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Using artificial neural networks to ask ‘why’questions of minds and brains
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Nancy Kanwisher
,
Meenakshi Khosla
,
Katharina Dobs
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A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition
Prior work has identified cortical regions selectively responsive to specific categories of visual stimuli. However, this …
Meenakshi Khosla
,
N Apurva Ratan Murty
,
Nancy Kanwisher
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Predicting individual task contrasts from resting‐state functional connectivity using a surface‐based convolutional network
Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state …
Gia H Ngo
,
Meenakshi Khosla
,
Keith Jamison
,
Amy Kuceyeski
,
Mert Sabuncu
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NeuroGen: Activation optimized image synthesis for discovery neuroscience
Functional MRI (fMRI) is a powerful technique that has allowed us to characterize visual cortex responses to stimuli, yet such …
Zijin Gu
,
Keith Jamison
,
Meenakshi Khosla
,
Emily J. Allen
,
Yihan Wu
,
Ghislain St-Yves
,
Thomas Naselaris
,
Kendrick Kay
,
Mert Sabuncu
,
Amy Kuceyeski
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Cortical response to naturalistic stimuli is largely predictable with deep neural networks
Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. …
Meenakshi Khosla
,
Gia H Ngo
,
Keith Jamison
,
Amy Kuceyeski
,
Mert Sabuncu
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Machine learning in resting-state fMRI analysis
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) …
Meenakshi Khosla
,
Keith Jamison
,
Gia H Ngo
,
Amy Kuceyeski
,
Mert Sabuncu
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Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction
The specificty and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on preprocessing choices, such as the …
Meenakshi Khosla
,
Keith Jamison
,
Gia H Ngo
,
Amy Kuceyeski
,
Mert Sabuncu
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