Biology subjects

Lahner, B.

Publications and source records attributed to Lahner, B..

6 recordsLinked to original sources

bioRxiv2025

MOSAIC: A scalable framework for fMRI dataset aggregation and modeling of human vision

Recent large-scale vision fMRI datasets have been invaluable resources to the vision neuroscience community for their deep sampling of individual subjects and diverse stimulus sets. However, practical limitations to the number of subjects, stimuli, and trials that can be collected prevent individual fMRI datasets from reaching the scale necessary for modern modeling approaches and robust conclusions. Here, we introduce MOSAIC (Meta-Organized Stimuli And fMRI Imaging data for Computational modeling), a fMRI dataset aggregation framework designed to leverage the richness of individual datasets for computationally intensive modeling and robust tests of generalization. MOSAIC is composed of eight large-scale vision fMRI datasets totaling 93 subjects, 430,007 fMRI-stimulus pairs, and 162,839 naturalistic and artificial stimuli. A shared fMRI preprocessing pipeline and a filtered test-train split minimizes dataset-specific confounds and test-set leakage when aggregating the datasets. Crucially, additional datasets can be integrated into MOSAIC post hoc, allowing MOSAIC to evolve according to the communitys interests. We use MOSAIC to show that perceptually diverse stimulus sets consistently improve decoding accuracy and stability, carrying implications for future fMRI stimulus set design. We then jointly train brain-optimized encoding models across subjects and datasets to predict fMRI activity of all visual cortex and even the whole brain. In silico functional localizer experiments performed on these digital twin models can recover subject-specific category-selective cortical regions, thereby validating our approach. Together, MOSAIC provides a scalable and community-driven solution to build robust, larger-scale models of human vision.

neuroscience↗

bioRxiv2023

BOLD Moments: modeling short visual events through a video fMRI dataset and metadata

Studying the neural basis of human dynamic visual perception requires extensive experimental data to evaluate the large swathes of functionally diverse brain neural networks driven by perceiving visual events. Here, we introduce the BOLD Moments Dataset (BMD), a repository of whole-brain fMRI responses to over 1,000 short (3s) naturalistic video clips of visual events across ten human subjects. We use the videos extensive metadata to show how the brain represents word- and sentence-level descriptions of visual events and identify correlates of video memorability scores extending into the parietal cortex. Furthermore, we reveal a match in hierarchical processing between cortical regions of interest and video-computable deep neural networks, and we showcase that BMD successfully captures temporal dynamics of visual events at second resolution. With its rich metadata, BMD offers new perspectives and accelerates research on the human brain basis of visual event perception.

neuroscience↗

bioRxiv2022

Theta phase specific modulation of hippocampal memory neurons

I.The theta rhythm, a quasi-periodic 4-10 Hz oscillation, is observed during memory processing in the hippocampus, with different phases of theta hypothesized to separate independent streams of information related to the encoding and recall of memories. At the cellular level, the discovery of hippocampal memory cells (engram neurons), as well as the modulation of memory recall through optogenetic activation of these cells, has provided evidence that certain memories are stored, in part, in a sparse ensemble of neurons in the hippocampus. In previous research, however, engram reactivation has been carried out using open loop stimulation at fixed frequencies; the relationship between engram neuron reactivation and ongoing network oscillations has not been taken into consideration. To address this concern, we implemented a closed-loop reactivation of engram neurons that enabled phase-specific stimulation relative to theta oscillations in the local field potential. Using this real-time approach, we tested the impact of activating engram neurons during the peak (encoding phase) and trough (recall phase) of theta oscillations. Consistent with previously hypothesized functions of theta oscillations in memory function, we show that stimulating engram neurons at the trough of theta is more effective in eliciting behavioral recall than either fixed frequency stimulation or stimulation at the peak of theta. Moreover, phase-specific trough stimulation is accompanied by an increase in the coupling between gamma and theta oscillations in CA1 hippocampus. Oure results provide a causal link between phase-specific activation of engram cells and the behavioral expression of memory.

neuroscience↗

bioRxiv2020

Spatiotemporal Dynamics of Sound Representations Reveal a Hierarchical Progression of Category Selectivity

As the human brain transforms incoming sounds, it remains unclear whether semantic meaning is assigned via distributed, domain-general architectures or specialized hierarchical streams. Here we show that the spatiotemporal progression from acoustic to semantically dominated representations is consistent with a hierarchical processing scheme. Combining magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) patterns, we found superior temporal responses beginning ~80 ms post-stimulus onset, spreading to extratemporal cortices by ~130 ms. Early acoustically-dominated representations trended systematically toward semantic category dominance over time (after ~200 ms) and space (beyond primary cortex). Semantic category representation was spatially specific: vocalizations were preferentially distinguished in temporal and frontal voice-selective regions and the fusiform face area; scene and object sounds were distinguished in parahippocampal and medial place areas. Our results are consistent with an extended auditory processing hierarchy in which acoustic representations give rise to multiple streams specialized by category, including areas typically considered visual cortex.

neuroscience↗

bioRxiv2020

Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks

Research at the intersection of computer vision and neuroscience has revealed hierarchical correspondence between layers of deep convolutional neural networks (DCNNs) and cascade of regions along human ventral visual cortex. Recently, studies have uncovered emergence of human interpretable concepts within DCNNs layers trained to identify visual objects and scenes. Here, we asked whether an artificial neural network (with convolutional structure) trained for visual categorization would demonstrate spatial correspondences with human brain regions showing central/peripheral biases. Using representational similarity analysis, we compared activations of convolutional layers of a DCNN trained for object and scene categorization with neural representations in human brain visual regions. Results reveal a brain-like topographical organization in the layers of the DCNN, such that activations of layer-units with central-bias were associated with brain regions with foveal tendencies (e.g. fusiform gyrus), and activations of layer-units with selectivity for image backgrounds were associated with cortical regions showing peripheral preference (e.g. parahippocampal cortex). The emergence of a categorical topographical correspondence between DCNNs and brain regions suggests these models are a good approximation of the perceptual representation generated by biological neural networks.

neuroscience↗

bioRxiv2018

Reliability and Generalizability of Similarity-Based Fusion of MEG and fMRI Data in Human Ventral and Dorsal Visual Streams

To build a representation of what we see, the human brain recruits regions throughout the visual cortex in cascading sequence. Recently, an approach was proposed to evaluate the dynamics of visual perception in high spatiotemporal resolution at the scale of the whole brain. This method combined functional magnetic resonance imaging (fMRI) data with magnetoencephalography (MEG) data using representational similarity analysis and revealed a hierarchical progression from primary visual cortex through the dorsal and ventral streams. To assess the replicability of this method, here we present results of a visual recognition neuro-imaging fusion experiment, and compare them within and across experimental settings. We evaluated the reliability of this method by assessing the consistency of the results under similar test conditions, showing high agreement within participants. We then generalized these results to a separate group of individuals and visual input by comparing them to the fMRI-MEG fusion data of Cichy et al (2016), revealing a highly similar temporal progression recruiting both the dorsal and ventral streams. Together these results are a testament to the reproducibility of the fMRI-MEG fusion approach and allows for the interpretation of these spatiotemporal dynamic in a broader context.

neuroscience↗