -syn-induced DA neuron loss, indicating that compensatory mechanisms provide neuroprotection. We performed a F3 forward genetic screen in C. elegans atfs-1(lf) mutants to identify molecular components associated with the modulation of neurodegeneration via UPRmt signaling in -syn-expressing DA neurons. Homozygous mutant animals were examined for enhanced neurodegeneration; multiple independent alleles were uncovered. Among these, we identified new nonsense alleles encoding the histone lysine demethylases (H3K27me3), jmjd-1.2 and jmjd-3.1. Another line carried a nonsense allele of twk-14. This gene encodes a conserved protein termed KCNK12 in mammals that facilitates passive background K+ leak currents to set and stabilize resting membrane potential. To further examine the association of these gene products in DA neurodegeneration, mutants and/or RNA interference were employed. DA neurodegeneration was observed in the -syn + atfs-1(lf) background when jmjd-1.2, jmjd-3.1, or twk-14 were individually depleted. These results provide evidence that jmjd-1.2 and jmjd-3.1, which encode previously characterized H3K27me3 demethylases, and the uncharacterized twk-14 gene product, orthologous to human KCNK12, naturally confer protection from -syn neurotoxicity.

genetics↗

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↗

bioRxiv2019

Towards a state-space geometry of neural responses to natural scenes: A steady-state approach

Our understanding of information processing by the mammalian visual system has come through a variety of techniques ranging from psychophysics and fMRI to single unit recording and EEG. Each technique provides unique insights into the processing framework of the early visual system. Here, we focus on the nature of the information that is carried by steady state visual evoked potentials (SSVEPs). To study the information provided by SSVEPs, we presented human participants with a population of natural scenes and measured the relative SSVEP response. Rather than focus on particular features of this signal, we focused on the full state-space of possible responses and investigated how the evoked responses are mapped onto this space. Our results show that it is possible to map the relatively high-dimensional signal carried by SSVEPs onto a 2-dimensional space with little loss. We also show that a simple biologically plausible model can account for a high proportion of the explainable variance ([~]73%) in that space. Finally, we describe a technique for measuring the mutual information that is available about images from SSVEPs. The techniques introduced here represent a new approach to understanding the nature of the information carried by SSVEPs. Crucially, this approach is general and can provide a means of comparing results across different neural recording methods. Altogether, our study sheds light on the encoding principles of early vision and provides a much needed reference point for understanding subsequent transformations of the early visual response space to deeper knowledge structures that link different visual environments.

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↗

bioRxiv2017

Distinct contributions of functional and deep neural network features to scene representation in brain and behavior

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

neuroscience↗