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ML Tea: Aggregating fMRI datasets for training brain-optimized models of human vision

March 17 2025

4:00P - 4:45P

Location

32-G882

Speaker: Benjamin Lahner

Title: Aggregating fMRI datasets for training brain-optimized models of human vision

Abstract: Large-scale fMRI datasets are revolutionizing our understanding of the neural processes underlying human perception, driving new breakthroughs in neuroscience and computational modeling. Yet individual fMRI data collection efforts remain constrained by practical limitations in scan time, creating an inherent tradeoff between subjects, stimuli, and stimulus repetitions. This tradeoff often compromises stimuli diversity, data quality, and generalizability of findings such that even the largest fMRI datasets cannot fully leverage the power of high-parameter artificial neural network models and high-dimensional feature spaces. To overcome these challenges, we introduce MOSAIC (Meta-Organized Stimuli And fMRI Imaging data for Computational modeling): a scalable framework for aggregating fMRI responses across multiple subjects and datasets. We preprocessed and registered eight event-related fMRI vision datasets (Natural Scenes Dataset, Natural Object Dataset, BOLD Moments Dataset, BOLD5000, Human Actions Dataset, Deeprecon, Generic Object Decoding, and THINGS) to the fsLR32k cortical surface space with fMRIPrep to obtain 430,007 fMRI-stimulus pairs over 93 subjects and 162,839 unique stimuli. We estimated single-trial beta values with GLMsingle (Prince et al., 2022), obtaining parameter estimates of similar or higher quality than the originally published datasets. Critically, we curated the dataset by eliminating stimuli with perceptual similarity above a defined threshold to prevent test-train leakage. This rigorous pipeline resulted in a well-defined stimulus-response dataset with 144,360 training stimuli, 18,145 test stimuli, and 334 synthetic stimuli well-suited for building and evaluating robust models of human vision. We show preliminary results using MOSAIC to investigate how the internal representations between brain-optimized neural networks differ from task-optimized neural networks and perform a large-scale decoding analysis that highlights the importance of stimulus set diversity. This framework empowers the vision science community to collaboratively generate a scalable, generalizable foundation for studying human vision.

Bio: Ben Lahner is a PhD candidate in computational neuroscience working with Dr. Aude Oliva. His research combines fMRI data with machine learning and deep learning techniques to better understand facets of the human visual system. His previous work has investigated visual memory, action understanding, and video decoding from brain activity patterns.

 

Add to Calendar 2025-03-17 16:00:00 2025-03-17 16:45:00 America/New_York ML Tea: Aggregating fMRI datasets for training brain-optimized models of human vision Speaker: Benjamin LahnerTitle: Aggregating fMRI datasets for training brain-optimized models of human visionAbstract: Large-scale fMRI datasets are revolutionizing our understanding of the neural processes underlying human perception, driving new breakthroughs in neuroscience and computational modeling. Yet individual fMRI data collection efforts remain constrained by practical limitations in scan time, creating an inherent tradeoff between subjects, stimuli, and stimulus repetitions. This tradeoff often compromises stimuli diversity, data quality, and generalizability of findings such that even the largest fMRI datasets cannot fully leverage the power of high-parameter artificial neural network models and high-dimensional feature spaces. To overcome these challenges, we introduce MOSAIC (Meta-Organized Stimuli And fMRI Imaging data for Computational modeling): a scalable framework for aggregating fMRI responses across multiple subjects and datasets. We preprocessed and registered eight event-related fMRI vision datasets (Natural Scenes Dataset, Natural Object Dataset, BOLD Moments Dataset, BOLD5000, Human Actions Dataset, Deeprecon, Generic Object Decoding, and THINGS) to the fsLR32k cortical surface space with fMRIPrep to obtain 430,007 fMRI-stimulus pairs over 93 subjects and 162,839 unique stimuli. We estimated single-trial beta values with GLMsingle (Prince et al., 2022), obtaining parameter estimates of similar or higher quality than the originally published datasets. Critically, we curated the dataset by eliminating stimuli with perceptual similarity above a defined threshold to prevent test-train leakage. This rigorous pipeline resulted in a well-defined stimulus-response dataset with 144,360 training stimuli, 18,145 test stimuli, and 334 synthetic stimuli well-suited for building and evaluating robust models of human vision. We show preliminary results using MOSAIC to investigate how the internal representations between brain-optimized neural networks differ from task-optimized neural networks and perform a large-scale decoding analysis that highlights the importance of stimulus set diversity. This framework empowers the vision science community to collaboratively generate a scalable, generalizable foundation for studying human vision.Bio: Ben Lahner is a PhD candidate in computational neuroscience working with Dr. Aude Oliva. His research combines fMRI data with machine learning and deep learning techniques to better understand facets of the human visual system. His previous work has investigated visual memory, action understanding, and video decoding from brain activity patterns.  TBD

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