18 citations · 47 across the 11 of their papers we have counts for
12 papers
Modulating human brain responses via optimal natural image selection and synthetic image generation
Zijin Gu, Keith Jamison, Mert R. Sabuncu +1
Understanding how human brains interpret and process information is important. Here, we investigated the selectivity and inter-individual differences in human brain responses to im…
Decoding natural image stimuli from fMRI data with a surface-based convolutional network
Zijin Gu, Keith Jamison, Amy Kuceyeski +1
Due to the low signal-to-noise ratio and limited resolution of functional MRI data, and the high complexity of natural images, reconstructing a visual stimulus from human brain fMR…
Personalized visual encoding model construction with small data
Zijin Gu, Keith Jamison, Mert Sabuncu +1
Encoding models that predict brain response patterns to stimuli are one way to capture this relationship between variability in bottom-up neural systems and individual's behavior o…
NeuroGen: activation optimized image synthesis for discovery neuroscience
Zijin Gu, Keith W. Jamison, Meenakshi Khosla +6
Functional MRI (fMRI) is a powerful technique that has allowed us to characterize visual cortex responses to stimuli, yet such experiments are by nature constructed based on a prio…
Neural encoding with visual attention
Meenakshi Khosla, Gia H. Ngo, Keith Jamison +2
Visual perception is critically influenced by the focus of attention. Due to limited resources, it is well known that neural representations are biased in favor of attended locatio…
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity
Gia H. Ngo, Meenakshi Khosla, Keith Jamison +2
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals. Connectomic fingerprints have proven useful in many mac…
A shared neural encoding model for the prediction of subject-specific fMRI response
Meenakshi Khosla, Gia H. Ngo, Keith Jamison +2
The increasing popularity of naturalistic paradigms in fMRI (such as movie watching) demands novel strategies for multi-subject data analysis, such as use of neural encoding models…
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach
Meenakshi Khosla, Keith Jamison, Amy Kuceyeski +1
Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human…
Machine learning in resting-state fMRI analysis
Meenakshi Khosla, Keith Jamison, Gia H. Ngo +2
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various u…
Large Spectral Density Matrix Estimation by Thresholding
Yiming Sun, Yige Li, Amy Kuceyeski +1
Spectral density matrix estimation of multivariate time series is a classical problem in time series and signal processing. In modern neuroscience, spectral density based metrics a…
Ensemble learning with 3D convolutional neural networks for connectome-based prediction
Meenakshi Khosla, Keith Jamison, Amy Kuceyeski +1
The specificty and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on pre-processing choices, such as the parcellation scheme used to define regions of in…
3D Convolutional Neural Networks for Classification of Functional Connectomes
Meenakshi Khosla, Keith Jamison, Amy Kuceyeski +1
Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a diagnostic or prognostic tool for a wide variety of conditions, such as autism, Alzheimer's disease, a…